libjxl

butteraugli.cc (78837B)

---

 // Copyright (c) the JPEG XL Project Authors. All rights reserved.
 //
 // Use of this source code is governed by a BSD-style
 // license that can be found in the LICENSE file.
 //
 // Author: Jyrki Alakuijala (jyrki.alakuijala@gmail.com)
 //
 // The physical architecture of butteraugli is based on the following naming
 // convention:
 //   * Opsin - dynamics of the photosensitive chemicals in the retina
 //             with their immediate electrical processing
 //   * Xyb - hybrid opponent/trichromatic color space
 //     x is roughly red-subtract-green.
 //     y is yellow.
 //     b is blue.
 //     Xyb values are computed from Opsin mixing, not directly from rgb.
 //   * Mask - for visual masking
 //   * Hf - color modeling for spatially high-frequency features
 //   * Lf - color modeling for spatially low-frequency features
 //   * Diffmap - to cluster and build an image of error between the images
 //   * Blur - to hold the smoothing code
 
 #include "lib/jxl/butteraugli/butteraugli.h"
 
 #include <stdint.h>
 #include <stdio.h>
 #include <stdlib.h>
 #include <string.h>
 
 #include <algorithm>
 #include <cmath>
 #include <memory>
 #include <vector>
 
 #include "lib/jxl/image.h"
 
 #undef HWY_TARGET_INCLUDE
 #define HWY_TARGET_INCLUDE "lib/jxl/butteraugli/butteraugli.cc"
 #include <hwy/foreach_target.h>
 
 #include "lib/jxl/base/fast_math-inl.h"
 #include "lib/jxl/base/printf_macros.h"
 #include "lib/jxl/base/status.h"
 #include "lib/jxl/convolve.h"
 #include "lib/jxl/image_ops.h"
 
 #ifndef JXL_BUTTERAUGLI_ONCE
 #define JXL_BUTTERAUGLI_ONCE
 
 namespace jxl {
 
 static const double wMfMalta = 37.0819870399;
 static const double norm1Mf = 130262059.556;
 static const double wMfMaltaX = 8246.75321353;
 static const double norm1MfX = 1009002.70582;
 static const double wHfMalta = 18.7237414387;
 static const double norm1Hf = 4498534.45232;
 static const double wHfMaltaX = 6923.99476109;
 static const double norm1HfX = 8051.15833247;
 static const double wUhfMalta = 1.10039032555;
 static const double norm1Uhf = 71.7800275169;
 static const double wUhfMaltaX = 173.5;
 static const double norm1UhfX = 5.0;
 static const double wmul[9] = {
     400.0,         1.50815703118,  0,
     2150.0,        10.6195433239,  16.2176043152,
     29.2353797994, 0.844626970982, 0.703646627719,
 };
 
 std::vector<float> ComputeKernel(float sigma) {
   const float m = 2.25;  // Accuracy increases when m is increased.
   const double scaler = -1.0 / (2.0 * sigma * sigma);
   const int diff = std::max<int>(1, m * std::fabs(sigma));
   std::vector<float> kernel(2 * diff + 1);
   for (int i = -diff; i <= diff; ++i) {
     kernel[i + diff] = std::exp(scaler * i * i);
   }
   return kernel;
 }
 
 void ConvolveBorderColumn(const ImageF& in, const std::vector<float>& kernel,
                           const size_t x, float* BUTTERAUGLI_RESTRICT row_out) {
   const size_t offset = kernel.size() / 2;
   int minx = x < offset ? 0 : x - offset;
   int maxx = std::min<int>(in.xsize() - 1, x + offset);
   float weight = 0.0f;
   for (int j = minx; j <= maxx; ++j) {
     weight += kernel[j - x + offset];
   }
   float scale = 1.0f / weight;
   for (size_t y = 0; y < in.ysize(); ++y) {
     const float* BUTTERAUGLI_RESTRICT row_in = in.Row(y);
     float sum = 0.0f;
     for (int j = minx; j <= maxx; ++j) {
       sum += row_in[j] * kernel[j - x + offset];
     }
     row_out[y] = sum * scale;
   }
 }
 
 // Computes a horizontal convolution and transposes the result.
 void ConvolutionWithTranspose(const ImageF& in,
                               const std::vector<float>& kernel,
                               ImageF* BUTTERAUGLI_RESTRICT out) {
   JXL_CHECK(out->xsize() == in.ysize());
   JXL_CHECK(out->ysize() == in.xsize());
   const size_t len = kernel.size();
   const size_t offset = len / 2;
   float weight_no_border = 0.0f;
   for (size_t j = 0; j < len; ++j) {
     weight_no_border += kernel[j];
   }
   const float scale_no_border = 1.0f / weight_no_border;
   const size_t border1 = std::min(in.xsize(), offset);
   const size_t border2 = in.xsize() > offset ? in.xsize() - offset : 0;
   std::vector<float> scaled_kernel(len / 2 + 1);
   for (size_t i = 0; i <= len / 2; ++i) {
     scaled_kernel[i] = kernel[i] * scale_no_border;
   }
 
   // middle
   switch (len) {
     case 7: {
       const float sk0 = scaled_kernel[0];
       const float sk1 = scaled_kernel[1];
       const float sk2 = scaled_kernel[2];
       const float sk3 = scaled_kernel[3];
       for (size_t y = 0; y < in.ysize(); ++y) {
         const float* BUTTERAUGLI_RESTRICT row_in = in.Row(y) + border1 - offset;
         for (size_t x = border1; x < border2; ++x, ++row_in) {
           const float sum0 = (row_in[0] + row_in[6]) * sk0;
           const float sum1 = (row_in[1] + row_in[5]) * sk1;
           const float sum2 = (row_in[2] + row_in[4]) * sk2;
           const float sum = (row_in[3]) * sk3 + sum0 + sum1 + sum2;
           float* BUTTERAUGLI_RESTRICT row_out = out->Row(x);
           row_out[y] = sum;
         }
       }
     } break;
     case 13: {
       for (size_t y = 0; y < in.ysize(); ++y) {
         const float* BUTTERAUGLI_RESTRICT row_in = in.Row(y) + border1 - offset;
         for (size_t x = border1; x < border2; ++x, ++row_in) {
           float sum0 = (row_in[0] + row_in[12]) * scaled_kernel[0];
           float sum1 = (row_in[1] + row_in[11]) * scaled_kernel[1];
           float sum2 = (row_in[2] + row_in[10]) * scaled_kernel[2];
           float sum3 = (row_in[3] + row_in[9]) * scaled_kernel[3];
           sum0 += (row_in[4] + row_in[8]) * scaled_kernel[4];
           sum1 += (row_in[5] + row_in[7]) * scaled_kernel[5];
           const float sum = (row_in[6]) * scaled_kernel[6];
           float* BUTTERAUGLI_RESTRICT row_out = out->Row(x);
           row_out[y] = sum + sum0 + sum1 + sum2 + sum3;
         }
       }
       break;
     }
     case 15: {
       for (size_t y = 0; y < in.ysize(); ++y) {
         const float* BUTTERAUGLI_RESTRICT row_in = in.Row(y) + border1 - offset;
         for (size_t x = border1; x < border2; ++x, ++row_in) {
           float sum0 = (row_in[0] + row_in[14]) * scaled_kernel[0];
           float sum1 = (row_in[1] + row_in[13]) * scaled_kernel[1];
           float sum2 = (row_in[2] + row_in[12]) * scaled_kernel[2];
           float sum3 = (row_in[3] + row_in[11]) * scaled_kernel[3];
           sum0 += (row_in[4] + row_in[10]) * scaled_kernel[4];
           sum1 += (row_in[5] + row_in[9]) * scaled_kernel[5];
           sum2 += (row_in[6] + row_in[8]) * scaled_kernel[6];
           const float sum = (row_in[7]) * scaled_kernel[7];
           float* BUTTERAUGLI_RESTRICT row_out = out->Row(x);
           row_out[y] = sum + sum0 + sum1 + sum2 + sum3;
         }
       }
       break;
     }
     case 33: {
       for (size_t y = 0; y < in.ysize(); ++y) {
         const float* BUTTERAUGLI_RESTRICT row_in = in.Row(y) + border1 - offset;
         for (size_t x = border1; x < border2; ++x, ++row_in) {
           float sum0 = (row_in[0] + row_in[32]) * scaled_kernel[0];
           float sum1 = (row_in[1] + row_in[31]) * scaled_kernel[1];
           float sum2 = (row_in[2] + row_in[30]) * scaled_kernel[2];
           float sum3 = (row_in[3] + row_in[29]) * scaled_kernel[3];
           sum0 += (row_in[4] + row_in[28]) * scaled_kernel[4];
           sum1 += (row_in[5] + row_in[27]) * scaled_kernel[5];
           sum2 += (row_in[6] + row_in[26]) * scaled_kernel[6];
           sum3 += (row_in[7] + row_in[25]) * scaled_kernel[7];
           sum0 += (row_in[8] + row_in[24]) * scaled_kernel[8];
           sum1 += (row_in[9] + row_in[23]) * scaled_kernel[9];
           sum2 += (row_in[10] + row_in[22]) * scaled_kernel[10];
           sum3 += (row_in[11] + row_in[21]) * scaled_kernel[11];
           sum0 += (row_in[12] + row_in[20]) * scaled_kernel[12];
           sum1 += (row_in[13] + row_in[19]) * scaled_kernel[13];
           sum2 += (row_in[14] + row_in[18]) * scaled_kernel[14];
           sum3 += (row_in[15] + row_in[17]) * scaled_kernel[15];
           const float sum = (row_in[16]) * scaled_kernel[16];
           float* BUTTERAUGLI_RESTRICT row_out = out->Row(x);
           row_out[y] = sum + sum0 + sum1 + sum2 + sum3;
         }
       }
       break;
     }
     default:
       JXL_UNREACHABLE("Kernel size %" PRIuS " not implemented", len);
   }
   // left border
   for (size_t x = 0; x < border1; ++x) {
     ConvolveBorderColumn(in, kernel, x, out->Row(x));
   }
 
   // right border
   for (size_t x = border2; x < in.xsize(); ++x) {
     ConvolveBorderColumn(in, kernel, x, out->Row(x));
   }
 }
 
 // A blur somewhat similar to a 2D Gaussian blur.
 // See: https://en.wikipedia.org/wiki/Gaussian_blur
 //
 // This is a bottleneck because the sigma can be quite large (>7). We can use
 // gauss_blur.cc (runtime independent of sigma, closer to a 4*sigma truncated
 // Gaussian and our 2.25 in ComputeKernel), but its boundary conditions are
 // zero-valued. This leads to noticeable differences at the edges of diffmaps.
 // We retain a special case for 5x5 kernels (even faster than gauss_blur),
 // optionally use gauss_blur followed by fixup of the borders for large images,
 // or fall back to the previous truncated FIR followed by a transpose.
 Status Blur(const ImageF& in, float sigma, const ButteraugliParams& params,
             BlurTemp* temp, ImageF* out) {
   std::vector<float> kernel = ComputeKernel(sigma);
   // Separable5 does an in-place convolution, so this fast path is not safe if
   // in aliases out.
   if (kernel.size() == 5 && &in != out) {
     float sum_weights = 0.0f;
     for (const float w : kernel) {
       sum_weights += w;
     }
     const float scale = 1.0f / sum_weights;
     const float w0 = kernel[2] * scale;
     const float w1 = kernel[1] * scale;
     const float w2 = kernel[0] * scale;
     const WeightsSeparable5 weights = {
         {HWY_REP4(w0), HWY_REP4(w1), HWY_REP4(w2)},
         {HWY_REP4(w0), HWY_REP4(w1), HWY_REP4(w2)},
     };
     Separable5(in, Rect(in), weights, /*pool=*/nullptr, out);
     return true;
   }
 
   ImageF* temp_t;
   JXL_RETURN_IF_ERROR(temp->GetTransposed(in, &temp_t));
   ConvolutionWithTranspose(in, kernel, temp_t);
   ConvolutionWithTranspose(*temp_t, kernel, out);
   return true;
 }
 
 // Allows PaddedMaltaUnit to call either function via overloading.
 struct MaltaTagLF {};
 struct MaltaTag {};
 
 }  // namespace jxl
 
 #endif  // JXL_BUTTERAUGLI_ONCE
 
 #include <hwy/highway.h>
 HWY_BEFORE_NAMESPACE();
 namespace jxl {
 namespace HWY_NAMESPACE {
 
 // These templates are not found via ADL.
 using hwy::HWY_NAMESPACE::Abs;
 using hwy::HWY_NAMESPACE::Div;
 using hwy::HWY_NAMESPACE::Gt;
 using hwy::HWY_NAMESPACE::IfThenElse;
 using hwy::HWY_NAMESPACE::IfThenElseZero;
 using hwy::HWY_NAMESPACE::Lt;
 using hwy::HWY_NAMESPACE::Max;
 using hwy::HWY_NAMESPACE::Mul;
 using hwy::HWY_NAMESPACE::MulAdd;
 using hwy::HWY_NAMESPACE::MulSub;
 using hwy::HWY_NAMESPACE::Neg;
 using hwy::HWY_NAMESPACE::Sub;
 using hwy::HWY_NAMESPACE::Vec;
 using hwy::HWY_NAMESPACE::ZeroIfNegative;
 
 template <class D, class V>
 HWY_INLINE V MaximumClamp(D d, V v, double kMaxVal) {
   static const double kMul = 0.724216145665;
   const V mul = Set(d, kMul);
   const V maxval = Set(d, kMaxVal);
   // If greater than maxval or less than -maxval, replace with if_*.
   const V if_pos = MulAdd(Sub(v, maxval), mul, maxval);
   const V if_neg = MulSub(Add(v, maxval), mul, maxval);
   const V pos_or_v = IfThenElse(Ge(v, maxval), if_pos, v);
   return IfThenElse(Lt(v, Neg(maxval)), if_neg, pos_or_v);
 }
 
 // Make area around zero less important (remove it).
 template <class D, class V>
 HWY_INLINE V RemoveRangeAroundZero(const D d, const double kw, const V x) {
   const auto w = Set(d, kw);
   return IfThenElse(Gt(x, w), Sub(x, w),
                     IfThenElseZero(Lt(x, Neg(w)), Add(x, w)));
 }
 
 // Make area around zero more important (2x it until the limit).
 template <class D, class V>
 HWY_INLINE V AmplifyRangeAroundZero(const D d, const double kw, const V x) {
   const auto w = Set(d, kw);
   return IfThenElse(Gt(x, w), Add(x, w),
                     IfThenElse(Lt(x, Neg(w)), Sub(x, w), Add(x, x)));
 }
 
 // XybLowFreqToVals converts from low-frequency XYB space to the 'vals' space.
 // Vals space can be converted to L2-norm space (Euclidean and normalized)
 // through visual masking.
 template <class D, class V>
 HWY_INLINE void XybLowFreqToVals(const D d, const V& x, const V& y,
                                  const V& b_arg, V* HWY_RESTRICT valx,
                                  V* HWY_RESTRICT valy, V* HWY_RESTRICT valb) {
   static const double xmul_scalar = 33.832837186260;
   static const double ymul_scalar = 14.458268100570;
   static const double bmul_scalar = 49.87984651440;
   static const double y_to_b_mul_scalar = -0.362267051518;
   const V xmul = Set(d, xmul_scalar);
   const V ymul = Set(d, ymul_scalar);
   const V bmul = Set(d, bmul_scalar);
   const V y_to_b_mul = Set(d, y_to_b_mul_scalar);
   const V b = MulAdd(y_to_b_mul, y, b_arg);
   *valb = Mul(b, bmul);
   *valx = Mul(x, xmul);
   *valy = Mul(y, ymul);
 }
 
 void XybLowFreqToVals(Image3F* xyb_lf) {
   // Modify range around zero code only concerns the high frequency
   // planes and only the X and Y channels.
   // Convert low freq xyb to vals space so that we can do a simple squared sum
   // diff on the low frequencies later.
   const HWY_FULL(float) d;
   for (size_t y = 0; y < xyb_lf->ysize(); ++y) {
     float* BUTTERAUGLI_RESTRICT row_x = xyb_lf->PlaneRow(0, y);
     float* BUTTERAUGLI_RESTRICT row_y = xyb_lf->PlaneRow(1, y);
     float* BUTTERAUGLI_RESTRICT row_b = xyb_lf->PlaneRow(2, y);
     for (size_t x = 0; x < xyb_lf->xsize(); x += Lanes(d)) {
       auto valx = Undefined(d);
       auto valy = Undefined(d);
       auto valb = Undefined(d);
       XybLowFreqToVals(d, Load(d, row_x + x), Load(d, row_y + x),
                        Load(d, row_b + x), &valx, &valy, &valb);
       Store(valx, d, row_x + x);
       Store(valy, d, row_y + x);
       Store(valb, d, row_b + x);
     }
   }
 }
 
 void SuppressXByY(const ImageF& in_y, ImageF* HWY_RESTRICT inout_x) {
   JXL_DASSERT(SameSize(*inout_x, in_y));
   const size_t xsize = in_y.xsize();
   const size_t ysize = in_y.ysize();
   const HWY_FULL(float) d;
   static const double suppress = 46.0;
   static const double s = 0.653020556257;
   const auto sv = Set(d, s);
   const auto one_minus_s = Set(d, 1.0 - s);
   const auto ywv = Set(d, suppress);
 
   for (size_t y = 0; y < ysize; ++y) {
     const float* HWY_RESTRICT row_y = in_y.ConstRow(y);
     float* HWY_RESTRICT row_x = inout_x->Row(y);
     for (size_t x = 0; x < xsize; x += Lanes(d)) {
       const auto vx = Load(d, row_x + x);
       const auto vy = Load(d, row_y + x);
       const auto scaler =
           MulAdd(Div(ywv, MulAdd(vy, vy, ywv)), one_minus_s, sv);
       Store(Mul(scaler, vx), d, row_x + x);
     }
   }
 }
 
 void Subtract(const ImageF& a, const ImageF& b, ImageF* c) {
   const HWY_FULL(float) d;
   for (size_t y = 0; y < a.ysize(); ++y) {
     const float* row_a = a.ConstRow(y);
     const float* row_b = b.ConstRow(y);
     float* row_c = c->Row(y);
     for (size_t x = 0; x < a.xsize(); x += Lanes(d)) {
       Store(Sub(Load(d, row_a + x), Load(d, row_b + x)), d, row_c + x);
     }
   }
 }
 
 Status SeparateLFAndMF(const ButteraugliParams& params, const Image3F& xyb,
                        Image3F* lf, Image3F* mf, BlurTemp* blur_temp) {
   static const double kSigmaLf = 7.15593339443;
   for (int i = 0; i < 3; ++i) {
     // Extract lf ...
     JXL_RETURN_IF_ERROR(
         Blur(xyb.Plane(i), kSigmaLf, params, blur_temp, &lf->Plane(i)));
     // ... and keep everything else in mf.
     Subtract(xyb.Plane(i), lf->Plane(i), &mf->Plane(i));
   }
   XybLowFreqToVals(lf);
   return true;
 }
 
 Status SeparateMFAndHF(const ButteraugliParams& params, Image3F* mf, ImageF* hf,
                        BlurTemp* blur_temp) {
   const HWY_FULL(float) d;
   static const double kSigmaHf = 3.22489901262;
   const size_t xsize = mf->xsize();
   const size_t ysize = mf->ysize();
   JXL_ASSIGN_OR_RETURN(hf[0], ImageF::Create(xsize, ysize));
   JXL_ASSIGN_OR_RETURN(hf[1], ImageF::Create(xsize, ysize));
   for (int i = 0; i < 3; ++i) {
     if (i == 2) {
       JXL_RETURN_IF_ERROR(
           Blur(mf->Plane(i), kSigmaHf, params, blur_temp, &mf->Plane(i)));
       break;
     }
     for (size_t y = 0; y < ysize; ++y) {
       float* BUTTERAUGLI_RESTRICT row_mf = mf->PlaneRow(i, y);
       float* BUTTERAUGLI_RESTRICT row_hf = hf[i].Row(y);
       for (size_t x = 0; x < xsize; x += Lanes(d)) {
         Store(Load(d, row_mf + x), d, row_hf + x);
       }
     }
     JXL_RETURN_IF_ERROR(
         Blur(mf->Plane(i), kSigmaHf, params, blur_temp, &mf->Plane(i)));
     static const double kRemoveMfRange = 0.29;
     static const double kAddMfRange = 0.1;
     if (i == 0) {
       for (size_t y = 0; y < ysize; ++y) {
         float* BUTTERAUGLI_RESTRICT row_mf = mf->PlaneRow(0, y);
         float* BUTTERAUGLI_RESTRICT row_hf = hf[0].Row(y);
         for (size_t x = 0; x < xsize; x += Lanes(d)) {
           auto mf = Load(d, row_mf + x);
           auto hf = Sub(Load(d, row_hf + x), mf);
           mf = RemoveRangeAroundZero(d, kRemoveMfRange, mf);
           Store(mf, d, row_mf + x);
           Store(hf, d, row_hf + x);
         }
       }
     } else {
       for (size_t y = 0; y < ysize; ++y) {
         float* BUTTERAUGLI_RESTRICT row_mf = mf->PlaneRow(1, y);
         float* BUTTERAUGLI_RESTRICT row_hf = hf[1].Row(y);
         for (size_t x = 0; x < xsize; x += Lanes(d)) {
           auto mf = Load(d, row_mf + x);
           auto hf = Sub(Load(d, row_hf + x), mf);
 
           mf = AmplifyRangeAroundZero(d, kAddMfRange, mf);
           Store(mf, d, row_mf + x);
           Store(hf, d, row_hf + x);
         }
       }
     }
   }
   // Suppress red-green by intensity change in the high freq channels.
   SuppressXByY(hf[1], &hf[0]);
   return true;
 }
 
 Status SeparateHFAndUHF(const ButteraugliParams& params, ImageF* hf,
                         ImageF* uhf, BlurTemp* blur_temp) {
   const HWY_FULL(float) d;
   const size_t xsize = hf[0].xsize();
   const size_t ysize = hf[0].ysize();
   static const double kSigmaUhf = 1.56416327805;
   JXL_ASSIGN_OR_RETURN(uhf[0], ImageF::Create(xsize, ysize));
   JXL_ASSIGN_OR_RETURN(uhf[1], ImageF::Create(xsize, ysize));
   for (int i = 0; i < 2; ++i) {
     // Divide hf into hf and uhf.
     for (size_t y = 0; y < ysize; ++y) {
       float* BUTTERAUGLI_RESTRICT row_uhf = uhf[i].Row(y);
       float* BUTTERAUGLI_RESTRICT row_hf = hf[i].Row(y);
       for (size_t x = 0; x < xsize; ++x) {
         row_uhf[x] = row_hf[x];
       }
     }
     JXL_RETURN_IF_ERROR(Blur(hf[i], kSigmaUhf, params, blur_temp, &hf[i]));
     static const double kRemoveHfRange = 1.5;
     static const double kAddHfRange = 0.132;
     static const double kRemoveUhfRange = 0.04;
     static const double kMaxclampHf = 28.4691806922;
     static const double kMaxclampUhf = 5.19175294647;
     static double kMulYHf = 2.155;
     static double kMulYUhf = 2.69313763794;
     if (i == 0) {
       for (size_t y = 0; y < ysize; ++y) {
         float* BUTTERAUGLI_RESTRICT row_uhf = uhf[0].Row(y);
         float* BUTTERAUGLI_RESTRICT row_hf = hf[0].Row(y);
         for (size_t x = 0; x < xsize; x += Lanes(d)) {
           auto hf = Load(d, row_hf + x);
           auto uhf = Sub(Load(d, row_uhf + x), hf);
           hf = RemoveRangeAroundZero(d, kRemoveHfRange, hf);
           uhf = RemoveRangeAroundZero(d, kRemoveUhfRange, uhf);
           Store(hf, d, row_hf + x);
           Store(uhf, d, row_uhf + x);
         }
       }
     } else {
       for (size_t y = 0; y < ysize; ++y) {
         float* BUTTERAUGLI_RESTRICT row_uhf = uhf[1].Row(y);
         float* BUTTERAUGLI_RESTRICT row_hf = hf[1].Row(y);
         for (size_t x = 0; x < xsize; x += Lanes(d)) {
           auto hf = Load(d, row_hf + x);
           hf = MaximumClamp(d, hf, kMaxclampHf);
 
           auto uhf = Sub(Load(d, row_uhf + x), hf);
           uhf = MaximumClamp(d, uhf, kMaxclampUhf);
           uhf = Mul(uhf, Set(d, kMulYUhf));
           Store(uhf, d, row_uhf + x);
 
           hf = Mul(hf, Set(d, kMulYHf));
           hf = AmplifyRangeAroundZero(d, kAddHfRange, hf);
           Store(hf, d, row_hf + x);
         }
       }
     }
   }
   return true;
 }
 
 void DeallocateHFAndUHF(ImageF* hf, ImageF* uhf) {
   for (int i = 0; i < 2; ++i) {
     hf[i] = ImageF();
     uhf[i] = ImageF();
   }
 }
 
 Status SeparateFrequencies(size_t xsize, size_t ysize,
                            const ButteraugliParams& params, BlurTemp* blur_temp,
                            const Image3F& xyb, PsychoImage& ps) {
   JXL_ASSIGN_OR_RETURN(ps.lf, Image3F::Create(xyb.xsize(), xyb.ysize()));
   JXL_ASSIGN_OR_RETURN(ps.mf, Image3F::Create(xyb.xsize(), xyb.ysize()));
   JXL_RETURN_IF_ERROR(SeparateLFAndMF(params, xyb, &ps.lf, &ps.mf, blur_temp));
   JXL_RETURN_IF_ERROR(SeparateMFAndHF(params, &ps.mf, &ps.hf[0], blur_temp));
   JXL_RETURN_IF_ERROR(
       SeparateHFAndUHF(params, &ps.hf[0], &ps.uhf[0], blur_temp));
   return true;
 }
 
 namespace {
 template <typename V>
 BUTTERAUGLI_INLINE V Sum(V a, V b, V c, V d) {
   return Add(Add(a, b), Add(c, d));
 }
 template <typename V>
 BUTTERAUGLI_INLINE V Sum(V a, V b, V c, V d, V e) {
   return Sum(a, b, c, Add(d, e));
 }
 template <typename V>
 BUTTERAUGLI_INLINE V Sum(V a, V b, V c, V d, V e, V f, V g) {
   return Sum(a, b, c, Sum(d, e, f, g));
 }
 template <typename V>
 BUTTERAUGLI_INLINE V Sum(V a, V b, V c, V d, V e, V f, V g, V h, V i) {
   return Add(Add(Sum(a, b, c, d), Sum(e, f, g, h)), i);
 }
 }  // namespace
 
 template <class D>
 Vec<D> MaltaUnit(MaltaTagLF /*tag*/, const D df,
                  const float* BUTTERAUGLI_RESTRICT d, const intptr_t xs) {
   const intptr_t xs3 = 3 * xs;
 
   const auto center = LoadU(df, d);
 
   // x grows, y constant
   const auto sum_yconst = Sum(LoadU(df, d - 4), LoadU(df, d - 2), center,
                               LoadU(df, d + 2), LoadU(df, d + 4));
   // Will return this, sum of all line kernels
   auto retval = Mul(sum_yconst, sum_yconst);
   {
     // y grows, x constant
     auto sum = Sum(LoadU(df, d - xs3 - xs), LoadU(df, d - xs - xs), center,
                    LoadU(df, d + xs + xs), LoadU(df, d + xs3 + xs));
     retval = MulAdd(sum, sum, retval);
   }
   {
     // both grow
     auto sum = Sum(LoadU(df, d - xs3 - 3), LoadU(df, d - xs - xs - 2), center,
                    LoadU(df, d + xs + xs + 2), LoadU(df, d + xs3 + 3));
     retval = MulAdd(sum, sum, retval);
   }
   {
     // y grows, x shrinks
     auto sum = Sum(LoadU(df, d - xs3 + 3), LoadU(df, d - xs - xs + 2), center,
                    LoadU(df, d + xs + xs - 2), LoadU(df, d + xs3 - 3));
     retval = MulAdd(sum, sum, retval);
   }
   {
     // y grows -4 to 4, x shrinks 1 -> -1
     auto sum =
         Sum(LoadU(df, d - xs3 - xs + 1), LoadU(df, d - xs - xs + 1), center,
             LoadU(df, d + xs + xs - 1), LoadU(df, d + xs3 + xs - 1));
     retval = MulAdd(sum, sum, retval);
   }
   {
     //  y grows -4 to 4, x grows -1 -> 1
     auto sum =
         Sum(LoadU(df, d - xs3 - xs - 1), LoadU(df, d - xs - xs - 1), center,
             LoadU(df, d + xs + xs + 1), LoadU(df, d + xs3 + xs + 1));
     retval = MulAdd(sum, sum, retval);
   }
   {
     // x grows -4 to 4, y grows -1 to 1
     auto sum = Sum(LoadU(df, d - 4 - xs), LoadU(df, d - 2 - xs), center,
                    LoadU(df, d + 2 + xs), LoadU(df, d + 4 + xs));
     retval = MulAdd(sum, sum, retval);
   }
   {
     // x grows -4 to 4, y shrinks 1 to -1
     auto sum = Sum(LoadU(df, d - 4 + xs), LoadU(df, d - 2 + xs), center,
                    LoadU(df, d + 2 - xs), LoadU(df, d + 4 - xs));
     retval = MulAdd(sum, sum, retval);
   }
   {
     /* 0_________
        1__*______
        2___*_____
        3_________
        4____0____
        5_________
        6_____*___
        7______*__
        8_________ */
     auto sum = Sum(LoadU(df, d - xs3 - 2), LoadU(df, d - xs - xs - 1), center,
                    LoadU(df, d + xs + xs + 1), LoadU(df, d + xs3 + 2));
     retval = MulAdd(sum, sum, retval);
   }
   {
     /* 0_________
        1______*__
        2_____*___
        3_________
        4____0____
        5_________
        6___*_____
        7__*______
        8_________ */
     auto sum = Sum(LoadU(df, d - xs3 + 2), LoadU(df, d - xs - xs + 1), center,
                    LoadU(df, d + xs + xs - 1), LoadU(df, d + xs3 - 2));
     retval = MulAdd(sum, sum, retval);
   }
   {
     /* 0_________
        1_________
        2_*_______
        3__*______
        4____0____
        5______*__
        6_______*_
        7_________
        8_________ */
     auto sum = Sum(LoadU(df, d - xs - xs - 3), LoadU(df, d - xs - 2), center,
                    LoadU(df, d + xs + 2), LoadU(df, d + xs + xs + 3));
     retval = MulAdd(sum, sum, retval);
   }
   {
     /* 0_________
        1_________
        2_______*_
        3______*__
        4____0____
        5__*______
        6_*_______
        7_________
        8_________ */
     auto sum = Sum(LoadU(df, d - xs - xs + 3), LoadU(df, d - xs + 2), center,
                    LoadU(df, d + xs - 2), LoadU(df, d + xs + xs - 3));
     retval = MulAdd(sum, sum, retval);
   }
   {
     /* 0_________
        1_________
        2________*
        3______*__
        4____0____
        5__*______
        6*________
        7_________
        8_________ */
 
     auto sum = Sum(LoadU(df, d + xs + xs - 4), LoadU(df, d + xs - 2), center,
                    LoadU(df, d - xs + 2), LoadU(df, d - xs - xs + 4));
     retval = MulAdd(sum, sum, retval);
   }
   {
     /* 0_________
        1_________
        2*________
        3__*______
        4____0____
        5______*__
        6________*
        7_________
        8_________ */
     auto sum = Sum(LoadU(df, d - xs - xs - 4), LoadU(df, d - xs - 2), center,
                    LoadU(df, d + xs + 2), LoadU(df, d + xs + xs + 4));
     retval = MulAdd(sum, sum, retval);
   }
   {
     /* 0__*______
        1_________
        2___*_____
        3_________
        4____0____
        5_________
        6_____*___
        7_________
        8______*__ */
     auto sum =
         Sum(LoadU(df, d - xs3 - xs - 2), LoadU(df, d - xs - xs - 1), center,
             LoadU(df, d + xs + xs + 1), LoadU(df, d + xs3 + xs + 2));
     retval = MulAdd(sum, sum, retval);
   }
   {
     /* 0______*__
        1_________
        2_____*___
        3_________
        4____0____
        5_________
        6___*_____
        7_________
        8__*______ */
     auto sum =
         Sum(LoadU(df, d - xs3 - xs + 2), LoadU(df, d - xs - xs + 1), center,
             LoadU(df, d + xs + xs - 1), LoadU(df, d + xs3 + xs - 2));
     retval = MulAdd(sum, sum, retval);
   }
   return retval;
 }
 
 template <class D>
 Vec<D> MaltaUnit(MaltaTag /*tag*/, const D df,
                  const float* BUTTERAUGLI_RESTRICT d, const intptr_t xs) {
   const intptr_t xs3 = 3 * xs;
 
   const auto center = LoadU(df, d);
 
   // x grows, y constant
   const auto sum_yconst =
       Sum(LoadU(df, d - 4), LoadU(df, d - 3), LoadU(df, d - 2),
           LoadU(df, d - 1), center, LoadU(df, d + 1), LoadU(df, d + 2),
           LoadU(df, d + 3), LoadU(df, d + 4));
   // Will return this, sum of all line kernels
   auto retval = Mul(sum_yconst, sum_yconst);
 
   {
     // y grows, x constant
     auto sum = Sum(LoadU(df, d - xs3 - xs), LoadU(df, d - xs3),
                    LoadU(df, d - xs - xs), LoadU(df, d - xs), center,
                    LoadU(df, d + xs), LoadU(df, d + xs + xs),
                    LoadU(df, d + xs3), LoadU(df, d + xs3 + xs));
     retval = MulAdd(sum, sum, retval);
   }
   {
     // both grow
     auto sum = Sum(LoadU(df, d - xs3 - 3), LoadU(df, d - xs - xs - 2),
                    LoadU(df, d - xs - 1), center, LoadU(df, d + xs + 1),
                    LoadU(df, d + xs + xs + 2), LoadU(df, d + xs3 + 3));
     retval = MulAdd(sum, sum, retval);
   }
   {
     // y grows, x shrinks
     auto sum = Sum(LoadU(df, d - xs3 + 3), LoadU(df, d - xs - xs + 2),
                    LoadU(df, d - xs + 1), center, LoadU(df, d + xs - 1),
                    LoadU(df, d + xs + xs - 2), LoadU(df, d + xs3 - 3));
     retval = MulAdd(sum, sum, retval);
   }
   {
     // y grows -4 to 4, x shrinks 1 -> -1
     auto sum = Sum(LoadU(df, d - xs3 - xs + 1), LoadU(df, d - xs3 + 1),
                    LoadU(df, d - xs - xs + 1), LoadU(df, d - xs), center,
                    LoadU(df, d + xs), LoadU(df, d + xs + xs - 1),
                    LoadU(df, d + xs3 - 1), LoadU(df, d + xs3 + xs - 1));
     retval = MulAdd(sum, sum, retval);
   }
   {
     //  y grows -4 to 4, x grows -1 -> 1
     auto sum = Sum(LoadU(df, d - xs3 - xs - 1), LoadU(df, d - xs3 - 1),
                    LoadU(df, d - xs - xs - 1), LoadU(df, d - xs), center,
                    LoadU(df, d + xs), LoadU(df, d + xs + xs + 1),
                    LoadU(df, d + xs3 + 1), LoadU(df, d + xs3 + xs + 1));
     retval = MulAdd(sum, sum, retval);
   }
   {
     // x grows -4 to 4, y grows -1 to 1
     auto sum =
         Sum(LoadU(df, d - 4 - xs), LoadU(df, d - 3 - xs), LoadU(df, d - 2 - xs),
             LoadU(df, d - 1), center, LoadU(df, d + 1), LoadU(df, d + 2 + xs),
             LoadU(df, d + 3 + xs), LoadU(df, d + 4 + xs));
     retval = MulAdd(sum, sum, retval);
   }
   {
     // x grows -4 to 4, y shrinks 1 to -1
     auto sum =
         Sum(LoadU(df, d - 4 + xs), LoadU(df, d - 3 + xs), LoadU(df, d - 2 + xs),
             LoadU(df, d - 1), center, LoadU(df, d + 1), LoadU(df, d + 2 - xs),
             LoadU(df, d + 3 - xs), LoadU(df, d + 4 - xs));
     retval = MulAdd(sum, sum, retval);
   }
   {
     /* 0_________
        1__*______
        2___*_____
        3___*_____
        4____0____
        5_____*___
        6_____*___
        7______*__
        8_________ */
     auto sum = Sum(LoadU(df, d - xs3 - 2), LoadU(df, d - xs - xs - 1),
                    LoadU(df, d - xs - 1), center, LoadU(df, d + xs + 1),
                    LoadU(df, d + xs + xs + 1), LoadU(df, d + xs3 + 2));
     retval = MulAdd(sum, sum, retval);
   }
   {
     /* 0_________
        1______*__
        2_____*___
        3_____*___
        4____0____
        5___*_____
        6___*_____
        7__*______
        8_________ */
     auto sum = Sum(LoadU(df, d - xs3 + 2), LoadU(df, d - xs - xs + 1),
                    LoadU(df, d - xs + 1), center, LoadU(df, d + xs - 1),
                    LoadU(df, d + xs + xs - 1), LoadU(df, d + xs3 - 2));
     retval = MulAdd(sum, sum, retval);
   }
   {
     /* 0_________
        1_________
        2_*_______
        3__**_____
        4____0____
        5_____**__
        6_______*_
        7_________
        8_________ */
     auto sum = Sum(LoadU(df, d - xs - xs - 3), LoadU(df, d - xs - 2),
                    LoadU(df, d - xs - 1), center, LoadU(df, d + xs + 1),
                    LoadU(df, d + xs + 2), LoadU(df, d + xs + xs + 3));
     retval = MulAdd(sum, sum, retval);
   }
   {
     /* 0_________
        1_________
        2_______*_
        3_____**__
        4____0____
        5__**_____
        6_*_______
        7_________
        8_________ */
     auto sum = Sum(LoadU(df, d - xs - xs + 3), LoadU(df, d - xs + 2),
                    LoadU(df, d - xs + 1), center, LoadU(df, d + xs - 1),
                    LoadU(df, d + xs - 2), LoadU(df, d + xs + xs - 3));
     retval = MulAdd(sum, sum, retval);
   }
   {
     /* 0_________
        1_________
        2_________
        3______***
        4___*0*___
        5***______
        6_________
        7_________
        8_________ */
 
     auto sum =
         Sum(LoadU(df, d + xs - 4), LoadU(df, d + xs - 3), LoadU(df, d + xs - 2),
             LoadU(df, d - 1), center, LoadU(df, d + 1), LoadU(df, d - xs + 2),
             LoadU(df, d - xs + 3), LoadU(df, d - xs + 4));
     retval = MulAdd(sum, sum, retval);
   }
   {
     /* 0_________
        1_________
        2_________
        3***______
        4___*0*___
        5______***
        6_________
        7_________
        8_________ */
     auto sum =
         Sum(LoadU(df, d - xs - 4), LoadU(df, d - xs - 3), LoadU(df, d - xs - 2),
             LoadU(df, d - 1), center, LoadU(df, d + 1), LoadU(df, d + xs + 2),
             LoadU(df, d + xs + 3), LoadU(df, d + xs + 4));
     retval = MulAdd(sum, sum, retval);
   }
   {
     /* 0___*_____
        1___*_____
        2___*_____
        3____*____
        4____0____
        5____*____
        6_____*___
        7_____*___
        8_____*___ */
     auto sum = Sum(LoadU(df, d - xs3 - xs - 1), LoadU(df, d - xs3 - 1),
                    LoadU(df, d - xs - xs - 1), LoadU(df, d - xs), center,
                    LoadU(df, d + xs), LoadU(df, d + xs + xs + 1),
                    LoadU(df, d + xs3 + 1), LoadU(df, d + xs3 + xs + 1));
     retval = MulAdd(sum, sum, retval);
   }
   {
     /* 0_____*___
        1_____*___
        2____ *___
        3____*____
        4____0____
        5____*____
        6___*_____
        7___*_____
        8___*_____ */
     auto sum = Sum(LoadU(df, d - xs3 - xs + 1), LoadU(df, d - xs3 + 1),
                    LoadU(df, d - xs - xs + 1), LoadU(df, d - xs), center,
                    LoadU(df, d + xs), LoadU(df, d + xs + xs - 1),
                    LoadU(df, d + xs3 - 1), LoadU(df, d + xs3 + xs - 1));
     retval = MulAdd(sum, sum, retval);
   }
   return retval;
 }
 
 // Returns MaltaUnit. Avoids bounds-checks when x0 and y0 are known
 // to be far enough from the image borders. "diffs" is a packed image.
 template <class Tag>
 static BUTTERAUGLI_INLINE float PaddedMaltaUnit(const ImageF& diffs,
                                                 const size_t x0,
                                                 const size_t y0) {
   const float* BUTTERAUGLI_RESTRICT d = diffs.ConstRow(y0) + x0;
   const HWY_CAPPED(float, 1) df;
   if ((x0 >= 4 && y0 >= 4 && x0 < (diffs.xsize() - 4) &&
        y0 < (diffs.ysize() - 4))) {
     return GetLane(MaltaUnit(Tag(), df, d, diffs.PixelsPerRow()));
   }
 
   float borderimage[12 * 9];  // round up to 4
   for (int dy = 0; dy < 9; ++dy) {
     int y = y0 + dy - 4;
     if (y < 0 || static_cast<size_t>(y) >= diffs.ysize()) {
       for (int dx = 0; dx < 12; ++dx) {
         borderimage[dy * 12 + dx] = 0.0f;
       }
       continue;
     }
 
     const float* row_diffs = diffs.ConstRow(y);
     for (int dx = 0; dx < 9; ++dx) {
       int x = x0 + dx - 4;
       if (x < 0 || static_cast<size_t>(x) >= diffs.xsize()) {
         borderimage[dy * 12 + dx] = 0.0f;
       } else {
         borderimage[dy * 12 + dx] = row_diffs[x];
       }
     }
     std::fill(borderimage + dy * 12 + 9, borderimage + dy * 12 + 12, 0.0f);
   }
   return GetLane(MaltaUnit(Tag(), df, &borderimage[4 * 12 + 4], 12));
 }
 
 template <class Tag>
 static void MaltaDiffMapT(const Tag tag, const ImageF& lum0, const ImageF& lum1,
                           const double w_0gt1, const double w_0lt1,
                           const double norm1, const double len,
                           const double mulli, ImageF* HWY_RESTRICT diffs,
                           ImageF* HWY_RESTRICT block_diff_ac) {
   JXL_DASSERT(SameSize(lum0, lum1) && SameSize(lum0, *diffs));
   const size_t xsize_ = lum0.xsize();
   const size_t ysize_ = lum0.ysize();
 
   const float kWeight0 = 0.5;
   const float kWeight1 = 0.33;
 
   const double w_pre0gt1 = mulli * std::sqrt(kWeight0 * w_0gt1) / (len * 2 + 1);
   const double w_pre0lt1 = mulli * std::sqrt(kWeight1 * w_0lt1) / (len * 2 + 1);
   const float norm2_0gt1 = w_pre0gt1 * norm1;
   const float norm2_0lt1 = w_pre0lt1 * norm1;
 
   for (size_t y = 0; y < ysize_; ++y) {
     const float* HWY_RESTRICT row0 = lum0.ConstRow(y);
     const float* HWY_RESTRICT row1 = lum1.ConstRow(y);
     float* HWY_RESTRICT row_diffs = diffs->Row(y);
     for (size_t x = 0; x < xsize_; ++x) {
       const float absval = 0.5f * (std::abs(row0[x]) + std::abs(row1[x]));
       const float diff = row0[x] - row1[x];
       const float scaler = norm2_0gt1 / (static_cast<float>(norm1) + absval);
 
       // Primary symmetric quadratic objective.
       row_diffs[x] = scaler * diff;
 
       const float scaler2 = norm2_0lt1 / (static_cast<float>(norm1) + absval);
       const double fabs0 = std::fabs(row0[x]);
 
       // Secondary half-open quadratic objectives.
       const double too_small = 0.55 * fabs0;
       const double too_big = 1.05 * fabs0;
 
       if (row0[x] < 0) {
         if (row1[x] > -too_small) {
           double impact = scaler2 * (row1[x] + too_small);
           row_diffs[x] -= impact;
         } else if (row1[x] < -too_big) {
           double impact = scaler2 * (-row1[x] - too_big);
           row_diffs[x] += impact;
         }
       } else {
         if (row1[x] < too_small) {
           double impact = scaler2 * (too_small - row1[x]);
           row_diffs[x] += impact;
         } else if (row1[x] > too_big) {
           double impact = scaler2 * (row1[x] - too_big);
           row_diffs[x] -= impact;
         }
       }
     }
   }
 
   size_t y0 = 0;
   // Top
   for (; y0 < 4; ++y0) {
     float* BUTTERAUGLI_RESTRICT row_diff = block_diff_ac->Row(y0);
     for (size_t x0 = 0; x0 < xsize_; ++x0) {
       row_diff[x0] += PaddedMaltaUnit<Tag>(*diffs, x0, y0);
     }
   }
 
   const HWY_FULL(float) df;
   const size_t aligned_x = std::max(static_cast<size_t>(4), Lanes(df));
   const intptr_t stride = diffs->PixelsPerRow();
 
   // Middle
   for (; y0 < ysize_ - 4; ++y0) {
     const float* BUTTERAUGLI_RESTRICT row_in = diffs->ConstRow(y0);
     float* BUTTERAUGLI_RESTRICT row_diff = block_diff_ac->Row(y0);
     size_t x0 = 0;
     for (; x0 < aligned_x; ++x0) {
       row_diff[x0] += PaddedMaltaUnit<Tag>(*diffs, x0, y0);
     }
     for (; x0 + Lanes(df) + 4 <= xsize_; x0 += Lanes(df)) {
       auto diff = Load(df, row_diff + x0);
       diff = Add(diff, MaltaUnit(Tag(), df, row_in + x0, stride));
       Store(diff, df, row_diff + x0);
     }
 
     for (; x0 < xsize_; ++x0) {
       row_diff[x0] += PaddedMaltaUnit<Tag>(*diffs, x0, y0);
     }
   }
 
   // Bottom
   for (; y0 < ysize_; ++y0) {
     float* BUTTERAUGLI_RESTRICT row_diff = block_diff_ac->Row(y0);
     for (size_t x0 = 0; x0 < xsize_; ++x0) {
       row_diff[x0] += PaddedMaltaUnit<Tag>(*diffs, x0, y0);
     }
   }
 }
 
 // Need non-template wrapper functions for HWY_EXPORT.
 void MaltaDiffMap(const ImageF& lum0, const ImageF& lum1, const double w_0gt1,
                   const double w_0lt1, const double norm1,
                   ImageF* HWY_RESTRICT diffs,
                   ImageF* HWY_RESTRICT block_diff_ac) {
   const double len = 3.75;
   static const double mulli = 0.39905817637;
   MaltaDiffMapT(MaltaTag(), lum0, lum1, w_0gt1, w_0lt1, norm1, len, mulli,
                 diffs, block_diff_ac);
 }
 
 void MaltaDiffMapLF(const ImageF& lum0, const ImageF& lum1, const double w_0gt1,
                     const double w_0lt1, const double norm1,
                     ImageF* HWY_RESTRICT diffs,
                     ImageF* HWY_RESTRICT block_diff_ac) {
   const double len = 3.75;
   static const double mulli = 0.611612573796;
   MaltaDiffMapT(MaltaTagLF(), lum0, lum1, w_0gt1, w_0lt1, norm1, len, mulli,
                 diffs, block_diff_ac);
 }
 
 void CombineChannelsForMasking(const ImageF* hf, const ImageF* uhf,
                                ImageF* out) {
   // Only X and Y components are involved in masking. B's influence
   // is considered less important in the high frequency area, and we
   // don't model masking from lower frequency signals.
   static const float muls[3] = {
       2.5f,
       0.4f,
       0.4f,
   };
   // Silly and unoptimized approach here. TODO(jyrki): rework this.
   for (size_t y = 0; y < hf[0].ysize(); ++y) {
     const float* BUTTERAUGLI_RESTRICT row_y_hf = hf[1].Row(y);
     const float* BUTTERAUGLI_RESTRICT row_y_uhf = uhf[1].Row(y);
     const float* BUTTERAUGLI_RESTRICT row_x_hf = hf[0].Row(y);
     const float* BUTTERAUGLI_RESTRICT row_x_uhf = uhf[0].Row(y);
     float* BUTTERAUGLI_RESTRICT row = out->Row(y);
     for (size_t x = 0; x < hf[0].xsize(); ++x) {
       float xdiff = (row_x_uhf[x] + row_x_hf[x]) * muls[0];
       float ydiff = row_y_uhf[x] * muls[1] + row_y_hf[x] * muls[2];
       row[x] = xdiff * xdiff + ydiff * ydiff;
       row[x] = std::sqrt(row[x]);
     }
   }
 }
 
 void DiffPrecompute(const ImageF& xyb, float mul, float bias_arg, ImageF* out) {
   const size_t xsize = xyb.xsize();
   const size_t ysize = xyb.ysize();
   const float bias = mul * bias_arg;
   const float sqrt_bias = std::sqrt(bias);
   for (size_t y = 0; y < ysize; ++y) {
     const float* BUTTERAUGLI_RESTRICT row_in = xyb.Row(y);
     float* BUTTERAUGLI_RESTRICT row_out = out->Row(y);
     for (size_t x = 0; x < xsize; ++x) {
       // kBias makes sqrt behave more linearly.
       row_out[x] = std::sqrt(mul * std::abs(row_in[x]) + bias) - sqrt_bias;
     }
   }
 }
 
 // std::log(80.0) / std::log(255.0);
 constexpr float kIntensityTargetNormalizationHack = 0.79079917404f;
 static const float kInternalGoodQualityThreshold =
     17.83f * kIntensityTargetNormalizationHack;
 static const float kGlobalScale = 1.0 / kInternalGoodQualityThreshold;
 
 void StoreMin3(const float v, float& min0, float& min1, float& min2) {
   if (v < min2) {
     if (v < min0) {
       min2 = min1;
       min1 = min0;
       min0 = v;
     } else if (v < min1) {
       min2 = min1;
       min1 = v;
     } else {
       min2 = v;
     }
   }
 }
 
 // Look for smooth areas near the area of degradation.
 // If the areas area generally smooth, don't do masking.
 void FuzzyErosion(const ImageF& from, ImageF* to) {
   const size_t xsize = from.xsize();
   const size_t ysize = from.ysize();
   static const int kStep = 3;
   for (size_t y = 0; y < ysize; ++y) {
     for (size_t x = 0; x < xsize; ++x) {
       float min0 = from.Row(y)[x];
       float min1 = 2 * min0;
       float min2 = min1;
       if (x >= kStep) {
         float v = from.Row(y)[x - kStep];
         StoreMin3(v, min0, min1, min2);
         if (y >= kStep) {
           float v = from.Row(y - kStep)[x - kStep];
           StoreMin3(v, min0, min1, min2);
         }
         if (y < ysize - kStep) {
           float v = from.Row(y + kStep)[x - kStep];
           StoreMin3(v, min0, min1, min2);
         }
       }
       if (x < xsize - kStep) {
         float v = from.Row(y)[x + kStep];
         StoreMin3(v, min0, min1, min2);
         if (y >= kStep) {
           float v = from.Row(y - kStep)[x + kStep];
           StoreMin3(v, min0, min1, min2);
         }
         if (y < ysize - kStep) {
           float v = from.Row(y + kStep)[x + kStep];
           StoreMin3(v, min0, min1, min2);
         }
       }
       if (y >= kStep) {
         float v = from.Row(y - kStep)[x];
         StoreMin3(v, min0, min1, min2);
       }
       if (y < ysize - kStep) {
         float v = from.Row(y + kStep)[x];
         StoreMin3(v, min0, min1, min2);
       }
       to->Row(y)[x] = (0.45f * min0 + 0.3f * min1 + 0.25f * min2);
     }
   }
 }
 
 // Compute values of local frequency and dc masking based on the activity
 // in the two images. img_diff_ac may be null.
 Status Mask(const ImageF& mask0, const ImageF& mask1,
             const ButteraugliParams& params, BlurTemp* blur_temp,
             ImageF* BUTTERAUGLI_RESTRICT mask,
             ImageF* BUTTERAUGLI_RESTRICT diff_ac) {
   const size_t xsize = mask0.xsize();
   const size_t ysize = mask0.ysize();
   JXL_ASSIGN_OR_RETURN(*mask, ImageF::Create(xsize, ysize));
   static const float kMul = 6.19424080439;
   static const float kBias = 12.61050594197;
   static const float kRadius = 2.7;
   JXL_ASSIGN_OR_RETURN(ImageF diff0, ImageF::Create(xsize, ysize));
   JXL_ASSIGN_OR_RETURN(ImageF diff1, ImageF::Create(xsize, ysize));
   JXL_ASSIGN_OR_RETURN(ImageF blurred0, ImageF::Create(xsize, ysize));
   JXL_ASSIGN_OR_RETURN(ImageF blurred1, ImageF::Create(xsize, ysize));
   DiffPrecompute(mask0, kMul, kBias, &diff0);
   DiffPrecompute(mask1, kMul, kBias, &diff1);
   JXL_RETURN_IF_ERROR(Blur(diff0, kRadius, params, blur_temp, &blurred0));
   FuzzyErosion(blurred0, &diff0);
   JXL_RETURN_IF_ERROR(Blur(diff1, kRadius, params, blur_temp, &blurred1));
   for (size_t y = 0; y < ysize; ++y) {
     for (size_t x = 0; x < xsize; ++x) {
       mask->Row(y)[x] = diff0.Row(y)[x];
       if (diff_ac != nullptr) {
         static const float kMaskToErrorMul = 10.0;
         float diff = blurred0.Row(y)[x] - blurred1.Row(y)[x];
         diff_ac->Row(y)[x] += kMaskToErrorMul * diff * diff;
       }
     }
   }
   return true;
 }
 
 // `diff_ac` may be null.
 Status MaskPsychoImage(const PsychoImage& pi0, const PsychoImage& pi1,
                        const size_t xsize, const size_t ysize,
                        const ButteraugliParams& params, BlurTemp* blur_temp,
                        ImageF* BUTTERAUGLI_RESTRICT mask,
                        ImageF* BUTTERAUGLI_RESTRICT diff_ac) {
   JXL_ASSIGN_OR_RETURN(ImageF mask0, ImageF::Create(xsize, ysize));
   JXL_ASSIGN_OR_RETURN(ImageF mask1, ImageF::Create(xsize, ysize));
   CombineChannelsForMasking(&pi0.hf[0], &pi0.uhf[0], &mask0);
   CombineChannelsForMasking(&pi1.hf[0], &pi1.uhf[0], &mask1);
   JXL_RETURN_IF_ERROR(Mask(mask0, mask1, params, blur_temp, mask, diff_ac));
   return true;
 }
 
 double MaskY(double delta) {
   static const double offset = 0.829591754942;
   static const double scaler = 0.451936922203;
   static const double mul = 2.5485944793;
   const double c = mul / ((scaler * delta) + offset);
   const double retval = kGlobalScale * (1.0 + c);
   return retval * retval;
 }
 
 double MaskDcY(double delta) {
   static const double offset = 0.20025578522;
   static const double scaler = 3.87449418804;
   static const double mul = 0.505054525019;
   const double c = mul / ((scaler * delta) + offset);
   const double retval = kGlobalScale * (1.0 + c);
   return retval * retval;
 }
 
 inline float MaskColor(const float color[3], const float mask) {
   return color[0] * mask + color[1] * mask + color[2] * mask;
 }
 
 // Diffmap := sqrt of sum{diff images by multiplied by X and Y/B masks}
 void CombineChannelsToDiffmap(const ImageF& mask, const Image3F& block_diff_dc,
                               const Image3F& block_diff_ac, float xmul,
                               ImageF* result) {
   JXL_CHECK(SameSize(mask, *result));
   size_t xsize = mask.xsize();
   size_t ysize = mask.ysize();
   for (size_t y = 0; y < ysize; ++y) {
     float* BUTTERAUGLI_RESTRICT row_out = result->Row(y);
     for (size_t x = 0; x < xsize; ++x) {
       float val = mask.Row(y)[x];
       float maskval = MaskY(val);
       float dc_maskval = MaskDcY(val);
       float diff_dc[3];
       float diff_ac[3];
       for (int i = 0; i < 3; ++i) {
         diff_dc[i] = block_diff_dc.PlaneRow(i, y)[x];
         diff_ac[i] = block_diff_ac.PlaneRow(i, y)[x];
       }
       diff_ac[0] *= xmul;
       diff_dc[0] *= xmul;
       row_out[x] = std::sqrt(MaskColor(diff_dc, dc_maskval) +
                              MaskColor(diff_ac, maskval));
     }
   }
 }
 
 // Adds weighted L2 difference between i0 and i1 to diffmap.
 static void L2Diff(const ImageF& i0, const ImageF& i1, const float w,
                    ImageF* BUTTERAUGLI_RESTRICT diffmap) {
   if (w == 0) return;
 
   const HWY_FULL(float) d;
   const auto weight = Set(d, w);
 
   for (size_t y = 0; y < i0.ysize(); ++y) {
     const float* BUTTERAUGLI_RESTRICT row0 = i0.ConstRow(y);
     const float* BUTTERAUGLI_RESTRICT row1 = i1.ConstRow(y);
     float* BUTTERAUGLI_RESTRICT row_diff = diffmap->Row(y);
 
     for (size_t x = 0; x < i0.xsize(); x += Lanes(d)) {
       const auto diff = Sub(Load(d, row0 + x), Load(d, row1 + x));
       const auto diff2 = Mul(diff, diff);
       const auto prev = Load(d, row_diff + x);
       Store(MulAdd(diff2, weight, prev), d, row_diff + x);
     }
   }
 }
 
 // Initializes diffmap to the weighted L2 difference between i0 and i1.
 static void SetL2Diff(const ImageF& i0, const ImageF& i1, const float w,
                       ImageF* BUTTERAUGLI_RESTRICT diffmap) {
   if (w == 0) return;
 
   const HWY_FULL(float) d;
   const auto weight = Set(d, w);
 
   for (size_t y = 0; y < i0.ysize(); ++y) {
     const float* BUTTERAUGLI_RESTRICT row0 = i0.ConstRow(y);
     const float* BUTTERAUGLI_RESTRICT row1 = i1.ConstRow(y);
     float* BUTTERAUGLI_RESTRICT row_diff = diffmap->Row(y);
 
     for (size_t x = 0; x < i0.xsize(); x += Lanes(d)) {
       const auto diff = Sub(Load(d, row0 + x), Load(d, row1 + x));
       const auto diff2 = Mul(diff, diff);
       Store(Mul(diff2, weight), d, row_diff + x);
     }
   }
 }
 
 // i0 is the original image.
 // i1 is the deformed copy.
 static void L2DiffAsymmetric(const ImageF& i0, const ImageF& i1, float w_0gt1,
                              float w_0lt1,
                              ImageF* BUTTERAUGLI_RESTRICT diffmap) {
   if (w_0gt1 == 0 && w_0lt1 == 0) {
     return;
   }
 
   const HWY_FULL(float) d;
   const auto vw_0gt1 = Set(d, w_0gt1 * 0.8);
   const auto vw_0lt1 = Set(d, w_0lt1 * 0.8);
 
   for (size_t y = 0; y < i0.ysize(); ++y) {
     const float* BUTTERAUGLI_RESTRICT row0 = i0.Row(y);
     const float* BUTTERAUGLI_RESTRICT row1 = i1.Row(y);
     float* BUTTERAUGLI_RESTRICT row_diff = diffmap->Row(y);
 
     for (size_t x = 0; x < i0.xsize(); x += Lanes(d)) {
       const auto val0 = Load(d, row0 + x);
       const auto val1 = Load(d, row1 + x);
 
       // Primary symmetric quadratic objective.
       const auto diff = Sub(val0, val1);
       auto total = MulAdd(Mul(diff, diff), vw_0gt1, Load(d, row_diff + x));
 
       // Secondary half-open quadratic objectives.
       const auto fabs0 = Abs(val0);
       const auto too_small = Mul(Set(d, 0.4), fabs0);
       const auto too_big = fabs0;
 
       const auto if_neg = IfThenElse(
           Gt(val1, Neg(too_small)), Add(val1, too_small),
           IfThenElseZero(Lt(val1, Neg(too_big)), Sub(Neg(val1), too_big)));
       const auto if_pos =
           IfThenElse(Lt(val1, too_small), Sub(too_small, val1),
                      IfThenElseZero(Gt(val1, too_big), Sub(val1, too_big)));
       const auto v = IfThenElse(Lt(val0, Zero(d)), if_neg, if_pos);
       total = MulAdd(vw_0lt1, Mul(v, v), total);
       Store(total, d, row_diff + x);
     }
   }
 }
 
 // A simple HDR compatible gamma function.
 template <class DF, class V>
 V Gamma(const DF df, V v) {
   // ln(2) constant folded in because we want std::log but have FastLog2f.
   const auto kRetMul = Set(df, 19.245013259874995f * 0.693147180559945f);
   const auto kRetAdd = Set(df, -23.16046239805755);
   // This should happen rarely, but may lead to a NaN in log, which is
   // undesirable. Since negative photons don't exist we solve the NaNs by
   // clamping here.
   v = ZeroIfNegative(v);
 
   const auto biased = Add(v, Set(df, 9.9710635769299145));
   const auto log = FastLog2f(df, biased);
   // We could fold this into a custom Log2 polynomial, but there would be
   // relatively little gain.
   return MulAdd(kRetMul, log, kRetAdd);
 }
 
 template <bool Clamp, class DF, class V>
 BUTTERAUGLI_INLINE void OpsinAbsorbance(const DF df, const V& in0, const V& in1,
                                         const V& in2, V* JXL_RESTRICT out0,
                                         V* JXL_RESTRICT out1,
                                         V* JXL_RESTRICT out2) {
   // https://en.wikipedia.org/wiki/Photopsin absorbance modeling.
   static const double mixi0 = 0.29956550340058319;
   static const double mixi1 = 0.63373087833825936;
   static const double mixi2 = 0.077705617820981968;
   static const double mixi3 = 1.7557483643287353;
   static const double mixi4 = 0.22158691104574774;
   static const double mixi5 = 0.69391388044116142;
   static const double mixi6 = 0.0987313588422;
   static const double mixi7 = 1.7557483643287353;
   static const double mixi8 = 0.02;
   static const double mixi9 = 0.02;
   static const double mixi10 = 0.20480129041026129;
   static const double mixi11 = 12.226454707163354;
 
   const V mix0 = Set(df, mixi0);
   const V mix1 = Set(df, mixi1);
   const V mix2 = Set(df, mixi2);
   const V mix3 = Set(df, mixi3);
   const V mix4 = Set(df, mixi4);
   const V mix5 = Set(df, mixi5);
   const V mix6 = Set(df, mixi6);
   const V mix7 = Set(df, mixi7);
   const V mix8 = Set(df, mixi8);
   const V mix9 = Set(df, mixi9);
   const V mix10 = Set(df, mixi10);
   const V mix11 = Set(df, mixi11);
 
   *out0 = MulAdd(mix0, in0, MulAdd(mix1, in1, MulAdd(mix2, in2, mix3)));
   *out1 = MulAdd(mix4, in0, MulAdd(mix5, in1, MulAdd(mix6, in2, mix7)));
   *out2 = MulAdd(mix8, in0, MulAdd(mix9, in1, MulAdd(mix10, in2, mix11)));
 
   if (Clamp) {
     *out0 = Max(*out0, mix3);
     *out1 = Max(*out1, mix7);
     *out2 = Max(*out2, mix11);
   }
 }
 
 // `blurred` is a temporary image used inside this function and not returned.
 Status OpsinDynamicsImage(const Image3F& rgb, const ButteraugliParams& params,
                           Image3F* blurred, BlurTemp* blur_temp, Image3F* xyb) {
   const double kSigma = 1.2;
   JXL_RETURN_IF_ERROR(
       Blur(rgb.Plane(0), kSigma, params, blur_temp, &blurred->Plane(0)));
   JXL_RETURN_IF_ERROR(
       Blur(rgb.Plane(1), kSigma, params, blur_temp, &blurred->Plane(1)));
   JXL_RETURN_IF_ERROR(
       Blur(rgb.Plane(2), kSigma, params, blur_temp, &blurred->Plane(2)));
   const HWY_FULL(float) df;
   const auto intensity_target_multiplier = Set(df, params.intensity_target);
   for (size_t y = 0; y < rgb.ysize(); ++y) {
     const float* row_r = rgb.ConstPlaneRow(0, y);
     const float* row_g = rgb.ConstPlaneRow(1, y);
     const float* row_b = rgb.ConstPlaneRow(2, y);
     const float* row_blurred_r = blurred->ConstPlaneRow(0, y);
     const float* row_blurred_g = blurred->ConstPlaneRow(1, y);
     const float* row_blurred_b = blurred->ConstPlaneRow(2, y);
     float* row_out_x = xyb->PlaneRow(0, y);
     float* row_out_y = xyb->PlaneRow(1, y);
     float* row_out_b = xyb->PlaneRow(2, y);
     const auto min = Set(df, 1e-4f);
     for (size_t x = 0; x < rgb.xsize(); x += Lanes(df)) {
       auto sensitivity0 = Undefined(df);
       auto sensitivity1 = Undefined(df);
       auto sensitivity2 = Undefined(df);
       {
         // Calculate sensitivity based on the smoothed image gamma derivative.
         auto pre_mixed0 = Undefined(df);
         auto pre_mixed1 = Undefined(df);
         auto pre_mixed2 = Undefined(df);
         OpsinAbsorbance<true>(
             df, Mul(Load(df, row_blurred_r + x), intensity_target_multiplier),
             Mul(Load(df, row_blurred_g + x), intensity_target_multiplier),
             Mul(Load(df, row_blurred_b + x), intensity_target_multiplier),
             &pre_mixed0, &pre_mixed1, &pre_mixed2);
         pre_mixed0 = Max(pre_mixed0, min);
         pre_mixed1 = Max(pre_mixed1, min);
         pre_mixed2 = Max(pre_mixed2, min);
         sensitivity0 = Div(Gamma(df, pre_mixed0), pre_mixed0);
         sensitivity1 = Div(Gamma(df, pre_mixed1), pre_mixed1);
         sensitivity2 = Div(Gamma(df, pre_mixed2), pre_mixed2);
         sensitivity0 = Max(sensitivity0, min);
         sensitivity1 = Max(sensitivity1, min);
         sensitivity2 = Max(sensitivity2, min);
       }
       auto cur_mixed0 = Undefined(df);
       auto cur_mixed1 = Undefined(df);
       auto cur_mixed2 = Undefined(df);
       OpsinAbsorbance<false>(
           df, Mul(Load(df, row_r + x), intensity_target_multiplier),
           Mul(Load(df, row_g + x), intensity_target_multiplier),
           Mul(Load(df, row_b + x), intensity_target_multiplier), &cur_mixed0,
           &cur_mixed1, &cur_mixed2);
       cur_mixed0 = Mul(cur_mixed0, sensitivity0);
       cur_mixed1 = Mul(cur_mixed1, sensitivity1);
       cur_mixed2 = Mul(cur_mixed2, sensitivity2);
       // This is a kludge. The negative values should be zeroed away before
       // blurring. Ideally there would be no negative values in the first place.
       const auto min01 = Set(df, 1.7557483643287353f);
       const auto min2 = Set(df, 12.226454707163354f);
       cur_mixed0 = Max(cur_mixed0, min01);
       cur_mixed1 = Max(cur_mixed1, min01);
       cur_mixed2 = Max(cur_mixed2, min2);
 
       Store(Sub(cur_mixed0, cur_mixed1), df, row_out_x + x);
       Store(Add(cur_mixed0, cur_mixed1), df, row_out_y + x);
       Store(cur_mixed2, df, row_out_b + x);
     }
   }
   return true;
 }
 
 Status ButteraugliDiffmapInPlace(Image3F& image0, Image3F& image1,
                                  const ButteraugliParams& params,
                                  ImageF& diffmap) {
   // image0 and image1 are in linear sRGB color space
   const size_t xsize = image0.xsize();
   const size_t ysize = image0.ysize();
   BlurTemp blur_temp;
   {
     // Convert image0 and image1 to XYB in-place
     JXL_ASSIGN_OR_RETURN(Image3F temp, Image3F::Create(xsize, ysize));
     JXL_RETURN_IF_ERROR(
         OpsinDynamicsImage(image0, params, &temp, &blur_temp, &image0));
     JXL_RETURN_IF_ERROR(
         OpsinDynamicsImage(image1, params, &temp, &blur_temp, &image1));
   }
   // image0 and image1 are in XYB color space
   JXL_ASSIGN_OR_RETURN(ImageF block_diff_dc, ImageF::Create(xsize, ysize));
   ZeroFillImage(&block_diff_dc);
   {
     // separate out LF components from image0 and image1 and compute the dc
     // diff image from them
     JXL_ASSIGN_OR_RETURN(Image3F lf0, Image3F::Create(xsize, ysize));
     JXL_ASSIGN_OR_RETURN(Image3F lf1, Image3F::Create(xsize, ysize));
     JXL_RETURN_IF_ERROR(
         SeparateLFAndMF(params, image0, &lf0, &image0, &blur_temp));
     JXL_RETURN_IF_ERROR(
         SeparateLFAndMF(params, image1, &lf1, &image1, &blur_temp));
     for (size_t c = 0; c < 3; ++c) {
       L2Diff(lf0.Plane(c), lf1.Plane(c), wmul[6 + c], &block_diff_dc);
     }
   }
   // image0 and image1 are MF residuals (before blurring) in XYB color space
   ImageF hf0[2];
   ImageF hf1[2];
   JXL_RETURN_IF_ERROR(SeparateMFAndHF(params, &image0, &hf0[0], &blur_temp));
   JXL_RETURN_IF_ERROR(SeparateMFAndHF(params, &image1, &hf1[0], &blur_temp));
   // image0 and image1 are MF-images in XYB color space
 
   JXL_ASSIGN_OR_RETURN(ImageF block_diff_ac, ImageF::Create(xsize, ysize));
   ZeroFillImage(&block_diff_ac);
   // start accumulating ac diff image from MF images
   {
     JXL_ASSIGN_OR_RETURN(ImageF diffs, ImageF::Create(xsize, ysize));
     MaltaDiffMapLF(image0.Plane(1), image1.Plane(1), wMfMalta, wMfMalta,
                    norm1Mf, &diffs, &block_diff_ac);
     MaltaDiffMapLF(image0.Plane(0), image1.Plane(0), wMfMaltaX, wMfMaltaX,
                    norm1MfX, &diffs, &block_diff_ac);
   }
   for (size_t c = 0; c < 3; ++c) {
     L2Diff(image0.Plane(c), image1.Plane(c), wmul[3 + c], &block_diff_ac);
   }
   // we will not need the MF-images and more, so we deallocate them to reduce
   // peak memory usage
   image0 = Image3F();
   image1 = Image3F();
 
   ImageF uhf0[2];
   ImageF uhf1[2];
   JXL_RETURN_IF_ERROR(SeparateHFAndUHF(params, &hf0[0], &uhf0[0], &blur_temp));
   JXL_RETURN_IF_ERROR(SeparateHFAndUHF(params, &hf1[0], &uhf1[0], &blur_temp));
 
   // continue accumulating ac diff image from HF and UHF images
   const float hf_asymmetry = params.hf_asymmetry;
   {
     JXL_ASSIGN_OR_RETURN(ImageF diffs, ImageF::Create(xsize, ysize));
     MaltaDiffMap(uhf0[1], uhf1[1], wUhfMalta * hf_asymmetry,
                  wUhfMalta / hf_asymmetry, norm1Uhf, &diffs, &block_diff_ac);
     MaltaDiffMap(uhf0[0], uhf1[0], wUhfMaltaX * hf_asymmetry,
                  wUhfMaltaX / hf_asymmetry, norm1UhfX, &diffs, &block_diff_ac);
     MaltaDiffMapLF(hf0[1], hf1[1], wHfMalta * std::sqrt(hf_asymmetry),
                    wHfMalta / std::sqrt(hf_asymmetry), norm1Hf, &diffs,
                    &block_diff_ac);
     MaltaDiffMapLF(hf0[0], hf1[0], wHfMaltaX * std::sqrt(hf_asymmetry),
                    wHfMaltaX / std::sqrt(hf_asymmetry), norm1HfX, &diffs,
                    &block_diff_ac);
   }
   for (size_t c = 0; c < 2; ++c) {
     L2DiffAsymmetric(hf0[c], hf1[c], wmul[c] * hf_asymmetry,
                      wmul[c] / hf_asymmetry, &block_diff_ac);
   }
 
   // compute mask image from HF and UHF X and Y images
   JXL_ASSIGN_OR_RETURN(ImageF mask, ImageF::Create(xsize, ysize));
   {
     JXL_ASSIGN_OR_RETURN(ImageF mask0, ImageF::Create(xsize, ysize));
     JXL_ASSIGN_OR_RETURN(ImageF mask1, ImageF::Create(xsize, ysize));
     CombineChannelsForMasking(&hf0[0], &uhf0[0], &mask0);
     CombineChannelsForMasking(&hf1[0], &uhf1[0], &mask1);
     DeallocateHFAndUHF(&hf1[0], &uhf1[0]);
     DeallocateHFAndUHF(&hf0[0], &uhf0[0]);
     JXL_RETURN_IF_ERROR(
         Mask(mask0, mask1, params, &blur_temp, &mask, &block_diff_ac));
   }
 
   // compute final diffmap from mask image and ac and dc diff images
   JXL_ASSIGN_OR_RETURN(diffmap, ImageF::Create(xsize, ysize));
   for (size_t y = 0; y < ysize; ++y) {
     const float* row_dc = block_diff_dc.Row(y);
     const float* row_ac = block_diff_ac.Row(y);
     float* row_out = diffmap.Row(y);
     for (size_t x = 0; x < xsize; ++x) {
       const float val = mask.Row(y)[x];
       row_out[x] = sqrt(row_dc[x] * MaskDcY(val) + row_ac[x] * MaskY(val));
     }
   }
   return true;
 }
 
 // NOLINTNEXTLINE(google-readability-namespace-comments)
 }  // namespace HWY_NAMESPACE
 }  // namespace jxl
 HWY_AFTER_NAMESPACE();
 
 #if HWY_ONCE
 namespace jxl {
 
 HWY_EXPORT(SeparateFrequencies);       // Local function.
 HWY_EXPORT(MaskPsychoImage);           // Local function.
 HWY_EXPORT(L2DiffAsymmetric);          // Local function.
 HWY_EXPORT(L2Diff);                    // Local function.
 HWY_EXPORT(SetL2Diff);                 // Local function.
 HWY_EXPORT(CombineChannelsToDiffmap);  // Local function.
 HWY_EXPORT(MaltaDiffMap);              // Local function.
 HWY_EXPORT(MaltaDiffMapLF);            // Local function.
 HWY_EXPORT(OpsinDynamicsImage);        // Local function.
 HWY_EXPORT(ButteraugliDiffmapInPlace);  // Local function.
 
 #if BUTTERAUGLI_ENABLE_CHECKS
 
 static inline bool IsNan(const float x) {
   uint32_t bits;
   memcpy(&bits, &x, sizeof(bits));
   const uint32_t bitmask_exp = 0x7F800000;
   return (bits & bitmask_exp) == bitmask_exp && (bits & 0x7FFFFF);
 }
 
 static inline bool IsNan(const double x) {
   uint64_t bits;
   memcpy(&bits, &x, sizeof(bits));
   return (0x7ff0000000000001ULL <= bits && bits <= 0x7fffffffffffffffULL) ||
          (0xfff0000000000001ULL <= bits && bits <= 0xffffffffffffffffULL);
 }
 
 static inline void CheckImage(const ImageF& image, const char* name) {
   for (size_t y = 0; y < image.ysize(); ++y) {
     const float* BUTTERAUGLI_RESTRICT row = image.Row(y);
     for (size_t x = 0; x < image.xsize(); ++x) {
       if (IsNan(row[x])) {
         printf("NAN: Image %s @ %" PRIuS ",%" PRIuS " (of %" PRIuS ",%" PRIuS
                ")\n",
                name, x, y, image.xsize(), image.ysize());
         exit(1);
       }
     }
   }
 }
 
 #define CHECK_NAN(x, str)                \
   do {                                   \
     if (IsNan(x)) {                      \
       printf("%d: %s\n", __LINE__, str); \
       abort();                           \
     }                                    \
   } while (0)
 
 #define CHECK_IMAGE(image, name) CheckImage(image, name)
 
 #else  // BUTTERAUGLI_ENABLE_CHECKS
 
 #define CHECK_NAN(x, str)
 #define CHECK_IMAGE(image, name)
 
 #endif  // BUTTERAUGLI_ENABLE_CHECKS
 
 // Calculate a 2x2 subsampled image for purposes of recursive butteraugli at
 // multiresolution.
 static StatusOr<Image3F> SubSample2x(const Image3F& in) {
   size_t xs = (in.xsize() + 1) / 2;
   size_t ys = (in.ysize() + 1) / 2;
   JXL_ASSIGN_OR_RETURN(Image3F retval, Image3F::Create(xs, ys));
   for (size_t c = 0; c < 3; ++c) {
     for (size_t y = 0; y < ys; ++y) {
       for (size_t x = 0; x < xs; ++x) {
         retval.PlaneRow(c, y)[x] = 0;
       }
     }
   }
   for (size_t c = 0; c < 3; ++c) {
     for (size_t y = 0; y < in.ysize(); ++y) {
       for (size_t x = 0; x < in.xsize(); ++x) {
         retval.PlaneRow(c, y / 2)[x / 2] += 0.25f * in.PlaneRow(c, y)[x];
       }
     }
     if ((in.xsize() & 1) != 0) {
       for (size_t y = 0; y < retval.ysize(); ++y) {
         size_t last_column = retval.xsize() - 1;
         retval.PlaneRow(c, y)[last_column] *= 2.0f;
       }
     }
     if ((in.ysize() & 1) != 0) {
       for (size_t x = 0; x < retval.xsize(); ++x) {
         size_t last_row = retval.ysize() - 1;
         retval.PlaneRow(c, last_row)[x] *= 2.0f;
       }
     }
   }
   return retval;
 }
 
 // Supersample src by 2x and add it to dest.
 static void AddSupersampled2x(const ImageF& src, float w, ImageF& dest) {
   for (size_t y = 0; y < dest.ysize(); ++y) {
     for (size_t x = 0; x < dest.xsize(); ++x) {
       // There will be less errors from the more averaged images.
       // We take it into account to some extent using a scaler.
       static const double kHeuristicMixingValue = 0.3;
       dest.Row(y)[x] *= 1.0 - kHeuristicMixingValue * w;
       dest.Row(y)[x] += w * src.Row(y / 2)[x / 2];
     }
   }
 }
 
 Image3F* ButteraugliComparator::Temp() const {
   bool was_in_use = temp_in_use_.test_and_set(std::memory_order_acq_rel);
   JXL_ASSERT(!was_in_use);
   (void)was_in_use;
   return &temp_;
 }
 
 void ButteraugliComparator::ReleaseTemp() const { temp_in_use_.clear(); }
 
 ButteraugliComparator::ButteraugliComparator(size_t xsize, size_t ysize,
                                              const ButteraugliParams& params)
     : xsize_(xsize), ysize_(ysize), params_(params) {}
 
 StatusOr<std::unique_ptr<ButteraugliComparator>> ButteraugliComparator::Make(
     const Image3F& rgb0, const ButteraugliParams& params) {
   size_t xsize = rgb0.xsize();
   size_t ysize = rgb0.ysize();
   std::unique_ptr<ButteraugliComparator> result =
       std::unique_ptr<ButteraugliComparator>(
           new ButteraugliComparator(xsize, ysize, params));
   JXL_ASSIGN_OR_RETURN(result->temp_, Image3F::Create(xsize, ysize));
 
   if (xsize < 8 || ysize < 8) {
     return result;
   }
 
   JXL_ASSIGN_OR_RETURN(Image3F xyb0, Image3F::Create(xsize, ysize));
   JXL_RETURN_IF_ERROR(HWY_DYNAMIC_DISPATCH(OpsinDynamicsImage)(
       rgb0, params, result->Temp(), &result->blur_temp_, &xyb0));
   result->ReleaseTemp();
   JXL_RETURN_IF_ERROR(HWY_DYNAMIC_DISPATCH(SeparateFrequencies)(
       xsize, ysize, params, &result->blur_temp_, xyb0, result->pi0_));
 
   // Awful recursive construction of samples of different resolution.
   // This is an after-thought and possibly somewhat parallel in
   // functionality with the PsychoImage multi-resolution approach.
   JXL_ASSIGN_OR_RETURN(Image3F subsampledRgb0, SubSample2x(rgb0));
   StatusOr<std::unique_ptr<ButteraugliComparator>> sub =
       ButteraugliComparator::Make(subsampledRgb0, params);
   if (!sub.ok()) return sub.status();
   result->sub_ = std::move(sub).value();
 
   return result;
 }
 
 Status ButteraugliComparator::Mask(ImageF* BUTTERAUGLI_RESTRICT mask) const {
   return HWY_DYNAMIC_DISPATCH(MaskPsychoImage)(
       pi0_, pi0_, xsize_, ysize_, params_, &blur_temp_, mask, nullptr);
 }
 
 Status ButteraugliComparator::Diffmap(const Image3F& rgb1,
                                       ImageF& result) const {
   if (xsize_ < 8 || ysize_ < 8) {
     ZeroFillImage(&result);
     return true;
   }
   JXL_ASSIGN_OR_RETURN(Image3F xyb1, Image3F::Create(xsize_, ysize_));
   JXL_RETURN_IF_ERROR(HWY_DYNAMIC_DISPATCH(OpsinDynamicsImage)(
       rgb1, params_, Temp(), &blur_temp_, &xyb1));
   ReleaseTemp();
   JXL_RETURN_IF_ERROR(DiffmapOpsinDynamicsImage(xyb1, result));
   if (sub_) {
     if (sub_->xsize_ < 8 || sub_->ysize_ < 8) {
       return true;
     }
     JXL_ASSIGN_OR_RETURN(Image3F sub_xyb,
                          Image3F::Create(sub_->xsize_, sub_->ysize_));
     JXL_ASSIGN_OR_RETURN(Image3F subsampledRgb1, SubSample2x(rgb1));
     JXL_RETURN_IF_ERROR(HWY_DYNAMIC_DISPATCH(OpsinDynamicsImage)(
         subsampledRgb1, params_, sub_->Temp(), &sub_->blur_temp_, &sub_xyb));
     sub_->ReleaseTemp();
     ImageF subresult;
     JXL_RETURN_IF_ERROR(sub_->DiffmapOpsinDynamicsImage(sub_xyb, subresult));
     AddSupersampled2x(subresult, 0.5, result);
   }
   return true;
 }
 
 Status ButteraugliComparator::DiffmapOpsinDynamicsImage(const Image3F& xyb1,
                                                         ImageF& result) const {
   if (xsize_ < 8 || ysize_ < 8) {
     ZeroFillImage(&result);
     return true;
   }
   PsychoImage pi1;
   JXL_RETURN_IF_ERROR(HWY_DYNAMIC_DISPATCH(SeparateFrequencies)(
       xsize_, ysize_, params_, &blur_temp_, xyb1, pi1));
   JXL_ASSIGN_OR_RETURN(result, ImageF::Create(xsize_, ysize_));
   return DiffmapPsychoImage(pi1, result);
 }
 
 namespace {
 
 void MaltaDiffMap(const ImageF& lum0, const ImageF& lum1, const double w_0gt1,
                   const double w_0lt1, const double norm1,
                   ImageF* HWY_RESTRICT diffs,
                   Image3F* HWY_RESTRICT block_diff_ac, size_t c) {
   HWY_DYNAMIC_DISPATCH(MaltaDiffMap)
   (lum0, lum1, w_0gt1, w_0lt1, norm1, diffs, &block_diff_ac->Plane(c));
 }
 
 void MaltaDiffMapLF(const ImageF& lum0, const ImageF& lum1, const double w_0gt1,
                     const double w_0lt1, const double norm1,
                     ImageF* HWY_RESTRICT diffs,
                     Image3F* HWY_RESTRICT block_diff_ac, size_t c) {
   HWY_DYNAMIC_DISPATCH(MaltaDiffMapLF)
   (lum0, lum1, w_0gt1, w_0lt1, norm1, diffs, &block_diff_ac->Plane(c));
 }
 
 }  // namespace
 
 Status ButteraugliComparator::DiffmapPsychoImage(const PsychoImage& pi1,
                                                  ImageF& diffmap) const {
   if (xsize_ < 8 || ysize_ < 8) {
     ZeroFillImage(&diffmap);
     return true;
   }
 
   const float hf_asymmetry_ = params_.hf_asymmetry;
   const float xmul_ = params_.xmul;
 
   JXL_ASSIGN_OR_RETURN(ImageF diffs, ImageF::Create(xsize_, ysize_));
   JXL_ASSIGN_OR_RETURN(Image3F block_diff_ac, Image3F::Create(xsize_, ysize_));
   ZeroFillImage(&block_diff_ac);
   MaltaDiffMap(pi0_.uhf[1], pi1.uhf[1], wUhfMalta * hf_asymmetry_,
                wUhfMalta / hf_asymmetry_, norm1Uhf, &diffs, &block_diff_ac, 1);
   MaltaDiffMap(pi0_.uhf[0], pi1.uhf[0], wUhfMaltaX * hf_asymmetry_,
                wUhfMaltaX / hf_asymmetry_, norm1UhfX, &diffs, &block_diff_ac,
                0);
   MaltaDiffMapLF(pi0_.hf[1], pi1.hf[1], wHfMalta * std::sqrt(hf_asymmetry_),
                  wHfMalta / std::sqrt(hf_asymmetry_), norm1Hf, &diffs,
                  &block_diff_ac, 1);
   MaltaDiffMapLF(pi0_.hf[0], pi1.hf[0], wHfMaltaX * std::sqrt(hf_asymmetry_),
                  wHfMaltaX / std::sqrt(hf_asymmetry_), norm1HfX, &diffs,
                  &block_diff_ac, 0);
   MaltaDiffMapLF(pi0_.mf.Plane(1), pi1.mf.Plane(1), wMfMalta, wMfMalta, norm1Mf,
                  &diffs, &block_diff_ac, 1);
   MaltaDiffMapLF(pi0_.mf.Plane(0), pi1.mf.Plane(0), wMfMaltaX, wMfMaltaX,
                  norm1MfX, &diffs, &block_diff_ac, 0);
 
   JXL_ASSIGN_OR_RETURN(Image3F block_diff_dc, Image3F::Create(xsize_, ysize_));
   for (size_t c = 0; c < 3; ++c) {
     if (c < 2) {  // No blue channel error accumulated at HF.
       HWY_DYNAMIC_DISPATCH(L2DiffAsymmetric)
       (pi0_.hf[c], pi1.hf[c], wmul[c] * hf_asymmetry_, wmul[c] / hf_asymmetry_,
        &block_diff_ac.Plane(c));
     }
     HWY_DYNAMIC_DISPATCH(L2Diff)
     (pi0_.mf.Plane(c), pi1.mf.Plane(c), wmul[3 + c], &block_diff_ac.Plane(c));
     HWY_DYNAMIC_DISPATCH(SetL2Diff)
     (pi0_.lf.Plane(c), pi1.lf.Plane(c), wmul[6 + c], &block_diff_dc.Plane(c));
   }
 
   ImageF mask;
   JXL_RETURN_IF_ERROR(HWY_DYNAMIC_DISPATCH(MaskPsychoImage)(
       pi0_, pi1, xsize_, ysize_, params_, &blur_temp_, &mask,
       &block_diff_ac.Plane(1)));
 
   HWY_DYNAMIC_DISPATCH(CombineChannelsToDiffmap)
   (mask, block_diff_dc, block_diff_ac, xmul_, &diffmap);
   return true;
 }
 
 double ButteraugliScoreFromDiffmap(const ImageF& diffmap,
                                    const ButteraugliParams* params) {
   float retval = 0.0f;
   for (size_t y = 0; y < diffmap.ysize(); ++y) {
     const float* BUTTERAUGLI_RESTRICT row = diffmap.ConstRow(y);
     for (size_t x = 0; x < diffmap.xsize(); ++x) {
       retval = std::max(retval, row[x]);
     }
   }
   return retval;
 }
 
 Status ButteraugliDiffmap(const Image3F& rgb0, const Image3F& rgb1,
                           double hf_asymmetry, double xmul, ImageF& diffmap) {
   ButteraugliParams params;
   params.hf_asymmetry = hf_asymmetry;
   params.xmul = xmul;
   return ButteraugliDiffmap(rgb0, rgb1, params, diffmap);
 }
 
 template <size_t kMax>
 bool ButteraugliDiffmapSmall(const Image3F& rgb0, const Image3F& rgb1,
                              const ButteraugliParams& params, ImageF& diffmap) {
   const size_t xsize = rgb0.xsize();
   const size_t ysize = rgb0.ysize();
   // Butteraugli values for small (where xsize or ysize is smaller
   // than 8 pixels) images are non-sensical, but most likely it is
   // less disruptive to try to compute something than just give up.
   // Temporarily extend the borders of the image to fit 8 x 8 size.
   size_t xborder = xsize < kMax ? (kMax - xsize) / 2 : 0;
   size_t yborder = ysize < kMax ? (kMax - ysize) / 2 : 0;
   size_t xscaled = std::max<size_t>(kMax, xsize);
   size_t yscaled = std::max<size_t>(kMax, ysize);
   JXL_ASSIGN_OR_RETURN(Image3F scaled0, Image3F::Create(xscaled, yscaled));
   JXL_ASSIGN_OR_RETURN(Image3F scaled1, Image3F::Create(xscaled, yscaled));
   for (int i = 0; i < 3; ++i) {
     for (size_t y = 0; y < yscaled; ++y) {
       for (size_t x = 0; x < xscaled; ++x) {
         size_t x2 = std::min<size_t>(xsize - 1, x > xborder ? x - xborder : 0);
         size_t y2 = std::min<size_t>(ysize - 1, y > yborder ? y - yborder : 0);
         scaled0.PlaneRow(i, y)[x] = rgb0.PlaneRow(i, y2)[x2];
         scaled1.PlaneRow(i, y)[x] = rgb1.PlaneRow(i, y2)[x2];
       }
     }
   }
   ImageF diffmap_scaled;
   const bool ok = ButteraugliDiffmap(scaled0, scaled1, params, diffmap_scaled);
   JXL_ASSIGN_OR_RETURN(diffmap, ImageF::Create(xsize, ysize));
   for (size_t y = 0; y < ysize; ++y) {
     for (size_t x = 0; x < xsize; ++x) {
       diffmap.Row(y)[x] = diffmap_scaled.Row(y + yborder)[x + xborder];
     }
   }
   return ok;
 }
 
 Status ButteraugliDiffmap(const Image3F& rgb0, const Image3F& rgb1,
                           const ButteraugliParams& params, ImageF& diffmap) {
   const size_t xsize = rgb0.xsize();
   const size_t ysize = rgb0.ysize();
   if (xsize < 1 || ysize < 1) {
     return JXL_FAILURE("Zero-sized image");
   }
   if (!SameSize(rgb0, rgb1)) {
     return JXL_FAILURE("Size mismatch");
   }
   static const int kMax = 8;
   if (xsize < kMax || ysize < kMax) {
     return ButteraugliDiffmapSmall<kMax>(rgb0, rgb1, params, diffmap);
   }
   JXL_ASSIGN_OR_RETURN(std::unique_ptr<ButteraugliComparator> butteraugli,
                        ButteraugliComparator::Make(rgb0, params));
   JXL_RETURN_IF_ERROR(butteraugli->Diffmap(rgb1, diffmap));
   return true;
 }
 
 bool ButteraugliInterface(const Image3F& rgb0, const Image3F& rgb1,
                           float hf_asymmetry, float xmul, ImageF& diffmap,
                           double& diffvalue) {
   ButteraugliParams params;
   params.hf_asymmetry = hf_asymmetry;
   params.xmul = xmul;
   return ButteraugliInterface(rgb0, rgb1, params, diffmap, diffvalue);
 }
 
 bool ButteraugliInterface(const Image3F& rgb0, const Image3F& rgb1,
                           const ButteraugliParams& params, ImageF& diffmap,
                           double& diffvalue) {
   if (!ButteraugliDiffmap(rgb0, rgb1, params, diffmap)) {
     return false;
   }
   diffvalue = ButteraugliScoreFromDiffmap(diffmap, &params);
   return true;
 }
 
 Status ButteraugliInterfaceInPlace(Image3F&& rgb0, Image3F&& rgb1,
                                    const ButteraugliParams& params,
                                    ImageF& diffmap, double& diffvalue) {
   const size_t xsize = rgb0.xsize();
   const size_t ysize = rgb0.ysize();
   if (xsize < 1 || ysize < 1) {
     return JXL_FAILURE("Zero-sized image");
   }
   if (!SameSize(rgb0, rgb1)) {
     return JXL_FAILURE("Size mismatch");
   }
   static const int kMax = 8;
   if (xsize < kMax || ysize < kMax) {
     bool ok = ButteraugliDiffmapSmall<kMax>(rgb0, rgb1, params, diffmap);
     diffvalue = ButteraugliScoreFromDiffmap(diffmap, &params);
     return ok;
   }
   ImageF subdiffmap;
   if (xsize >= 15 && ysize >= 15) {
     JXL_ASSIGN_OR_RETURN(Image3F rgb0_sub, SubSample2x(rgb0));
     JXL_ASSIGN_OR_RETURN(Image3F rgb1_sub, SubSample2x(rgb1));
     JXL_RETURN_IF_ERROR(HWY_DYNAMIC_DISPATCH(ButteraugliDiffmapInPlace)(
         rgb0_sub, rgb1_sub, params, subdiffmap));
   }
   JXL_RETURN_IF_ERROR(HWY_DYNAMIC_DISPATCH(ButteraugliDiffmapInPlace)(
       rgb0, rgb1, params, diffmap));
   if (xsize >= 15 && ysize >= 15) {
     AddSupersampled2x(subdiffmap, 0.5, diffmap);
   }
   diffvalue = ButteraugliScoreFromDiffmap(diffmap, &params);
   return true;
 }
 
 double ButteraugliFuzzyClass(double score) {
   static const double fuzzy_width_up = 4.8;
   static const double fuzzy_width_down = 4.8;
   static const double m0 = 2.0;
   static const double scaler = 0.7777;
   double val;
   if (score < 1.0) {
     // val in [scaler .. 2.0]
     val = m0 / (1.0 + exp((score - 1.0) * fuzzy_width_down));
     val -= 1.0;           // from [1 .. 2] to [0 .. 1]
     val *= 2.0 - scaler;  // from [0 .. 1] to [0 .. 2.0 - scaler]
     val += scaler;        // from [0 .. 2.0 - scaler] to [scaler .. 2.0]
   } else {
     // val in [0 .. scaler]
     val = m0 / (1.0 + exp((score - 1.0) * fuzzy_width_up));
     val *= scaler;
   }
   return val;
 }
 
 // #define PRINT_OUT_NORMALIZATION
 
 double ButteraugliFuzzyInverse(double seek) {
   double pos = 0;
   // NOLINTNEXTLINE(clang-analyzer-security.FloatLoopCounter)
   for (double range = 1.0; range >= 1e-10; range *= 0.5) {
     double cur = ButteraugliFuzzyClass(pos);
     if (cur < seek) {
       pos -= range;
     } else {
       pos += range;
     }
   }
 #ifdef PRINT_OUT_NORMALIZATION
   if (seek == 1.0) {
     fprintf(stderr, "Fuzzy inverse %g\n", pos);
   }
 #endif
   return pos;
 }
 
 #ifdef PRINT_OUT_NORMALIZATION
 static double print_out_normalization = ButteraugliFuzzyInverse(1.0);
 #endif
 
 namespace {
 
 void ScoreToRgb(double score, double good_threshold, double bad_threshold,
                 float rgb[3]) {
   double heatmap[12][3] = {
       {0, 0, 0},       {0, 0, 1},
       {0, 1, 1},       {0, 1, 0},  // Good level
       {1, 1, 0},       {1, 0, 0},  // Bad level
       {1, 0, 1},       {0.5, 0.5, 1.0},
       {1.0, 0.5, 0.5},  // Pastel colors for the very bad quality range.
       {1.0, 1.0, 0.5}, {1, 1, 1},
       {1, 1, 1},  // Last color repeated to have a solid range of white.
   };
   if (score < good_threshold) {
     score = (score / good_threshold) * 0.3;
   } else if (score < bad_threshold) {
     score = 0.3 +
             (score - good_threshold) / (bad_threshold - good_threshold) * 0.15;
   } else {
     score = 0.45 + (score - bad_threshold) / (bad_threshold * 12) * 0.5;
   }
   static const int kTableSize = sizeof(heatmap) / sizeof(heatmap[0]);
   score = std::min<double>(std::max<double>(score * (kTableSize - 1), 0.0),
                            kTableSize - 2);
   int ix = static_cast<int>(score);
   ix = std::min(std::max(0, ix), kTableSize - 2);  // Handle NaN
   double mix = score - ix;
   for (int i = 0; i < 3; ++i) {
     double v = mix * heatmap[ix + 1][i] + (1 - mix) * heatmap[ix][i];
     rgb[i] = pow(v, 0.5);
   }
 }
 
 }  // namespace
 
 StatusOr<Image3F> CreateHeatMapImage(const ImageF& distmap,
                                      double good_threshold,
                                      double bad_threshold) {
   JXL_ASSIGN_OR_RETURN(Image3F heatmap,
                        Image3F::Create(distmap.xsize(), distmap.ysize()));
   for (size_t y = 0; y < distmap.ysize(); ++y) {
     const float* BUTTERAUGLI_RESTRICT row_distmap = distmap.ConstRow(y);
     float* BUTTERAUGLI_RESTRICT row_h0 = heatmap.PlaneRow(0, y);
     float* BUTTERAUGLI_RESTRICT row_h1 = heatmap.PlaneRow(1, y);
     float* BUTTERAUGLI_RESTRICT row_h2 = heatmap.PlaneRow(2, y);
     for (size_t x = 0; x < distmap.xsize(); ++x) {
       const float d = row_distmap[x];
       float rgb[3];
       ScoreToRgb(d, good_threshold, bad_threshold, rgb);
       row_h0[x] = rgb[0];
       row_h1[x] = rgb[1];
       row_h2[x] = rgb[2];
     }
   }
   return heatmap;
 }
 
 }  // namespace jxl
 #endif  // HWY_ONCE