libjxl

convolve_separable5.cc (9572B)

---

 // 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.
 
 #include "lib/jxl/convolve.h"
 
 #undef HWY_TARGET_INCLUDE
 #define HWY_TARGET_INCLUDE "lib/jxl/convolve_separable5.cc"
 #include <hwy/foreach_target.h>
 #include <hwy/highway.h>
 
 #include "lib/jxl/convolve-inl.h"
 
 HWY_BEFORE_NAMESPACE();
 namespace jxl {
 namespace HWY_NAMESPACE {
 
 // These templates are not found via ADL.
 using hwy::HWY_NAMESPACE::Add;
 using hwy::HWY_NAMESPACE::Mul;
 using hwy::HWY_NAMESPACE::MulAdd;
 using hwy::HWY_NAMESPACE::Vec;
 
 // 5x5 convolution by separable kernel with a single scan through the input.
 // This is more cache-efficient than separate horizontal/vertical passes, and
 // possibly faster (given enough registers) than tiling and/or transposing.
 //
 // Overview: imagine a 5x5 window around a central pixel. First convolve the
 // rows by multiplying the pixels with the corresponding weights from
 // WeightsSeparable5.horz[abs(x_offset) * 4]. Then multiply each of these
 // intermediate results by the corresponding vertical weight, i.e.
 // vert[abs(y_offset) * 4]. Finally, store the sum of these values as the
 // convolution result at the position of the central pixel in the output.
 //
 // Each of these operations uses SIMD vectors. The central pixel and most
 // importantly the output are aligned, so neighnoring pixels (e.g. x_offset=1)
 // require unaligned loads. Because weights are supplied in identical groups of
 // 4, we can use LoadDup128 to load them (slightly faster).
 //
 // Uses mirrored boundary handling. Until x >= kRadius, the horizontal
 // convolution uses Neighbors class to shuffle vectors as if each of its lanes
 // had been loaded from the mirrored offset. Similarly, the last full vector to
 // write uses mirroring. In the case of scalar vectors, Neighbors is not usable
 // and the value is loaded directly. Otherwise, the number of valid pixels
 // modulo the vector size enables a small optimization: for smaller offsets,
 // a non-mirrored load is sufficient.
 class Separable5Strategy {
   using D = HWY_CAPPED(float, 16);
   using V = Vec<D>;
 
  public:
   static constexpr int64_t kRadius = 2;
 
   template <size_t kSizeModN, class WrapRow>
   static JXL_MAYBE_INLINE void ConvolveRow(
       const float* const JXL_RESTRICT row_m, const size_t xsize,
       const int64_t stride, const WrapRow& wrap_row,
       const WeightsSeparable5& weights, float* const JXL_RESTRICT row_out) {
     const D d;
     const int64_t neg_stride = -stride;  // allows LEA addressing.
     const float* const JXL_RESTRICT row_t2 =
         wrap_row(row_m + 2 * neg_stride, stride);
     const float* const JXL_RESTRICT row_t1 =
         wrap_row(row_m + 1 * neg_stride, stride);
     const float* const JXL_RESTRICT row_b1 =
         wrap_row(row_m + 1 * stride, stride);
     const float* const JXL_RESTRICT row_b2 =
         wrap_row(row_m + 2 * stride, stride);
 
     const V wh0 = LoadDup128(d, weights.horz + 0 * 4);
     const V wh1 = LoadDup128(d, weights.horz + 1 * 4);
     const V wh2 = LoadDup128(d, weights.horz + 2 * 4);
     const V wv0 = LoadDup128(d, weights.vert + 0 * 4);
     const V wv1 = LoadDup128(d, weights.vert + 1 * 4);
     const V wv2 = LoadDup128(d, weights.vert + 2 * 4);
 
     size_t x = 0;
 
     // More than one iteration for scalars.
     for (; x < kRadius; x += Lanes(d)) {
       const V conv0 =
           Mul(HorzConvolveFirst(row_m, x, xsize, wh0, wh1, wh2), wv0);
 
       const V conv1t = HorzConvolveFirst(row_t1, x, xsize, wh0, wh1, wh2);
       const V conv1b = HorzConvolveFirst(row_b1, x, xsize, wh0, wh1, wh2);
       const V conv1 = MulAdd(Add(conv1t, conv1b), wv1, conv0);
 
       const V conv2t = HorzConvolveFirst(row_t2, x, xsize, wh0, wh1, wh2);
       const V conv2b = HorzConvolveFirst(row_b2, x, xsize, wh0, wh1, wh2);
       const V conv2 = MulAdd(Add(conv2t, conv2b), wv2, conv1);
       Store(conv2, d, row_out + x);
     }
 
     // Main loop: load inputs without padding
     for (; x + Lanes(d) + kRadius <= xsize; x += Lanes(d)) {
       const V conv0 = Mul(HorzConvolve(row_m + x, wh0, wh1, wh2), wv0);
 
       const V conv1t = HorzConvolve(row_t1 + x, wh0, wh1, wh2);
       const V conv1b = HorzConvolve(row_b1 + x, wh0, wh1, wh2);
       const V conv1 = MulAdd(Add(conv1t, conv1b), wv1, conv0);
 
       const V conv2t = HorzConvolve(row_t2 + x, wh0, wh1, wh2);
       const V conv2b = HorzConvolve(row_b2 + x, wh0, wh1, wh2);
       const V conv2 = MulAdd(Add(conv2t, conv2b), wv2, conv1);
       Store(conv2, d, row_out + x);
     }
 
     // Last full vector to write (the above loop handled mod >= kRadius)
 #if HWY_TARGET == HWY_SCALAR
     while (x < xsize) {
 #else
     if (kSizeModN < kRadius) {
 #endif
       const V conv0 =
           Mul(HorzConvolveLast<kSizeModN>(row_m, x, xsize, wh0, wh1, wh2), wv0);
 
       const V conv1t =
           HorzConvolveLast<kSizeModN>(row_t1, x, xsize, wh0, wh1, wh2);
       const V conv1b =
           HorzConvolveLast<kSizeModN>(row_b1, x, xsize, wh0, wh1, wh2);
       const V conv1 = MulAdd(Add(conv1t, conv1b), wv1, conv0);
 
       const V conv2t =
           HorzConvolveLast<kSizeModN>(row_t2, x, xsize, wh0, wh1, wh2);
       const V conv2b =
           HorzConvolveLast<kSizeModN>(row_b2, x, xsize, wh0, wh1, wh2);
       const V conv2 = MulAdd(Add(conv2t, conv2b), wv2, conv1);
       Store(conv2, d, row_out + x);
       x += Lanes(d);
     }
 
     // If mod = 0, the above vector was the last.
     if (kSizeModN != 0) {
       for (; x < xsize; ++x) {
         float mul = 0.0f;
         for (int64_t dy = -kRadius; dy <= kRadius; ++dy) {
           const float wy = weights.vert[std::abs(dy) * 4];
           const float* clamped_row = wrap_row(row_m + dy * stride, stride);
           for (int64_t dx = -kRadius; dx <= kRadius; ++dx) {
             const float wx = weights.horz[std::abs(dx) * 4];
             const int64_t clamped_x = Mirror(x + dx, xsize);
             mul += clamped_row[clamped_x] * wx * wy;
           }
         }
         row_out[x] = mul;
       }
     }
   }
 
  private:
   // Same as HorzConvolve for the first/last vector in a row.
   static JXL_MAYBE_INLINE V HorzConvolveFirst(
       const float* const JXL_RESTRICT row, const int64_t x, const int64_t xsize,
       const V wh0, const V wh1, const V wh2) {
     const D d;
     const V c = LoadU(d, row + x);
     const V mul0 = Mul(c, wh0);
 
 #if HWY_TARGET == HWY_SCALAR
     const V l1 = LoadU(d, row + Mirror(x - 1, xsize));
     const V l2 = LoadU(d, row + Mirror(x - 2, xsize));
 #else
     (void)xsize;
     const V l1 = Neighbors::FirstL1(c);
     const V l2 = Neighbors::FirstL2(c);
 #endif
 
     const V r1 = LoadU(d, row + x + 1);
     const V r2 = LoadU(d, row + x + 2);
 
     const V mul1 = MulAdd(Add(l1, r1), wh1, mul0);
     const V mul2 = MulAdd(Add(l2, r2), wh2, mul1);
     return mul2;
   }
 
   template <size_t kSizeModN>
   static JXL_MAYBE_INLINE V
   HorzConvolveLast(const float* const JXL_RESTRICT row, const int64_t x,
                    const int64_t xsize, const V wh0, const V wh1, const V wh2) {
     const D d;
     const V c = LoadU(d, row + x);
     const V mul0 = Mul(c, wh0);
 
     const V l1 = LoadU(d, row + x - 1);
     const V l2 = LoadU(d, row + x - 2);
 
     V r1;
     V r2;
 #if HWY_TARGET == HWY_SCALAR
     r1 = LoadU(d, row + Mirror(x + 1, xsize));
     r2 = LoadU(d, row + Mirror(x + 2, xsize));
 #else
     const size_t N = Lanes(d);
     if (kSizeModN == 0) {
       r2 = TableLookupLanes(c, SetTableIndices(d, MirrorLanes(N - 2)));
       r1 = TableLookupLanes(c, SetTableIndices(d, MirrorLanes(N - 1)));
     } else {  // == 1
       const auto last = LoadU(d, row + xsize - N);
       r2 = TableLookupLanes(last, SetTableIndices(d, MirrorLanes(N - 1)));
       r1 = last;
     }
 #endif
 
     // Sum of pixels with Manhattan distance i, multiplied by weights[i].
     const V sum1 = Add(l1, r1);
     const V mul1 = MulAdd(sum1, wh1, mul0);
     const V sum2 = Add(l2, r2);
     const V mul2 = MulAdd(sum2, wh2, mul1);
     return mul2;
   }
 
   // Requires kRadius valid pixels before/after pos.
   static JXL_MAYBE_INLINE V HorzConvolve(const float* const JXL_RESTRICT pos,
                                          const V wh0, const V wh1,
                                          const V wh2) {
     const D d;
     const V c = LoadU(d, pos);
     const V mul0 = Mul(c, wh0);
 
     // Loading anew is faster than combining vectors.
     const V l1 = LoadU(d, pos - 1);
     const V r1 = LoadU(d, pos + 1);
     const V l2 = LoadU(d, pos - 2);
     const V r2 = LoadU(d, pos + 2);
     // Sum of pixels with Manhattan distance i, multiplied by weights[i].
     const V sum1 = Add(l1, r1);
     const V mul1 = MulAdd(sum1, wh1, mul0);
     const V sum2 = Add(l2, r2);
     const V mul2 = MulAdd(sum2, wh2, mul1);
     return mul2;
   }
 };
 
 void Separable5(const ImageF& in, const Rect& rect,
                 const WeightsSeparable5& weights, ThreadPool* pool,
                 ImageF* out) {
   using Conv = ConvolveT<Separable5Strategy>;
   if (rect.xsize() >= Conv::MinWidth()) {
     Conv::Run(in, rect, weights, pool, out);
     return;
   }
 
   SlowSeparable5(in, rect, weights, pool, out, Rect(*out));
 }
 
 // NOLINTNEXTLINE(google-readability-namespace-comments)
 }  // namespace HWY_NAMESPACE
 }  // namespace jxl
 HWY_AFTER_NAMESPACE();
 
 #if HWY_ONCE
 namespace jxl {
 
 HWY_EXPORT(Separable5);
 void Separable5(const ImageF& in, const Rect& rect,
                 const WeightsSeparable5& weights, ThreadPool* pool,
                 ImageF* out) {
   HWY_DYNAMIC_DISPATCH(Separable5)(in, rect, weights, pool, out);
 }
 
 }  // namespace jxl
 #endif  // HWY_ONCE