summaryrefslogtreecommitdiffstats
path: root/third_party/jpeg-xl/lib/jxl/convolve_separable5.cc
diff options
context:
space:
mode:
Diffstat (limited to 'third_party/jpeg-xl/lib/jxl/convolve_separable5.cc')
-rw-r--r--third_party/jpeg-xl/lib/jxl/convolve_separable5.cc261
1 files changed, 261 insertions, 0 deletions
diff --git a/third_party/jpeg-xl/lib/jxl/convolve_separable5.cc b/third_party/jpeg-xl/lib/jxl/convolve_separable5.cc
new file mode 100644
index 0000000000..b26ff54bbc
--- /dev/null
+++ b/third_party/jpeg-xl/lib/jxl/convolve_separable5.cc
@@ -0,0 +1,261 @@
+// 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, 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()) {
+ return Conv::Run(in, rect, weights, pool, out);
+ }
+
+ return SlowSeparable5(in, rect, weights, pool, 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) {
+ return HWY_DYNAMIC_DISPATCH(Separable5)(in, rect, weights, pool, out);
+}
+
+} // namespace jxl
+#endif // HWY_ONCE