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author | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-04-07 17:32:43 +0000 |
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committer | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-04-07 17:32:43 +0000 |
commit | 6bf0a5cb5034a7e684dcc3500e841785237ce2dd (patch) | |
tree | a68f146d7fa01f0134297619fbe7e33db084e0aa /third_party/jpeg-xl/lib/jxl/enc_butteraugli_pnorm.cc | |
parent | Initial commit. (diff) | |
download | thunderbird-6bf0a5cb5034a7e684dcc3500e841785237ce2dd.tar.xz thunderbird-6bf0a5cb5034a7e684dcc3500e841785237ce2dd.zip |
Adding upstream version 1:115.7.0.upstream/1%115.7.0upstream
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
Diffstat (limited to '')
-rw-r--r-- | third_party/jpeg-xl/lib/jxl/enc_butteraugli_pnorm.cc | 211 |
1 files changed, 211 insertions, 0 deletions
diff --git a/third_party/jpeg-xl/lib/jxl/enc_butteraugli_pnorm.cc b/third_party/jpeg-xl/lib/jxl/enc_butteraugli_pnorm.cc new file mode 100644 index 0000000000..fe5629dcda --- /dev/null +++ b/third_party/jpeg-xl/lib/jxl/enc_butteraugli_pnorm.cc @@ -0,0 +1,211 @@ +// 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/enc_butteraugli_pnorm.h" + +#include <math.h> +#include <stdlib.h> + +#include <atomic> + +#undef HWY_TARGET_INCLUDE +#define HWY_TARGET_INCLUDE "lib/jxl/enc_butteraugli_pnorm.cc" +#include <hwy/foreach_target.h> +#include <hwy/highway.h> + +#include "lib/jxl/base/compiler_specific.h" +#include "lib/jxl/base/profiler.h" +#include "lib/jxl/base/status.h" +#include "lib/jxl/color_encoding_internal.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::GetLane; +using hwy::HWY_NAMESPACE::Mul; +using hwy::HWY_NAMESPACE::Rebind; + +double ComputeDistanceP(const ImageF& distmap, const ButteraugliParams& params, + double p) { + PROFILER_FUNC; + + const double onePerPixels = 1.0 / (distmap.ysize() * distmap.xsize()); + if (std::abs(p - 3.0) < 1E-6) { + double sum1[3] = {0.0}; + +// Prefer double if possible, but otherwise use float rather than scalar. +#if HWY_CAP_FLOAT64 + using T = double; + const Rebind<float, HWY_FULL(double)> df; +#else + using T = float; +#endif + const HWY_FULL(T) d; + constexpr size_t N = MaxLanes(HWY_FULL(T)()); + // Manually aligned storage to avoid asan crash on clang-7 due to + // unaligned spill. + HWY_ALIGN T sum_totals0[N] = {0}; + HWY_ALIGN T sum_totals1[N] = {0}; + HWY_ALIGN T sum_totals2[N] = {0}; + + for (size_t y = 0; y < distmap.ysize(); ++y) { + const float* JXL_RESTRICT row = distmap.ConstRow(y); + + auto sums0 = Zero(d); + auto sums1 = Zero(d); + auto sums2 = Zero(d); + + size_t x = 0; + for (; x + Lanes(d) <= distmap.xsize(); x += Lanes(d)) { +#if HWY_CAP_FLOAT64 + const auto d1 = PromoteTo(d, Load(df, row + x)); +#else + const auto d1 = Load(d, row + x); +#endif + const auto d2 = Mul(d1, Mul(d1, d1)); + sums0 = Add(sums0, d2); + const auto d3 = Mul(d2, d2); + sums1 = Add(sums1, d3); + const auto d4 = Mul(d3, d3); + sums2 = Add(sums2, d4); + } + + Store(Add(sums0, Load(d, sum_totals0)), d, sum_totals0); + Store(Add(sums1, Load(d, sum_totals1)), d, sum_totals1); + Store(Add(sums2, Load(d, sum_totals2)), d, sum_totals2); + + for (; x < distmap.xsize(); ++x) { + const double d1 = row[x]; + double d2 = d1 * d1 * d1; + sum1[0] += d2; + d2 *= d2; + sum1[1] += d2; + d2 *= d2; + sum1[2] += d2; + } + } + double v = 0; + v += pow( + onePerPixels * (sum1[0] + GetLane(SumOfLanes(d, Load(d, sum_totals0)))), + 1.0 / (p * 1.0)); + v += pow( + onePerPixels * (sum1[1] + GetLane(SumOfLanes(d, Load(d, sum_totals1)))), + 1.0 / (p * 2.0)); + v += pow( + onePerPixels * (sum1[2] + GetLane(SumOfLanes(d, Load(d, sum_totals2)))), + 1.0 / (p * 4.0)); + v /= 3.0; + return v; + } else { + static std::atomic<int> once{0}; + if (once.fetch_add(1, std::memory_order_relaxed) == 0) { + JXL_WARNING("WARNING: using slow ComputeDistanceP"); + } + double sum1[3] = {0.0}; + for (size_t y = 0; y < distmap.ysize(); ++y) { + const float* JXL_RESTRICT row = distmap.ConstRow(y); + for (size_t x = 0; x < distmap.xsize(); ++x) { + double d2 = std::pow(row[x], p); + sum1[0] += d2; + d2 *= d2; + sum1[1] += d2; + d2 *= d2; + sum1[2] += d2; + } + } + double v = 0; + for (int i = 0; i < 3; ++i) { + v += pow(onePerPixels * (sum1[i]), 1.0 / (p * (1 << i))); + } + v /= 3.0; + return v; + } +} + +// TODO(lode): take alpha into account when needed +double ComputeDistance2(const ImageBundle& ib1, const ImageBundle& ib2, + const JxlCmsInterface& cms) { + PROFILER_FUNC; + // Convert to sRGB - closer to perception than linear. + const Image3F* srgb1 = &ib1.color(); + Image3F copy1; + if (!ib1.IsSRGB()) { + JXL_CHECK( + ib1.CopyTo(Rect(ib1), ColorEncoding::SRGB(ib1.IsGray()), cms, ©1)); + srgb1 = ©1; + } + const Image3F* srgb2 = &ib2.color(); + Image3F copy2; + if (!ib2.IsSRGB()) { + JXL_CHECK( + ib2.CopyTo(Rect(ib2), ColorEncoding::SRGB(ib2.IsGray()), cms, ©2)); + srgb2 = ©2; + } + + JXL_CHECK(SameSize(*srgb1, *srgb2)); + + // TODO(veluca): SIMD. + float yuvmatrix[3][3] = {{0.299, 0.587, 0.114}, + {-0.14713, -0.28886, 0.436}, + {0.615, -0.51499, -0.10001}}; + double sum_of_squares[3] = {}; + for (size_t y = 0; y < srgb1->ysize(); ++y) { + const float* JXL_RESTRICT row1[3]; + const float* JXL_RESTRICT row2[3]; + for (size_t j = 0; j < 3; j++) { + row1[j] = srgb1->ConstPlaneRow(j, y); + row2[j] = srgb2->ConstPlaneRow(j, y); + } + for (size_t x = 0; x < srgb1->xsize(); ++x) { + float cdiff[3] = {}; + // YUV conversion is linear, so we can run it on the difference. + for (size_t j = 0; j < 3; j++) { + cdiff[j] = row1[j][x] - row2[j][x]; + } + float yuvdiff[3] = {}; + for (size_t j = 0; j < 3; j++) { + for (size_t k = 0; k < 3; k++) { + yuvdiff[j] += yuvmatrix[j][k] * cdiff[k]; + } + } + for (size_t j = 0; j < 3; j++) { + sum_of_squares[j] += yuvdiff[j] * yuvdiff[j]; + } + } + } + // Weighted PSNR as in JPEG-XL: chroma counts 1/8. + const float weights[3] = {6.0f / 8, 1.0f / 8, 1.0f / 8}; + // Avoid squaring the weight - 1/64 is too extreme. + double norm = 0; + for (size_t i = 0; i < 3; i++) { + norm += std::sqrt(sum_of_squares[i]) * weights[i]; + } + // This function returns distance *squared*. + return norm * norm; +} + +// NOLINTNEXTLINE(google-readability-namespace-comments) +} // namespace HWY_NAMESPACE +} // namespace jxl +HWY_AFTER_NAMESPACE(); + +#if HWY_ONCE +namespace jxl { +HWY_EXPORT(ComputeDistanceP); +double ComputeDistanceP(const ImageF& distmap, const ButteraugliParams& params, + double p) { + return HWY_DYNAMIC_DISPATCH(ComputeDistanceP)(distmap, params, p); +} + +HWY_EXPORT(ComputeDistance2); +double ComputeDistance2(const ImageBundle& ib1, const ImageBundle& ib2, + const JxlCmsInterface& cms) { + return HWY_DYNAMIC_DISPATCH(ComputeDistance2)(ib1, ib2, cms); +} + +} // namespace jxl +#endif |