// 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 #include #include #undef HWY_TARGET_INCLUDE #define HWY_TARGET_INCLUDE "lib/jxl/enc_butteraugli_pnorm.cc" #include #include #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::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 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 = d1 * d1 * d1; sums0 += d2; const auto d3 = d2 * d2; sums1 += d3; const auto d4 = d3 * d3; sums2 += d4; } Store(sums0 + Load(d, sum_totals0), d, sum_totals0); Store(sums1 + Load(d, sum_totals1), d, sum_totals1); Store(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 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