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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/quant_weights_test.cc | |
parent | Initial commit. (diff) | |
download | thunderbird-upstream.tar.xz thunderbird-upstream.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 'third_party/jpeg-xl/lib/jxl/quant_weights_test.cc')
-rw-r--r-- | third_party/jpeg-xl/lib/jxl/quant_weights_test.cc | 240 |
1 files changed, 240 insertions, 0 deletions
diff --git a/third_party/jpeg-xl/lib/jxl/quant_weights_test.cc b/third_party/jpeg-xl/lib/jxl/quant_weights_test.cc new file mode 100644 index 0000000000..f0497948a7 --- /dev/null +++ b/third_party/jpeg-xl/lib/jxl/quant_weights_test.cc @@ -0,0 +1,240 @@ +// 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/quant_weights.h" + +#include <stdlib.h> + +#include <algorithm> +#include <cmath> +#include <hwy/base.h> // HWY_ALIGN_MAX +#include <hwy/tests/test_util-inl.h> +#include <numeric> + +#include "lib/jxl/base/random.h" +#include "lib/jxl/dct_for_test.h" +#include "lib/jxl/dec_transforms_testonly.h" +#include "lib/jxl/enc_modular.h" +#include "lib/jxl/enc_quant_weights.h" +#include "lib/jxl/enc_transforms.h" + +namespace jxl { +namespace { + +template <typename T> +void CheckSimilar(T a, T b) { + EXPECT_EQ(a, b); +} +// minimum exponent = -15. +template <> +void CheckSimilar(float a, float b) { + float m = std::max(std::abs(a), std::abs(b)); + // 10 bits of precision are used in the format. Relative error should be + // below 2^-10. + EXPECT_LE(std::abs(a - b), m / 1024.0f) << "a: " << a << " b: " << b; +} + +TEST(QuantWeightsTest, DC) { + DequantMatrices mat; + float dc_quant[3] = {1e+5, 1e+3, 1e+1}; + DequantMatricesSetCustomDC(&mat, dc_quant); + for (size_t c = 0; c < 3; c++) { + CheckSimilar(mat.InvDCQuant(c), dc_quant[c]); + } +} + +void RoundtripMatrices(const std::vector<QuantEncoding>& encodings) { + ASSERT_TRUE(encodings.size() == DequantMatrices::kNum); + DequantMatrices mat; + CodecMetadata metadata; + FrameHeader frame_header(&metadata); + ModularFrameEncoder encoder(frame_header, CompressParams{}); + DequantMatricesSetCustom(&mat, encodings, &encoder); + const std::vector<QuantEncoding>& encodings_dec = mat.encodings(); + for (size_t i = 0; i < encodings.size(); i++) { + const QuantEncoding& e = encodings[i]; + const QuantEncoding& d = encodings_dec[i]; + // Check values roundtripped correctly. + EXPECT_EQ(e.mode, d.mode); + EXPECT_EQ(e.predefined, d.predefined); + EXPECT_EQ(e.source, d.source); + + EXPECT_EQ(static_cast<uint64_t>(e.dct_params.num_distance_bands), + static_cast<uint64_t>(d.dct_params.num_distance_bands)); + for (size_t c = 0; c < 3; c++) { + for (size_t j = 0; j < DctQuantWeightParams::kMaxDistanceBands; j++) { + CheckSimilar(e.dct_params.distance_bands[c][j], + d.dct_params.distance_bands[c][j]); + } + } + + if (e.mode == QuantEncoding::kQuantModeRAW) { + EXPECT_FALSE(!e.qraw.qtable); + EXPECT_FALSE(!d.qraw.qtable); + EXPECT_EQ(e.qraw.qtable->size(), d.qraw.qtable->size()); + for (size_t j = 0; j < e.qraw.qtable->size(); j++) { + EXPECT_EQ((*e.qraw.qtable)[j], (*d.qraw.qtable)[j]); + } + EXPECT_NEAR(e.qraw.qtable_den, d.qraw.qtable_den, 1e-7f); + } else { + // modes different than kQuantModeRAW use one of the other fields used + // here, which all happen to be arrays of floats. + for (size_t c = 0; c < 3; c++) { + for (size_t j = 0; j < 3; j++) { + CheckSimilar(e.idweights[c][j], d.idweights[c][j]); + } + for (size_t j = 0; j < 6; j++) { + CheckSimilar(e.dct2weights[c][j], d.dct2weights[c][j]); + } + for (size_t j = 0; j < 2; j++) { + CheckSimilar(e.dct4multipliers[c][j], d.dct4multipliers[c][j]); + } + CheckSimilar(e.dct4x8multipliers[c], d.dct4x8multipliers[c]); + for (size_t j = 0; j < 9; j++) { + CheckSimilar(e.afv_weights[c][j], d.afv_weights[c][j]); + } + for (size_t j = 0; j < DctQuantWeightParams::kMaxDistanceBands; j++) { + CheckSimilar(e.dct_params_afv_4x4.distance_bands[c][j], + d.dct_params_afv_4x4.distance_bands[c][j]); + } + } + } + } +} + +TEST(QuantWeightsTest, AllDefault) { + std::vector<QuantEncoding> encodings(DequantMatrices::kNum, + QuantEncoding::Library(0)); + RoundtripMatrices(encodings); +} + +void TestSingleQuantMatrix(DequantMatrices::QuantTable kind) { + std::vector<QuantEncoding> encodings(DequantMatrices::kNum, + QuantEncoding::Library(0)); + encodings[kind] = DequantMatrices::Library()[kind]; + RoundtripMatrices(encodings); +} + +// Ensure we can reasonably represent default quant tables. +TEST(QuantWeightsTest, DCT) { TestSingleQuantMatrix(DequantMatrices::DCT); } +TEST(QuantWeightsTest, IDENTITY) { + TestSingleQuantMatrix(DequantMatrices::IDENTITY); +} +TEST(QuantWeightsTest, DCT2X2) { + TestSingleQuantMatrix(DequantMatrices::DCT2X2); +} +TEST(QuantWeightsTest, DCT4X4) { + TestSingleQuantMatrix(DequantMatrices::DCT4X4); +} +TEST(QuantWeightsTest, DCT16X16) { + TestSingleQuantMatrix(DequantMatrices::DCT16X16); +} +TEST(QuantWeightsTest, DCT32X32) { + TestSingleQuantMatrix(DequantMatrices::DCT32X32); +} +TEST(QuantWeightsTest, DCT8X16) { + TestSingleQuantMatrix(DequantMatrices::DCT8X16); +} +TEST(QuantWeightsTest, DCT8X32) { + TestSingleQuantMatrix(DequantMatrices::DCT8X32); +} +TEST(QuantWeightsTest, DCT16X32) { + TestSingleQuantMatrix(DequantMatrices::DCT16X32); +} +TEST(QuantWeightsTest, DCT4X8) { + TestSingleQuantMatrix(DequantMatrices::DCT4X8); +} +TEST(QuantWeightsTest, AFV0) { TestSingleQuantMatrix(DequantMatrices::AFV0); } +TEST(QuantWeightsTest, RAW) { + std::vector<QuantEncoding> encodings(DequantMatrices::kNum, + QuantEncoding::Library(0)); + std::vector<int> matrix(3 * 32 * 32); + Rng rng(0); + for (size_t i = 0; i < matrix.size(); i++) matrix[i] = rng.UniformI(1, 256); + encodings[DequantMatrices::kQuantTable[AcStrategy::DCT32X32]] = + QuantEncoding::RAW(matrix, 2); + RoundtripMatrices(encodings); +} + +class QuantWeightsTargetTest : public hwy::TestWithParamTarget {}; +HWY_TARGET_INSTANTIATE_TEST_SUITE_P(QuantWeightsTargetTest); + +TEST_P(QuantWeightsTargetTest, DCTUniform) { + constexpr float kUniformQuant = 4; + float weights[3][2] = {{1.0f / kUniformQuant, 0}, + {1.0f / kUniformQuant, 0}, + {1.0f / kUniformQuant, 0}}; + DctQuantWeightParams dct_params(weights); + std::vector<QuantEncoding> encodings(DequantMatrices::kNum, + QuantEncoding::DCT(dct_params)); + DequantMatrices dequant_matrices; + CodecMetadata metadata; + FrameHeader frame_header(&metadata); + ModularFrameEncoder encoder(frame_header, CompressParams{}); + DequantMatricesSetCustom(&dequant_matrices, encodings, &encoder); + JXL_CHECK(dequant_matrices.EnsureComputed(~0u)); + + const float dc_quant[3] = {1.0f / kUniformQuant, 1.0f / kUniformQuant, + 1.0f / kUniformQuant}; + DequantMatricesSetCustomDC(&dequant_matrices, dc_quant); + + HWY_ALIGN_MAX float scratch_space[16 * 16 * 2]; + + // DCT8 + { + HWY_ALIGN_MAX float pixels[64]; + std::iota(std::begin(pixels), std::end(pixels), 0); + HWY_ALIGN_MAX float coeffs[64]; + const AcStrategy::Type dct = AcStrategy::DCT; + TransformFromPixels(dct, pixels, 8, coeffs, scratch_space); + HWY_ALIGN_MAX double slow_coeffs[64]; + for (size_t i = 0; i < 64; i++) slow_coeffs[i] = pixels[i]; + DCTSlow<8>(slow_coeffs); + + for (size_t i = 0; i < 64; i++) { + // DCTSlow doesn't multiply/divide by 1/N, so we do it manually. + slow_coeffs[i] = roundf(slow_coeffs[i] / kUniformQuant) * kUniformQuant; + coeffs[i] = roundf(coeffs[i] / dequant_matrices.Matrix(dct, 0)[i]) * + dequant_matrices.Matrix(dct, 0)[i]; + } + IDCTSlow<8>(slow_coeffs); + TransformToPixels(dct, coeffs, pixels, 8, scratch_space); + for (size_t i = 0; i < 64; i++) { + EXPECT_NEAR(pixels[i], slow_coeffs[i], 1e-4); + } + } + + // DCT16 + { + HWY_ALIGN_MAX float pixels[64 * 4]; + std::iota(std::begin(pixels), std::end(pixels), 0); + HWY_ALIGN_MAX float coeffs[64 * 4]; + const AcStrategy::Type dct = AcStrategy::DCT16X16; + TransformFromPixels(dct, pixels, 16, coeffs, scratch_space); + HWY_ALIGN_MAX double slow_coeffs[64 * 4]; + for (size_t i = 0; i < 64 * 4; i++) slow_coeffs[i] = pixels[i]; + DCTSlow<16>(slow_coeffs); + + for (size_t i = 0; i < 64 * 4; i++) { + slow_coeffs[i] = roundf(slow_coeffs[i] / kUniformQuant) * kUniformQuant; + coeffs[i] = roundf(coeffs[i] / dequant_matrices.Matrix(dct, 0)[i]) * + dequant_matrices.Matrix(dct, 0)[i]; + } + + IDCTSlow<16>(slow_coeffs); + TransformToPixels(dct, coeffs, pixels, 16, scratch_space); + for (size_t i = 0; i < 64 * 4; i++) { + EXPECT_NEAR(pixels[i], slow_coeffs[i], 1e-4); + } + } + + // Check that all matrices have the same DC quantization, i.e. that they all + // have the same scaling. + for (size_t i = 0; i < AcStrategy::kNumValidStrategies; i++) { + EXPECT_NEAR(dequant_matrices.Matrix(i, 0)[0], kUniformQuant, 1e-6); + } +} + +} // namespace +} // namespace jxl |