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// 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
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