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Diffstat (limited to 'third_party/aom/test/noise_model_test.cc')
| -rw-r--r-- | third_party/aom/test/noise_model_test.cc | 1332 |
1 files changed, 1332 insertions, 0 deletions
diff --git a/third_party/aom/test/noise_model_test.cc b/third_party/aom/test/noise_model_test.cc new file mode 100644 index 000000000..9b7fff8a2 --- /dev/null +++ b/third_party/aom/test/noise_model_test.cc @@ -0,0 +1,1332 @@ +#include <math.h> +#include <algorithm> +#include <vector> + +#include "aom_dsp/noise_model.h" +#include "aom_dsp/noise_util.h" +#include "config/aom_dsp_rtcd.h" +#include "test/acm_random.h" +#include "third_party/googletest/src/googletest/include/gtest/gtest.h" + +namespace { + +// Return normally distrbuted values with standard deviation of sigma. +double randn(libaom_test::ACMRandom *random, double sigma) { + while (1) { + const double u = 2.0 * ((double)random->Rand31() / + testing::internal::Random::kMaxRange) - + 1.0; + const double v = 2.0 * ((double)random->Rand31() / + testing::internal::Random::kMaxRange) - + 1.0; + const double s = u * u + v * v; + if (s > 0 && s < 1) { + return sigma * (u * sqrt(-2.0 * log(s) / s)); + } + } + return 0; +} + +// Synthesizes noise using the auto-regressive filter of the given lag, +// with the provided n coefficients sampled at the given coords. +void noise_synth(libaom_test::ACMRandom *random, int lag, int n, + const int (*coords)[2], const double *coeffs, double *data, + int w, int h) { + const int pad_size = 3 * lag; + const int padded_w = w + pad_size; + const int padded_h = h + pad_size; + int x = 0, y = 0; + std::vector<double> padded(padded_w * padded_h); + + for (y = 0; y < padded_h; ++y) { + for (x = 0; x < padded_w; ++x) { + padded[y * padded_w + x] = randn(random, 1.0); + } + } + for (y = lag; y < padded_h; ++y) { + for (x = lag; x < padded_w; ++x) { + double sum = 0; + int i = 0; + for (i = 0; i < n; ++i) { + const int dx = coords[i][0]; + const int dy = coords[i][1]; + sum += padded[(y + dy) * padded_w + (x + dx)] * coeffs[i]; + } + padded[y * padded_w + x] += sum; + } + } + // Copy over the padded rows to the output + for (y = 0; y < h; ++y) { + memcpy(data + y * w, &padded[0] + y * padded_w, sizeof(*data) * w); + } +} + +std::vector<float> get_noise_psd(double *noise, int width, int height, + int block_size) { + float *block = + (float *)aom_memalign(32, block_size * block_size * sizeof(block)); + std::vector<float> psd(block_size * block_size); + int num_blocks = 0; + struct aom_noise_tx_t *tx = aom_noise_tx_malloc(block_size); + for (int y = 0; y <= height - block_size; y += block_size / 2) { + for (int x = 0; x <= width - block_size; x += block_size / 2) { + for (int yy = 0; yy < block_size; ++yy) { + for (int xx = 0; xx < block_size; ++xx) { + block[yy * block_size + xx] = (float)noise[(y + yy) * width + x + xx]; + } + } + aom_noise_tx_forward(tx, &block[0]); + aom_noise_tx_add_energy(tx, &psd[0]); + num_blocks++; + } + } + for (int yy = 0; yy < block_size; ++yy) { + for (int xx = 0; xx <= block_size / 2; ++xx) { + psd[yy * block_size + xx] /= num_blocks; + } + } + // Fill in the data that is missing due to symmetries + for (int xx = 1; xx < block_size / 2; ++xx) { + psd[(block_size - xx)] = psd[xx]; + } + for (int yy = 1; yy < block_size; ++yy) { + for (int xx = 1; xx < block_size / 2; ++xx) { + psd[(block_size - yy) * block_size + (block_size - xx)] = + psd[yy * block_size + xx]; + } + } + aom_noise_tx_free(tx); + aom_free(block); + return psd; +} + +} // namespace + +TEST(NoiseStrengthSolver, GetCentersTwoBins) { + aom_noise_strength_solver_t solver; + aom_noise_strength_solver_init(&solver, 2, 8); + EXPECT_NEAR(0, aom_noise_strength_solver_get_center(&solver, 0), 1e-5); + EXPECT_NEAR(255, aom_noise_strength_solver_get_center(&solver, 1), 1e-5); + aom_noise_strength_solver_free(&solver); +} + +TEST(NoiseStrengthSolver, GetCentersTwoBins10bit) { + aom_noise_strength_solver_t solver; + aom_noise_strength_solver_init(&solver, 2, 10); + EXPECT_NEAR(0, aom_noise_strength_solver_get_center(&solver, 0), 1e-5); + EXPECT_NEAR(1023, aom_noise_strength_solver_get_center(&solver, 1), 1e-5); + aom_noise_strength_solver_free(&solver); +} + +TEST(NoiseStrengthSolver, GetCenters256Bins) { + const int num_bins = 256; + aom_noise_strength_solver_t solver; + aom_noise_strength_solver_init(&solver, num_bins, 8); + + for (int i = 0; i < 256; ++i) { + EXPECT_NEAR(i, aom_noise_strength_solver_get_center(&solver, i), 1e-5); + } + aom_noise_strength_solver_free(&solver); +} + +// Tests that the noise strength solver returns the identity transform when +// given identity-like constraints. +TEST(NoiseStrengthSolver, ObserveIdentity) { + const int num_bins = 256; + aom_noise_strength_solver_t solver; + EXPECT_EQ(1, aom_noise_strength_solver_init(&solver, num_bins, 8)); + + // We have to add a big more strength to constraints at the boundary to + // overcome any regularization. + for (int j = 0; j < 5; ++j) { + aom_noise_strength_solver_add_measurement(&solver, 0, 0); + aom_noise_strength_solver_add_measurement(&solver, 255, 255); + } + for (int i = 0; i < 256; ++i) { + aom_noise_strength_solver_add_measurement(&solver, i, i); + } + EXPECT_EQ(1, aom_noise_strength_solver_solve(&solver)); + for (int i = 2; i < num_bins - 2; ++i) { + EXPECT_NEAR(i, solver.eqns.x[i], 0.1); + } + + aom_noise_strength_lut_t lut; + EXPECT_EQ(1, aom_noise_strength_solver_fit_piecewise(&solver, 2, &lut)); + + ASSERT_EQ(2, lut.num_points); + EXPECT_NEAR(0.0, lut.points[0][0], 1e-5); + EXPECT_NEAR(0.0, lut.points[0][1], 0.5); + EXPECT_NEAR(255.0, lut.points[1][0], 1e-5); + EXPECT_NEAR(255.0, lut.points[1][1], 0.5); + + aom_noise_strength_lut_free(&lut); + aom_noise_strength_solver_free(&solver); +} + +TEST(NoiseStrengthSolver, SimplifiesCurve) { + const int num_bins = 256; + aom_noise_strength_solver_t solver; + EXPECT_EQ(1, aom_noise_strength_solver_init(&solver, num_bins, 8)); + + // Create a parabolic input + for (int i = 0; i < 256; ++i) { + const double x = (i - 127.5) / 63.5; + aom_noise_strength_solver_add_measurement(&solver, i, x * x); + } + EXPECT_EQ(1, aom_noise_strength_solver_solve(&solver)); + + // First try to fit an unconstrained lut + aom_noise_strength_lut_t lut; + EXPECT_EQ(1, aom_noise_strength_solver_fit_piecewise(&solver, -1, &lut)); + ASSERT_LE(20, lut.num_points); + aom_noise_strength_lut_free(&lut); + + // Now constrain the maximum number of points + const int kMaxPoints = 9; + EXPECT_EQ(1, + aom_noise_strength_solver_fit_piecewise(&solver, kMaxPoints, &lut)); + ASSERT_EQ(kMaxPoints, lut.num_points); + + // Check that the input parabola is still well represented + EXPECT_NEAR(0.0, lut.points[0][0], 1e-5); + EXPECT_NEAR(4.0, lut.points[0][1], 0.1); + for (int i = 1; i < lut.num_points - 1; ++i) { + const double x = (lut.points[i][0] - 128.) / 64.; + EXPECT_NEAR(x * x, lut.points[i][1], 0.1); + } + EXPECT_NEAR(255.0, lut.points[kMaxPoints - 1][0], 1e-5); + + EXPECT_NEAR(4.0, lut.points[kMaxPoints - 1][1], 0.1); + aom_noise_strength_lut_free(&lut); + aom_noise_strength_solver_free(&solver); +} + +TEST(NoiseStrengthLut, LutEvalSinglePoint) { + aom_noise_strength_lut_t lut; + ASSERT_TRUE(aom_noise_strength_lut_init(&lut, 1)); + ASSERT_EQ(1, lut.num_points); + lut.points[0][0] = 0; + lut.points[0][1] = 1; + EXPECT_EQ(1, aom_noise_strength_lut_eval(&lut, -1)); + EXPECT_EQ(1, aom_noise_strength_lut_eval(&lut, 0)); + EXPECT_EQ(1, aom_noise_strength_lut_eval(&lut, 1)); + aom_noise_strength_lut_free(&lut); +} + +TEST(NoiseStrengthLut, LutEvalMultiPointInterp) { + const double kEps = 1e-5; + aom_noise_strength_lut_t lut; + ASSERT_TRUE(aom_noise_strength_lut_init(&lut, 4)); + ASSERT_EQ(4, lut.num_points); + + lut.points[0][0] = 0; + lut.points[0][1] = 0; + + lut.points[1][0] = 1; + lut.points[1][1] = 1; + + lut.points[2][0] = 2; + lut.points[2][1] = 1; + + lut.points[3][0] = 100; + lut.points[3][1] = 1001; + + // Test lower boundary + EXPECT_EQ(0, aom_noise_strength_lut_eval(&lut, -1)); + EXPECT_EQ(0, aom_noise_strength_lut_eval(&lut, 0)); + + // Test first part that should be identity + EXPECT_NEAR(0.25, aom_noise_strength_lut_eval(&lut, 0.25), kEps); + EXPECT_NEAR(0.75, aom_noise_strength_lut_eval(&lut, 0.75), kEps); + + // This is a constant section (should evaluate to 1) + EXPECT_NEAR(1.0, aom_noise_strength_lut_eval(&lut, 1.25), kEps); + EXPECT_NEAR(1.0, aom_noise_strength_lut_eval(&lut, 1.75), kEps); + + // Test interpolation between to non-zero y coords. + EXPECT_NEAR(1, aom_noise_strength_lut_eval(&lut, 2), kEps); + EXPECT_NEAR(251, aom_noise_strength_lut_eval(&lut, 26.5), kEps); + EXPECT_NEAR(751, aom_noise_strength_lut_eval(&lut, 75.5), kEps); + + // Test upper boundary + EXPECT_EQ(1001, aom_noise_strength_lut_eval(&lut, 100)); + EXPECT_EQ(1001, aom_noise_strength_lut_eval(&lut, 101)); + + aom_noise_strength_lut_free(&lut); +} + +TEST(NoiseModel, InitSuccessWithValidSquareShape) { + aom_noise_model_params_t params = { AOM_NOISE_SHAPE_SQUARE, 2, 8, 0 }; + aom_noise_model_t model; + + EXPECT_TRUE(aom_noise_model_init(&model, params)); + + const int kNumCoords = 12; + const int kCoords[][2] = { { -2, -2 }, { -1, -2 }, { 0, -2 }, { 1, -2 }, + { 2, -2 }, { -2, -1 }, { -1, -1 }, { 0, -1 }, + { 1, -1 }, { 2, -1 }, { -2, 0 }, { -1, 0 } }; + EXPECT_EQ(kNumCoords, model.n); + for (int i = 0; i < kNumCoords; ++i) { + const int *coord = kCoords[i]; + EXPECT_EQ(coord[0], model.coords[i][0]); + EXPECT_EQ(coord[1], model.coords[i][1]); + } + aom_noise_model_free(&model); +} + +TEST(NoiseModel, InitSuccessWithValidDiamondShape) { + aom_noise_model_t model; + aom_noise_model_params_t params = { AOM_NOISE_SHAPE_DIAMOND, 2, 8, 0 }; + EXPECT_TRUE(aom_noise_model_init(&model, params)); + EXPECT_EQ(6, model.n); + const int kNumCoords = 6; + const int kCoords[][2] = { { 0, -2 }, { -1, -1 }, { 0, -1 }, + { 1, -1 }, { -2, 0 }, { -1, 0 } }; + EXPECT_EQ(kNumCoords, model.n); + for (int i = 0; i < kNumCoords; ++i) { + const int *coord = kCoords[i]; + EXPECT_EQ(coord[0], model.coords[i][0]); + EXPECT_EQ(coord[1], model.coords[i][1]); + } + aom_noise_model_free(&model); +} + +TEST(NoiseModel, InitFailsWithTooLargeLag) { + aom_noise_model_t model; + aom_noise_model_params_t params = { AOM_NOISE_SHAPE_SQUARE, 10, 8, 0 }; + EXPECT_FALSE(aom_noise_model_init(&model, params)); + aom_noise_model_free(&model); +} + +TEST(NoiseModel, InitFailsWithTooSmallLag) { + aom_noise_model_t model; + aom_noise_model_params_t params = { AOM_NOISE_SHAPE_SQUARE, 0, 8, 0 }; + EXPECT_FALSE(aom_noise_model_init(&model, params)); + aom_noise_model_free(&model); +} + +TEST(NoiseModel, InitFailsWithInvalidShape) { + aom_noise_model_t model; + aom_noise_model_params_t params = { aom_noise_shape(100), 3, 8, 0 }; + EXPECT_FALSE(aom_noise_model_init(&model, params)); + aom_noise_model_free(&model); +} + +// A container template class to hold a data type and extra arguments. +// All of these args are bundled into one struct so that we can use +// parameterized tests on combinations of supported data types +// (uint8_t and uint16_t) and bit depths (8, 10, 12). +template <typename T, int bit_depth, bool use_highbd> +struct BitDepthParams { + typedef T data_type_t; + static const int kBitDepth = bit_depth; + static const bool kUseHighBD = use_highbd; +}; + +template <typename T> +class FlatBlockEstimatorTest : public ::testing::Test, public T { + public: + virtual void SetUp() { random_.Reset(171); } + typedef std::vector<typename T::data_type_t> VecType; + VecType data_; + libaom_test::ACMRandom random_; +}; + +TYPED_TEST_CASE_P(FlatBlockEstimatorTest); + +TYPED_TEST_P(FlatBlockEstimatorTest, ExtractBlock) { + const int kBlockSize = 16; + aom_flat_block_finder_t flat_block_finder; + ASSERT_EQ(1, aom_flat_block_finder_init(&flat_block_finder, kBlockSize, + this->kBitDepth, this->kUseHighBD)); + const double normalization = flat_block_finder.normalization; + + // Test with an image of more than one block. + const int h = 2 * kBlockSize; + const int w = 2 * kBlockSize; + const int stride = 2 * kBlockSize; + this->data_.resize(h * stride, 128); + + // Set up the (0,0) block to be a plane and the (0,1) block to be a + // checkerboard + const int shift = this->kBitDepth - 8; + for (int y = 0; y < kBlockSize; ++y) { + for (int x = 0; x < kBlockSize; ++x) { + this->data_[y * stride + x] = (-y + x + 128) << shift; + this->data_[y * stride + x + kBlockSize] = + ((x % 2 + y % 2) % 2 ? 128 - 20 : 128 + 20) << shift; + } + } + std::vector<double> block(kBlockSize * kBlockSize, 1); + std::vector<double> plane(kBlockSize * kBlockSize, 1); + + // The block data should be a constant (zero) and the rest of the plane + // trend is covered in the plane data. + aom_flat_block_finder_extract_block(&flat_block_finder, + (uint8_t *)&this->data_[0], w, h, stride, + 0, 0, &plane[0], &block[0]); + for (int y = 0; y < kBlockSize; ++y) { + for (int x = 0; x < kBlockSize; ++x) { + EXPECT_NEAR(0, block[y * kBlockSize + x], 1e-5); + EXPECT_NEAR((double)(this->data_[y * stride + x]) / normalization, + plane[y * kBlockSize + x], 1e-5); + } + } + + // The plane trend is a constant, and the block is a zero mean checkerboard. + aom_flat_block_finder_extract_block(&flat_block_finder, + (uint8_t *)&this->data_[0], w, h, stride, + kBlockSize, 0, &plane[0], &block[0]); + const int mid = 128 << shift; + for (int y = 0; y < kBlockSize; ++y) { + for (int x = 0; x < kBlockSize; ++x) { + EXPECT_NEAR(((double)this->data_[y * stride + x + kBlockSize] - mid) / + normalization, + block[y * kBlockSize + x], 1e-5); + EXPECT_NEAR(mid / normalization, plane[y * kBlockSize + x], 1e-5); + } + } + aom_flat_block_finder_free(&flat_block_finder); +} + +TYPED_TEST_P(FlatBlockEstimatorTest, FindFlatBlocks) { + const int kBlockSize = 32; + aom_flat_block_finder_t flat_block_finder; + ASSERT_EQ(1, aom_flat_block_finder_init(&flat_block_finder, kBlockSize, + this->kBitDepth, this->kUseHighBD)); + + const int num_blocks_w = 8; + const int h = kBlockSize; + const int w = kBlockSize * num_blocks_w; + const int stride = w; + this->data_.resize(h * stride, 128); + std::vector<uint8_t> flat_blocks(num_blocks_w, 0); + + const int shift = this->kBitDepth - 8; + for (int y = 0; y < kBlockSize; ++y) { + for (int x = 0; x < kBlockSize; ++x) { + // Block 0 (not flat): constant doesn't have enough variance to qualify + this->data_[y * stride + x + 0 * kBlockSize] = 128 << shift; + + // Block 1 (not flat): too high of variance is hard to validate as flat + this->data_[y * stride + x + 1 * kBlockSize] = + ((uint8_t)(128 + randn(&this->random_, 5))) << shift; + + // Block 2 (flat): slight checkerboard added to constant + const int check = (x % 2 + y % 2) % 2 ? -2 : 2; + this->data_[y * stride + x + 2 * kBlockSize] = (128 + check) << shift; + + // Block 3 (flat): planar block with checkerboard pattern is also flat + this->data_[y * stride + x + 3 * kBlockSize] = + (y * 2 - x / 2 + 128 + check) << shift; + + // Block 4 (flat): gaussian random with standard deviation 1. + this->data_[y * stride + x + 4 * kBlockSize] = + ((uint8_t)(randn(&this->random_, 1) + x + 128.0)) << shift; + + // Block 5 (flat): gaussian random with standard deviation 2. + this->data_[y * stride + x + 5 * kBlockSize] = + ((uint8_t)(randn(&this->random_, 2) + y + 128.0)) << shift; + + // Block 6 (not flat): too high of directional gradient. + const int strong_edge = x > kBlockSize / 2 ? 64 : 0; + this->data_[y * stride + x + 6 * kBlockSize] = + ((uint8_t)(randn(&this->random_, 1) + strong_edge + 128.0)) << shift; + + // Block 7 (not flat): too high gradient. + const int big_check = ((x >> 2) % 2 + (y >> 2) % 2) % 2 ? -16 : 16; + this->data_[y * stride + x + 7 * kBlockSize] = + ((uint8_t)(randn(&this->random_, 1) + big_check + 128.0)) << shift; + } + } + + EXPECT_EQ(4, aom_flat_block_finder_run(&flat_block_finder, + (uint8_t *)&this->data_[0], w, h, + stride, &flat_blocks[0])); + + // First two blocks are not flat + EXPECT_EQ(0, flat_blocks[0]); + EXPECT_EQ(0, flat_blocks[1]); + + // Next 4 blocks are flat. + EXPECT_EQ(255, flat_blocks[2]); + EXPECT_EQ(255, flat_blocks[3]); + EXPECT_EQ(255, flat_blocks[4]); + EXPECT_EQ(255, flat_blocks[5]); + + // Last 2 are not flat by threshold + EXPECT_EQ(0, flat_blocks[6]); + EXPECT_EQ(0, flat_blocks[7]); + + // Add the noise from non-flat block 1 to every block. + for (int y = 0; y < kBlockSize; ++y) { + for (int x = 0; x < kBlockSize * num_blocks_w; ++x) { + this->data_[y * stride + x] += + (this->data_[y * stride + x % kBlockSize + kBlockSize] - + (128 << shift)); + } + } + // Now the scored selection will pick the one that is most likely flat (block + // 0) + EXPECT_EQ(1, aom_flat_block_finder_run(&flat_block_finder, + (uint8_t *)&this->data_[0], w, h, + stride, &flat_blocks[0])); + EXPECT_EQ(1, flat_blocks[0]); + EXPECT_EQ(0, flat_blocks[1]); + EXPECT_EQ(0, flat_blocks[2]); + EXPECT_EQ(0, flat_blocks[3]); + EXPECT_EQ(0, flat_blocks[4]); + EXPECT_EQ(0, flat_blocks[5]); + EXPECT_EQ(0, flat_blocks[6]); + EXPECT_EQ(0, flat_blocks[7]); + + aom_flat_block_finder_free(&flat_block_finder); +} + +REGISTER_TYPED_TEST_CASE_P(FlatBlockEstimatorTest, ExtractBlock, + FindFlatBlocks); + +typedef ::testing::Types<BitDepthParams<uint8_t, 8, false>, // lowbd + BitDepthParams<uint16_t, 8, true>, // lowbd in 16-bit + BitDepthParams<uint16_t, 10, true>, // highbd data + BitDepthParams<uint16_t, 12, true> > + AllBitDepthParams; +INSTANTIATE_TYPED_TEST_CASE_P(FlatBlockInstatiation, FlatBlockEstimatorTest, + AllBitDepthParams); + +template <typename T> +class NoiseModelUpdateTest : public ::testing::Test, public T { + public: + static const int kWidth = 128; + static const int kHeight = 128; + static const int kBlockSize = 16; + static const int kNumBlocksX = kWidth / kBlockSize; + static const int kNumBlocksY = kHeight / kBlockSize; + + virtual void SetUp() { + const aom_noise_model_params_t params = { AOM_NOISE_SHAPE_SQUARE, 3, + T::kBitDepth, T::kUseHighBD }; + ASSERT_TRUE(aom_noise_model_init(&model_, params)); + + random_.Reset(100171); + + data_.resize(kWidth * kHeight * 3); + denoised_.resize(kWidth * kHeight * 3); + noise_.resize(kWidth * kHeight * 3); + renoise_.resize(kWidth * kHeight); + flat_blocks_.resize(kNumBlocksX * kNumBlocksY); + + for (int c = 0, offset = 0; c < 3; ++c, offset += kWidth * kHeight) { + data_ptr_[c] = &data_[offset]; + noise_ptr_[c] = &noise_[offset]; + denoised_ptr_[c] = &denoised_[offset]; + strides_[c] = kWidth; + + data_ptr_raw_[c] = (uint8_t *)&data_[offset]; + denoised_ptr_raw_[c] = (uint8_t *)&denoised_[offset]; + } + chroma_sub_[0] = 0; + chroma_sub_[1] = 0; + } + + int NoiseModelUpdate(int block_size = kBlockSize) { + return aom_noise_model_update(&model_, data_ptr_raw_, denoised_ptr_raw_, + kWidth, kHeight, strides_, chroma_sub_, + &flat_blocks_[0], block_size); + } + + void TearDown() { aom_noise_model_free(&model_); } + + protected: + aom_noise_model_t model_; + std::vector<typename T::data_type_t> data_; + std::vector<typename T::data_type_t> denoised_; + + std::vector<double> noise_; + std::vector<double> renoise_; + std::vector<uint8_t> flat_blocks_; + + typename T::data_type_t *data_ptr_[3]; + typename T::data_type_t *denoised_ptr_[3]; + + double *noise_ptr_[3]; + int strides_[3]; + int chroma_sub_[2]; + libaom_test::ACMRandom random_; + + private: + uint8_t *data_ptr_raw_[3]; + uint8_t *denoised_ptr_raw_[3]; +}; + +TYPED_TEST_CASE_P(NoiseModelUpdateTest); + +TYPED_TEST_P(NoiseModelUpdateTest, UpdateFailsNoFlatBlocks) { + EXPECT_EQ(AOM_NOISE_STATUS_INSUFFICIENT_FLAT_BLOCKS, + this->NoiseModelUpdate()); +} + +TYPED_TEST_P(NoiseModelUpdateTest, UpdateSuccessForZeroNoiseAllFlat) { + this->flat_blocks_.assign(this->flat_blocks_.size(), 1); + this->denoised_.assign(this->denoised_.size(), 128); + this->data_.assign(this->denoised_.size(), 128); + EXPECT_EQ(AOM_NOISE_STATUS_INTERNAL_ERROR, this->NoiseModelUpdate()); +} + +TYPED_TEST_P(NoiseModelUpdateTest, UpdateFailsBlockSizeTooSmall) { + this->flat_blocks_.assign(this->flat_blocks_.size(), 1); + this->denoised_.assign(this->denoised_.size(), 128); + this->data_.assign(this->denoised_.size(), 128); + EXPECT_EQ(AOM_NOISE_STATUS_INVALID_ARGUMENT, + this->NoiseModelUpdate(6 /* block_size=6 is too small*/)); +} + +TYPED_TEST_P(NoiseModelUpdateTest, UpdateSuccessForWhiteRandomNoise) { + aom_noise_model_t &model = this->model_; + const int kWidth = this->kWidth; + const int kHeight = this->kHeight; + + const int shift = this->kBitDepth - 8; + for (int y = 0; y < kHeight; ++y) { + for (int x = 0; x < kWidth; ++x) { + this->data_ptr_[0][y * kWidth + x] = + int(64 + y + randn(&this->random_, 1)) << shift; + this->denoised_ptr_[0][y * kWidth + x] = (64 + y) << shift; + // Make the chroma planes completely correlated with the Y plane + for (int c = 1; c < 3; ++c) { + this->data_ptr_[c][y * kWidth + x] = this->data_ptr_[0][y * kWidth + x]; + this->denoised_ptr_[c][y * kWidth + x] = + this->denoised_ptr_[0][y * kWidth + x]; + } + } + } + this->flat_blocks_.assign(this->flat_blocks_.size(), 1); + EXPECT_EQ(AOM_NOISE_STATUS_OK, this->NoiseModelUpdate()); + + const double kCoeffEps = 0.075; + const int n = model.n; + for (int c = 0; c < 3; ++c) { + for (int i = 0; i < n; ++i) { + EXPECT_NEAR(0, model.latest_state[c].eqns.x[i], kCoeffEps); + EXPECT_NEAR(0, model.combined_state[c].eqns.x[i], kCoeffEps); + } + // The second and third channels are highly correlated with the first. + if (c > 0) { + ASSERT_EQ(n + 1, model.latest_state[c].eqns.n); + ASSERT_EQ(n + 1, model.combined_state[c].eqns.n); + + EXPECT_NEAR(1, model.latest_state[c].eqns.x[n], kCoeffEps); + EXPECT_NEAR(1, model.combined_state[c].eqns.x[n], kCoeffEps); + } + } + + // The fitted noise strength should be close to the standard deviation + // for all intensity bins. + const double kStdEps = 0.1; + const double normalize = 1 << shift; + + for (int i = 0; i < model.latest_state[0].strength_solver.eqns.n; ++i) { + EXPECT_NEAR(1.0, + model.latest_state[0].strength_solver.eqns.x[i] / normalize, + kStdEps); + EXPECT_NEAR(1.0, + model.combined_state[0].strength_solver.eqns.x[i] / normalize, + kStdEps); + } + + aom_noise_strength_lut_t lut; + aom_noise_strength_solver_fit_piecewise( + &model.latest_state[0].strength_solver, -1, &lut); + ASSERT_EQ(2, lut.num_points); + EXPECT_NEAR(0.0, lut.points[0][0], 1e-5); + EXPECT_NEAR(1.0, lut.points[0][1] / normalize, kStdEps); + EXPECT_NEAR((1 << this->kBitDepth) - 1, lut.points[1][0], 1e-5); + EXPECT_NEAR(1.0, lut.points[1][1] / normalize, kStdEps); + aom_noise_strength_lut_free(&lut); +} + +TYPED_TEST_P(NoiseModelUpdateTest, UpdateSuccessForScaledWhiteNoise) { + aom_noise_model_t &model = this->model_; + const int kWidth = this->kWidth; + const int kHeight = this->kHeight; + + const double kCoeffEps = 0.055; + const double kLowStd = 1; + const double kHighStd = 4; + const int shift = this->kBitDepth - 8; + for (int y = 0; y < kHeight; ++y) { + for (int x = 0; x < kWidth; ++x) { + for (int c = 0; c < 3; ++c) { + // The image data is bimodal: + // Bottom half has low intensity and low noise strength + // Top half has high intensity and high noise strength + const int avg = (y < kHeight / 2) ? 4 : 245; + const double std = (y < kHeight / 2) ? kLowStd : kHighStd; + this->data_ptr_[c][y * kWidth + x] = + ((uint8_t)std::min((int)255, + (int)(2 + avg + randn(&this->random_, std)))) + << shift; + this->denoised_ptr_[c][y * kWidth + x] = (2 + avg) << shift; + } + } + } + // Label all blocks as flat for the update + this->flat_blocks_.assign(this->flat_blocks_.size(), 1); + EXPECT_EQ(AOM_NOISE_STATUS_OK, this->NoiseModelUpdate()); + + const int n = model.n; + // The noise is uncorrelated spatially and with the y channel. + // All coefficients should be reasonably close to zero. + for (int c = 0; c < 3; ++c) { + for (int i = 0; i < n; ++i) { + EXPECT_NEAR(0, model.latest_state[c].eqns.x[i], kCoeffEps); + EXPECT_NEAR(0, model.combined_state[c].eqns.x[i], kCoeffEps); + } + if (c > 0) { + ASSERT_EQ(n + 1, model.latest_state[c].eqns.n); + ASSERT_EQ(n + 1, model.combined_state[c].eqns.n); + + // The correlation to the y channel should be low (near zero) + EXPECT_NEAR(0, model.latest_state[c].eqns.x[n], kCoeffEps); + EXPECT_NEAR(0, model.combined_state[c].eqns.x[n], kCoeffEps); + } + } + + // Noise strength should vary between kLowStd and kHighStd. + const double kStdEps = 0.15; + // We have to normalize fitted standard deviation based on bit depth. + const double normalize = (1 << shift); + + ASSERT_EQ(20, model.latest_state[0].strength_solver.eqns.n); + for (int i = 0; i < model.latest_state[0].strength_solver.eqns.n; ++i) { + const double a = i / 19.0; + const double expected = (kLowStd * (1.0 - a) + kHighStd * a); + EXPECT_NEAR(expected, + model.latest_state[0].strength_solver.eqns.x[i] / normalize, + kStdEps); + EXPECT_NEAR(expected, + model.combined_state[0].strength_solver.eqns.x[i] / normalize, + kStdEps); + } + + // If we fit a piecewise linear model, there should be two points: + // one near kLowStd at 0, and the other near kHighStd and 255. + aom_noise_strength_lut_t lut; + aom_noise_strength_solver_fit_piecewise( + &model.latest_state[0].strength_solver, 2, &lut); + ASSERT_EQ(2, lut.num_points); + EXPECT_NEAR(0, lut.points[0][0], 1e-4); + EXPECT_NEAR(kLowStd, lut.points[0][1] / normalize, kStdEps); + EXPECT_NEAR((1 << this->kBitDepth) - 1, lut.points[1][0], 1e-5); + EXPECT_NEAR(kHighStd, lut.points[1][1] / normalize, kStdEps); + aom_noise_strength_lut_free(&lut); +} + +TYPED_TEST_P(NoiseModelUpdateTest, UpdateSuccessForCorrelatedNoise) { + aom_noise_model_t &model = this->model_; + const int kWidth = this->kWidth; + const int kHeight = this->kHeight; + const int kNumCoeffs = 24; + const double kStd = 4; + const double kStdEps = 0.3; + const double kCoeffEps = 0.065; + // Use different coefficients for each channel + const double kCoeffs[3][24] = { + { 0.02884, -0.03356, 0.00633, 0.01757, 0.02849, -0.04620, + 0.02833, -0.07178, 0.07076, -0.11603, -0.10413, -0.16571, + 0.05158, -0.07969, 0.02640, -0.07191, 0.02530, 0.41968, + 0.21450, -0.00702, -0.01401, -0.03676, -0.08713, 0.44196 }, + { 0.00269, -0.01291, -0.01513, 0.07234, 0.03208, 0.00477, + 0.00226, -0.00254, 0.03533, 0.12841, -0.25970, -0.06336, + 0.05238, -0.00845, -0.03118, 0.09043, -0.36558, 0.48903, + 0.00595, -0.11938, 0.02106, 0.095956, -0.350139, 0.59305 }, + { -0.00643, -0.01080, -0.01466, 0.06951, 0.03707, -0.00482, + 0.00817, -0.00909, 0.02949, 0.12181, -0.25210, -0.07886, + 0.06083, -0.01210, -0.03108, 0.08944, -0.35875, 0.49150, + 0.00415, -0.12905, 0.02870, 0.09740, -0.34610, 0.58824 }, + }; + + ASSERT_EQ(model.n, kNumCoeffs); + this->chroma_sub_[0] = this->chroma_sub_[1] = 1; + + this->flat_blocks_.assign(this->flat_blocks_.size(), 1); + + // Add different noise onto each plane + const int shift = this->kBitDepth - 8; + for (int c = 0; c < 3; ++c) { + noise_synth(&this->random_, model.params.lag, model.n, model.coords, + kCoeffs[c], this->noise_ptr_[c], kWidth, kHeight); + const int x_shift = c > 0 ? this->chroma_sub_[0] : 0; + const int y_shift = c > 0 ? this->chroma_sub_[1] : 0; + for (int y = 0; y < (kHeight >> y_shift); ++y) { + for (int x = 0; x < (kWidth >> x_shift); ++x) { + const uint8_t value = 64 + x / 2 + y / 4; + this->data_ptr_[c][y * kWidth + x] = + (uint8_t(value + this->noise_ptr_[c][y * kWidth + x] * kStd)) + << shift; + this->denoised_ptr_[c][y * kWidth + x] = value << shift; + } + } + } + EXPECT_EQ(AOM_NOISE_STATUS_OK, this->NoiseModelUpdate()); + + // For the Y plane, the solved coefficients should be close to the original + const int n = model.n; + for (int c = 0; c < 3; ++c) { + for (int i = 0; i < n; ++i) { + EXPECT_NEAR(kCoeffs[c][i], model.latest_state[c].eqns.x[i], kCoeffEps); + EXPECT_NEAR(kCoeffs[c][i], model.combined_state[c].eqns.x[i], kCoeffEps); + } + // The chroma planes should be uncorrelated with the luma plane + if (c > 0) { + EXPECT_NEAR(0, model.latest_state[c].eqns.x[n], kCoeffEps); + EXPECT_NEAR(0, model.combined_state[c].eqns.x[n], kCoeffEps); + } + // Correlation between the coefficient vector and the fitted coefficients + // should be close to 1. + EXPECT_LT(0.98, aom_normalized_cross_correlation( + model.latest_state[c].eqns.x, kCoeffs[c], kNumCoeffs)); + + noise_synth(&this->random_, model.params.lag, model.n, model.coords, + model.latest_state[c].eqns.x, &this->renoise_[0], kWidth, + kHeight); + + EXPECT_TRUE(aom_noise_data_validate(&this->renoise_[0], kWidth, kHeight)); + } + + // Check fitted noise strength + const double normalize = 1 << shift; + for (int c = 0; c < 3; ++c) { + for (int i = 0; i < model.latest_state[c].strength_solver.eqns.n; ++i) { + EXPECT_NEAR(kStd, + model.latest_state[c].strength_solver.eqns.x[i] / normalize, + kStdEps); + } + } +} + +TYPED_TEST_P(NoiseModelUpdateTest, + NoiseStrengthChangeSignalsDifferentNoiseType) { + aom_noise_model_t &model = this->model_; + const int kWidth = this->kWidth; + const int kHeight = this->kHeight; + const int kBlockSize = this->kBlockSize; + // Create a gradient image with std = 2 uncorrelated noise + const double kStd = 2; + const int shift = this->kBitDepth - 8; + + for (int i = 0; i < kWidth * kHeight; ++i) { + const uint8_t val = (i % kWidth) < kWidth / 2 ? 64 : 192; + for (int c = 0; c < 3; ++c) { + this->noise_ptr_[c][i] = randn(&this->random_, 1); + this->data_ptr_[c][i] = ((uint8_t)(this->noise_ptr_[c][i] * kStd + val)) + << shift; + this->denoised_ptr_[c][i] = val << shift; + } + } + this->flat_blocks_.assign(this->flat_blocks_.size(), 1); + EXPECT_EQ(AOM_NOISE_STATUS_OK, this->NoiseModelUpdate()); + + const int kNumBlocks = kWidth * kHeight / kBlockSize / kBlockSize; + EXPECT_EQ(kNumBlocks, model.latest_state[0].strength_solver.num_equations); + EXPECT_EQ(kNumBlocks, model.latest_state[1].strength_solver.num_equations); + EXPECT_EQ(kNumBlocks, model.latest_state[2].strength_solver.num_equations); + EXPECT_EQ(kNumBlocks, model.combined_state[0].strength_solver.num_equations); + EXPECT_EQ(kNumBlocks, model.combined_state[1].strength_solver.num_equations); + EXPECT_EQ(kNumBlocks, model.combined_state[2].strength_solver.num_equations); + + // Bump up noise by an insignificant amount + for (int i = 0; i < kWidth * kHeight; ++i) { + const uint8_t val = (i % kWidth) < kWidth / 2 ? 64 : 192; + this->data_ptr_[0][i] = + ((uint8_t)(this->noise_ptr_[0][i] * (kStd + 0.085) + val)) << shift; + } + EXPECT_EQ(AOM_NOISE_STATUS_OK, this->NoiseModelUpdate()); + + const double kARGainTolerance = 0.02; + for (int c = 0; c < 3; ++c) { + EXPECT_EQ(kNumBlocks, model.latest_state[c].strength_solver.num_equations); + EXPECT_EQ(15250, model.latest_state[c].num_observations); + EXPECT_NEAR(1, model.latest_state[c].ar_gain, kARGainTolerance); + + EXPECT_EQ(2 * kNumBlocks, + model.combined_state[c].strength_solver.num_equations); + EXPECT_EQ(2 * 15250, model.combined_state[c].num_observations); + EXPECT_NEAR(1, model.combined_state[c].ar_gain, kARGainTolerance); + } + + // Bump up the noise strength on half the image for one channel by a + // significant amount. + for (int i = 0; i < kWidth * kHeight; ++i) { + const uint8_t val = (i % kWidth) < kWidth / 2 ? 64 : 128; + if (i % kWidth < kWidth / 2) { + this->data_ptr_[0][i] = + ((uint8_t)(randn(&this->random_, kStd + 0.5) + val)) << shift; + } + } + EXPECT_EQ(AOM_NOISE_STATUS_DIFFERENT_NOISE_TYPE, this->NoiseModelUpdate()); + + // Since we didn't update the combined state, it should still be at 2 * + // num_blocks + EXPECT_EQ(kNumBlocks, model.latest_state[0].strength_solver.num_equations); + EXPECT_EQ(2 * kNumBlocks, + model.combined_state[0].strength_solver.num_equations); + + // In normal operation, the "latest" estimate can be saved to the "combined" + // state for continued updates. + aom_noise_model_save_latest(&model); + for (int c = 0; c < 3; ++c) { + EXPECT_EQ(kNumBlocks, model.latest_state[c].strength_solver.num_equations); + EXPECT_EQ(15250, model.latest_state[c].num_observations); + EXPECT_NEAR(1, model.latest_state[c].ar_gain, kARGainTolerance); + + EXPECT_EQ(kNumBlocks, + model.combined_state[c].strength_solver.num_equations); + EXPECT_EQ(15250, model.combined_state[c].num_observations); + EXPECT_NEAR(1, model.combined_state[c].ar_gain, kARGainTolerance); + } +} + +TYPED_TEST_P(NoiseModelUpdateTest, NoiseCoeffsSignalsDifferentNoiseType) { + aom_noise_model_t &model = this->model_; + const int kWidth = this->kWidth; + const int kHeight = this->kHeight; + const double kCoeffs[2][24] = { + { 0.02884, -0.03356, 0.00633, 0.01757, 0.02849, -0.04620, + 0.02833, -0.07178, 0.07076, -0.11603, -0.10413, -0.16571, + 0.05158, -0.07969, 0.02640, -0.07191, 0.02530, 0.41968, + 0.21450, -0.00702, -0.01401, -0.03676, -0.08713, 0.44196 }, + { 0.00269, -0.01291, -0.01513, 0.07234, 0.03208, 0.00477, + 0.00226, -0.00254, 0.03533, 0.12841, -0.25970, -0.06336, + 0.05238, -0.00845, -0.03118, 0.09043, -0.36558, 0.48903, + 0.00595, -0.11938, 0.02106, 0.095956, -0.350139, 0.59305 } + }; + + noise_synth(&this->random_, model.params.lag, model.n, model.coords, + kCoeffs[0], this->noise_ptr_[0], kWidth, kHeight); + for (int i = 0; i < kWidth * kHeight; ++i) { + this->data_ptr_[0][i] = (uint8_t)(128 + this->noise_ptr_[0][i]); + } + this->flat_blocks_.assign(this->flat_blocks_.size(), 1); + EXPECT_EQ(AOM_NOISE_STATUS_OK, this->NoiseModelUpdate()); + + // Now try with the second set of AR coefficients + noise_synth(&this->random_, model.params.lag, model.n, model.coords, + kCoeffs[1], this->noise_ptr_[0], kWidth, kHeight); + for (int i = 0; i < kWidth * kHeight; ++i) { + this->data_ptr_[0][i] = (uint8_t)(128 + this->noise_ptr_[0][i]); + } + EXPECT_EQ(AOM_NOISE_STATUS_DIFFERENT_NOISE_TYPE, this->NoiseModelUpdate()); +} +REGISTER_TYPED_TEST_CASE_P(NoiseModelUpdateTest, UpdateFailsNoFlatBlocks, + UpdateSuccessForZeroNoiseAllFlat, + UpdateFailsBlockSizeTooSmall, + UpdateSuccessForWhiteRandomNoise, + UpdateSuccessForScaledWhiteNoise, + UpdateSuccessForCorrelatedNoise, + NoiseStrengthChangeSignalsDifferentNoiseType, + NoiseCoeffsSignalsDifferentNoiseType); + +INSTANTIATE_TYPED_TEST_CASE_P(NoiseModelUpdateTestInstatiation, + NoiseModelUpdateTest, AllBitDepthParams); + +TEST(NoiseModelGetGrainParameters, TestLagSize) { + aom_film_grain_t film_grain; + for (int lag = 1; lag <= 3; ++lag) { + aom_noise_model_params_t params = { AOM_NOISE_SHAPE_SQUARE, lag, 8, 0 }; + aom_noise_model_t model; + EXPECT_TRUE(aom_noise_model_init(&model, params)); + EXPECT_TRUE(aom_noise_model_get_grain_parameters(&model, &film_grain)); + EXPECT_EQ(lag, film_grain.ar_coeff_lag); + aom_noise_model_free(&model); + } + + aom_noise_model_params_t params = { AOM_NOISE_SHAPE_SQUARE, 4, 8, 0 }; + aom_noise_model_t model; + EXPECT_TRUE(aom_noise_model_init(&model, params)); + EXPECT_FALSE(aom_noise_model_get_grain_parameters(&model, &film_grain)); + aom_noise_model_free(&model); +} + +TEST(NoiseModelGetGrainParameters, TestARCoeffShiftBounds) { + struct TestCase { + double max_input_value; + int expected_ar_coeff_shift; + int expected_value; + }; + const int lag = 1; + const int kNumTestCases = 19; + const TestCase test_cases[] = { + // Test cases for ar_coeff_shift = 9 + { 0, 9, 0 }, + { 0.125, 9, 64 }, + { -0.125, 9, -64 }, + { 0.2499, 9, 127 }, + { -0.25, 9, -128 }, + // Test cases for ar_coeff_shift = 8 + { 0.25, 8, 64 }, + { -0.2501, 8, -64 }, + { 0.499, 8, 127 }, + { -0.5, 8, -128 }, + // Test cases for ar_coeff_shift = 7 + { 0.5, 7, 64 }, + { -0.5001, 7, -64 }, + { 0.999, 7, 127 }, + { -1, 7, -128 }, + // Test cases for ar_coeff_shift = 6 + { 1.0, 6, 64 }, + { -1.0001, 6, -64 }, + { 2.0, 6, 127 }, + { -2.0, 6, -128 }, + { 4, 6, 127 }, + { -4, 6, -128 }, + }; + aom_noise_model_params_t params = { AOM_NOISE_SHAPE_SQUARE, lag, 8, 0 }; + aom_noise_model_t model; + EXPECT_TRUE(aom_noise_model_init(&model, params)); + + for (int i = 0; i < kNumTestCases; ++i) { + const TestCase &test_case = test_cases[i]; + model.combined_state[0].eqns.x[0] = test_case.max_input_value; + + aom_film_grain_t film_grain; + EXPECT_TRUE(aom_noise_model_get_grain_parameters(&model, &film_grain)); + EXPECT_EQ(1, film_grain.ar_coeff_lag); + EXPECT_EQ(test_case.expected_ar_coeff_shift, film_grain.ar_coeff_shift); + EXPECT_EQ(test_case.expected_value, film_grain.ar_coeffs_y[0]); + } + aom_noise_model_free(&model); +} + +TEST(NoiseModelGetGrainParameters, TestNoiseStrengthShiftBounds) { + struct TestCase { + double max_input_value; + int expected_scaling_shift; + int expected_value; + }; + const int kNumTestCases = 10; + const TestCase test_cases[] = { + { 0, 11, 0 }, { 1, 11, 64 }, { 2, 11, 128 }, { 3.99, 11, 255 }, + { 4, 10, 128 }, { 7.99, 10, 255 }, { 8, 9, 128 }, { 16, 8, 128 }, + { 31.99, 8, 255 }, { 64, 8, 255 }, // clipped + }; + const int lag = 1; + aom_noise_model_params_t params = { AOM_NOISE_SHAPE_SQUARE, lag, 8, 0 }; + aom_noise_model_t model; + EXPECT_TRUE(aom_noise_model_init(&model, params)); + + for (int i = 0; i < kNumTestCases; ++i) { + const TestCase &test_case = test_cases[i]; + aom_equation_system_t &eqns = model.combined_state[0].strength_solver.eqns; + // Set the fitted scale parameters to be a constant value. + for (int j = 0; j < eqns.n; ++j) { + eqns.x[j] = test_case.max_input_value; + } + aom_film_grain_t film_grain; + EXPECT_TRUE(aom_noise_model_get_grain_parameters(&model, &film_grain)); + // We expect a single constant segemnt + EXPECT_EQ(test_case.expected_scaling_shift, film_grain.scaling_shift); + EXPECT_EQ(test_case.expected_value, film_grain.scaling_points_y[0][1]); + EXPECT_EQ(test_case.expected_value, film_grain.scaling_points_y[1][1]); + } + aom_noise_model_free(&model); +} + +// The AR coefficients are the same inputs used to generate "Test 2" in the test +// vectors +TEST(NoiseModelGetGrainParameters, GetGrainParametersReal) { + const double kInputCoeffsY[] = { 0.0315, 0.0073, 0.0218, 0.00235, 0.00511, + -0.0222, 0.0627, -0.022, 0.05575, -0.1816, + 0.0107, -0.1966, 0.00065, -0.0809, 0.04934, + -0.1349, -0.0352, 0.41772, 0.27973, 0.04207, + -0.0429, -0.1372, 0.06193, 0.52032 }; + const double kInputCoeffsCB[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5 }; + const double kInputCoeffsCR[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.5 }; + const int kExpectedARCoeffsY[] = { 4, 1, 3, 0, 1, -3, 8, -3, + 7, -23, 1, -25, 0, -10, 6, -17, + -5, 53, 36, 5, -5, -18, 8, 67 }; + const int kExpectedARCoeffsCB[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 84 }; + const int kExpectedARCoeffsCR[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -126 }; + // Scaling function is initialized analytically with a sqrt function. + const int kNumScalingPointsY = 12; + const int kExpectedScalingPointsY[][2] = { + { 0, 0 }, { 13, 44 }, { 27, 62 }, { 40, 76 }, + { 54, 88 }, { 67, 98 }, { 94, 117 }, { 121, 132 }, + { 148, 146 }, { 174, 159 }, { 201, 171 }, { 255, 192 }, + }; + + const int lag = 3; + aom_noise_model_params_t params = { AOM_NOISE_SHAPE_SQUARE, lag, 8, 0 }; + aom_noise_model_t model; + EXPECT_TRUE(aom_noise_model_init(&model, params)); + + // Setup the AR coeffs + memcpy(model.combined_state[0].eqns.x, kInputCoeffsY, sizeof(kInputCoeffsY)); + memcpy(model.combined_state[1].eqns.x, kInputCoeffsCB, + sizeof(kInputCoeffsCB)); + memcpy(model.combined_state[2].eqns.x, kInputCoeffsCR, + sizeof(kInputCoeffsCR)); + for (int i = 0; i < model.combined_state[0].strength_solver.num_bins; ++i) { + const double x = + ((double)i) / (model.combined_state[0].strength_solver.num_bins - 1.0); + model.combined_state[0].strength_solver.eqns.x[i] = 6 * sqrt(x); + model.combined_state[1].strength_solver.eqns.x[i] = 3; + model.combined_state[2].strength_solver.eqns.x[i] = 2; + + // Inject some observations into the strength solver, as during film grain + // parameter extraction an estimate of the average strength will be used to + // adjust correlation. + const int n = model.combined_state[0].strength_solver.num_bins; + for (int j = 0; j < model.combined_state[0].strength_solver.num_bins; ++j) { + model.combined_state[0].strength_solver.eqns.A[i * n + j] = 1; + model.combined_state[1].strength_solver.eqns.A[i * n + j] = 1; + model.combined_state[2].strength_solver.eqns.A[i * n + j] = 1; + } + } + + aom_film_grain_t film_grain; + EXPECT_TRUE(aom_noise_model_get_grain_parameters(&model, &film_grain)); + EXPECT_EQ(lag, film_grain.ar_coeff_lag); + EXPECT_EQ(3, film_grain.ar_coeff_lag); + EXPECT_EQ(7, film_grain.ar_coeff_shift); + EXPECT_EQ(10, film_grain.scaling_shift); + EXPECT_EQ(kNumScalingPointsY, film_grain.num_y_points); + EXPECT_EQ(1, film_grain.update_parameters); + EXPECT_EQ(1, film_grain.apply_grain); + + const int kNumARCoeffs = 24; + for (int i = 0; i < kNumARCoeffs; ++i) { + EXPECT_EQ(kExpectedARCoeffsY[i], film_grain.ar_coeffs_y[i]); + } + for (int i = 0; i < kNumARCoeffs + 1; ++i) { + EXPECT_EQ(kExpectedARCoeffsCB[i], film_grain.ar_coeffs_cb[i]); + } + for (int i = 0; i < kNumARCoeffs + 1; ++i) { + EXPECT_EQ(kExpectedARCoeffsCR[i], film_grain.ar_coeffs_cr[i]); + } + for (int i = 0; i < kNumScalingPointsY; ++i) { + EXPECT_EQ(kExpectedScalingPointsY[i][0], film_grain.scaling_points_y[i][0]); + EXPECT_EQ(kExpectedScalingPointsY[i][1], film_grain.scaling_points_y[i][1]); + } + + // CB strength should just be a piecewise segment + EXPECT_EQ(2, film_grain.num_cb_points); + EXPECT_EQ(0, film_grain.scaling_points_cb[0][0]); + EXPECT_EQ(255, film_grain.scaling_points_cb[1][0]); + EXPECT_EQ(96, film_grain.scaling_points_cb[0][1]); + EXPECT_EQ(96, film_grain.scaling_points_cb[1][1]); + + // CR strength should just be a piecewise segment + EXPECT_EQ(2, film_grain.num_cr_points); + EXPECT_EQ(0, film_grain.scaling_points_cr[0][0]); + EXPECT_EQ(255, film_grain.scaling_points_cr[1][0]); + EXPECT_EQ(64, film_grain.scaling_points_cr[0][1]); + EXPECT_EQ(64, film_grain.scaling_points_cr[1][1]); + + EXPECT_EQ(128, film_grain.cb_mult); + EXPECT_EQ(192, film_grain.cb_luma_mult); + EXPECT_EQ(256, film_grain.cb_offset); + EXPECT_EQ(128, film_grain.cr_mult); + EXPECT_EQ(192, film_grain.cr_luma_mult); + EXPECT_EQ(256, film_grain.cr_offset); + EXPECT_EQ(0, film_grain.chroma_scaling_from_luma); + EXPECT_EQ(0, film_grain.grain_scale_shift); + + aom_noise_model_free(&model); +} + +template <typename T> +class WienerDenoiseTest : public ::testing::Test, public T { + public: + static void SetUpTestCase() { aom_dsp_rtcd(); } + + protected: + void SetUp() { + static const float kNoiseLevel = 5.f; + static const float kStd = 4.0; + static const double kMaxValue = (1 << T::kBitDepth) - 1; + + chroma_sub_[0] = 1; + chroma_sub_[1] = 1; + stride_[0] = kWidth; + stride_[1] = kWidth / 2; + stride_[2] = kWidth / 2; + for (int k = 0; k < 3; ++k) { + data_[k].resize(kWidth * kHeight); + denoised_[k].resize(kWidth * kHeight); + noise_psd_[k].resize(kBlockSize * kBlockSize); + } + + const double kCoeffsY[] = { 0.0406, -0.116, -0.078, -0.152, 0.0033, -0.093, + 0.048, 0.404, 0.2353, -0.035, -0.093, 0.441 }; + const int kCoords[12][2] = { + { -2, -2 }, { -1, -2 }, { 0, -2 }, { 1, -2 }, { 2, -2 }, { -2, -1 }, + { -1, -1 }, { 0, -1 }, { 1, -1 }, { 2, -1 }, { -2, 0 }, { -1, 0 } + }; + const int kLag = 2; + const int kLength = 12; + libaom_test::ACMRandom random; + std::vector<double> noise(kWidth * kHeight); + noise_synth(&random, kLag, kLength, kCoords, kCoeffsY, &noise[0], kWidth, + kHeight); + noise_psd_[0] = get_noise_psd(&noise[0], kWidth, kHeight, kBlockSize); + for (int i = 0; i < kBlockSize * kBlockSize; ++i) { + noise_psd_[0][i] = (float)(noise_psd_[0][i] * kStd * kStd * kScaleNoise * + kScaleNoise / (kMaxValue * kMaxValue)); + } + + float psd_value = + aom_noise_psd_get_default_value(kBlockSizeChroma, kNoiseLevel); + for (int i = 0; i < kBlockSizeChroma * kBlockSizeChroma; ++i) { + noise_psd_[1][i] = psd_value; + noise_psd_[2][i] = psd_value; + } + for (int y = 0; y < kHeight; ++y) { + for (int x = 0; x < kWidth; ++x) { + data_[0][y * stride_[0] + x] = (typename T::data_type_t)fclamp( + (x + noise[y * stride_[0] + x] * kStd) * kScaleNoise, 0, kMaxValue); + } + } + + for (int c = 1; c < 3; ++c) { + for (int y = 0; y < (kHeight >> 1); ++y) { + for (int x = 0; x < (kWidth >> 1); ++x) { + data_[c][y * stride_[c] + x] = (typename T::data_type_t)fclamp( + (x + randn(&random, kStd)) * kScaleNoise, 0, kMaxValue); + } + } + } + for (int k = 0; k < 3; ++k) { + noise_psd_ptrs_[k] = &noise_psd_[k][0]; + } + } + static const int kBlockSize = 32; + static const int kBlockSizeChroma = 16; + static const int kWidth = 256; + static const int kHeight = 256; + static const int kScaleNoise = 1 << (T::kBitDepth - 8); + + std::vector<typename T::data_type_t> data_[3]; + std::vector<typename T::data_type_t> denoised_[3]; + std::vector<float> noise_psd_[3]; + int chroma_sub_[2]; + float *noise_psd_ptrs_[3]; + int stride_[3]; +}; + +TYPED_TEST_CASE_P(WienerDenoiseTest); + +TYPED_TEST_P(WienerDenoiseTest, InvalidBlockSize) { + const uint8_t *const data_ptrs[3] = { + reinterpret_cast<uint8_t *>(&this->data_[0][0]), + reinterpret_cast<uint8_t *>(&this->data_[1][0]), + reinterpret_cast<uint8_t *>(&this->data_[2][0]), + }; + uint8_t *denoised_ptrs[3] = { + reinterpret_cast<uint8_t *>(&this->denoised_[0][0]), + reinterpret_cast<uint8_t *>(&this->denoised_[1][0]), + reinterpret_cast<uint8_t *>(&this->denoised_[2][0]), + }; + EXPECT_EQ(0, aom_wiener_denoise_2d(data_ptrs, denoised_ptrs, this->kWidth, + this->kHeight, this->stride_, + this->chroma_sub_, this->noise_psd_ptrs_, + 18, this->kBitDepth, this->kUseHighBD)); + EXPECT_EQ(0, aom_wiener_denoise_2d(data_ptrs, denoised_ptrs, this->kWidth, + this->kHeight, this->stride_, + this->chroma_sub_, this->noise_psd_ptrs_, + 48, this->kBitDepth, this->kUseHighBD)); + EXPECT_EQ(0, aom_wiener_denoise_2d(data_ptrs, denoised_ptrs, this->kWidth, + this->kHeight, this->stride_, + this->chroma_sub_, this->noise_psd_ptrs_, + 64, this->kBitDepth, this->kUseHighBD)); +} + +TYPED_TEST_P(WienerDenoiseTest, InvalidChromaSubsampling) { + const uint8_t *const data_ptrs[3] = { + reinterpret_cast<uint8_t *>(&this->data_[0][0]), + reinterpret_cast<uint8_t *>(&this->data_[1][0]), + reinterpret_cast<uint8_t *>(&this->data_[2][0]), + }; + uint8_t *denoised_ptrs[3] = { + reinterpret_cast<uint8_t *>(&this->denoised_[0][0]), + reinterpret_cast<uint8_t *>(&this->denoised_[1][0]), + reinterpret_cast<uint8_t *>(&this->denoised_[2][0]), + }; + int chroma_sub[2] = { 1, 0 }; + EXPECT_EQ(0, aom_wiener_denoise_2d(data_ptrs, denoised_ptrs, this->kWidth, + this->kHeight, this->stride_, chroma_sub, + this->noise_psd_ptrs_, 32, this->kBitDepth, + this->kUseHighBD)); + + chroma_sub[0] = 0; + chroma_sub[1] = 1; + EXPECT_EQ(0, aom_wiener_denoise_2d(data_ptrs, denoised_ptrs, this->kWidth, + this->kHeight, this->stride_, chroma_sub, + this->noise_psd_ptrs_, 32, this->kBitDepth, + this->kUseHighBD)); +} + +TYPED_TEST_P(WienerDenoiseTest, GradientTest) { + const int kWidth = this->kWidth; + const int kHeight = this->kHeight; + const int kBlockSize = this->kBlockSize; + const uint8_t *const data_ptrs[3] = { + reinterpret_cast<uint8_t *>(&this->data_[0][0]), + reinterpret_cast<uint8_t *>(&this->data_[1][0]), + reinterpret_cast<uint8_t *>(&this->data_[2][0]), + }; + uint8_t *denoised_ptrs[3] = { + reinterpret_cast<uint8_t *>(&this->denoised_[0][0]), + reinterpret_cast<uint8_t *>(&this->denoised_[1][0]), + reinterpret_cast<uint8_t *>(&this->denoised_[2][0]), + }; + const int ret = aom_wiener_denoise_2d( + data_ptrs, denoised_ptrs, kWidth, kHeight, this->stride_, + this->chroma_sub_, this->noise_psd_ptrs_, this->kBlockSize, + this->kBitDepth, this->kUseHighBD); + EXPECT_EQ(1, ret); + + // Check the noise on the denoised image (from the analytical gradient) + // and make sure that it is less than what we added. + for (int c = 0; c < 3; ++c) { + std::vector<double> measured_noise(kWidth * kHeight); + + double var = 0; + const int shift = (c > 0); + for (int x = 0; x < (kWidth >> shift); ++x) { + for (int y = 0; y < (kHeight >> shift); ++y) { + const double diff = this->denoised_[c][y * this->stride_[c] + x] - + x * this->kScaleNoise; + var += diff * diff; + measured_noise[y * kWidth + x] = diff; + } + } + var /= (kWidth * kHeight); + const double std = sqrt(std::max(0.0, var)); + EXPECT_LE(std, 1.25f * this->kScaleNoise); + if (c == 0) { + std::vector<float> measured_psd = + get_noise_psd(&measured_noise[0], kWidth, kHeight, kBlockSize); + std::vector<double> measured_psd_d(kBlockSize * kBlockSize); + std::vector<double> noise_psd_d(kBlockSize * kBlockSize); + std::copy(measured_psd.begin(), measured_psd.end(), + measured_psd_d.begin()); + std::copy(this->noise_psd_[0].begin(), this->noise_psd_[0].end(), + noise_psd_d.begin()); + EXPECT_LT( + aom_normalized_cross_correlation(&measured_psd_d[0], &noise_psd_d[0], + (int)(noise_psd_d.size())), + 0.35); + } + } +} + +REGISTER_TYPED_TEST_CASE_P(WienerDenoiseTest, InvalidBlockSize, + InvalidChromaSubsampling, GradientTest); + +INSTANTIATE_TYPED_TEST_CASE_P(WienerDenoiseTestInstatiation, WienerDenoiseTest, + AllBitDepthParams); |