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aomedia / aom / 07105c00878acad3b4bb32b7290cceef74c6cba6 / . / test / noise_model_test.cc
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/*
* Copyright (c) 2018, Alliance for Open Media. All rights reserved
*
* This source code is subject to the terms of the BSD 2 Clause License and
* the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License
* was not distributed with this source code in the LICENSE file, you can
* obtain it at www.aomedia.org/license/software. If the Alliance for Open
* Media Patent License 1.0 was not distributed with this source code in the
* PATENTS file, you can obtain it at www.aomedia.org/license/patent.
*/
#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_SUITE_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_SUITE_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_SUITE_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_SUITE_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_SUITE_P(NoiseModelUpdateTest, UpdateFailsNoFlatBlocks,
UpdateSuccessForZeroNoiseAllFlat,
UpdateFailsBlockSizeTooSmall,
UpdateSuccessForWhiteRandomNoise,
UpdateSuccessForScaledWhiteNoise,
UpdateSuccessForCorrelatedNoise,
NoiseStrengthChangeSignalsDifferentNoiseType,
NoiseCoeffsSignalsDifferentNoiseType);
INSTANTIATE_TYPED_TEST_SUITE_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_SUITE_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_SUITE_P(WienerDenoiseTest, InvalidBlockSize,
InvalidChromaSubsampling, GradientTest);
INSTANTIATE_TYPED_TEST_SUITE_P(WienerDenoiseTestInstatiation, WienerDenoiseTest,
AllBitDepthParams);