Adding a sample binary to model film grain
This allows a user to provide a denoised file (with their own custom
denoiser) and outputs grain parameters that can be consumed by the
encoder. In this way, rather than denoising directly in the encoder,
the denoising doesn't have to happen on multiple passes (or on multiple
representations/scaled versions of the same file)
Change-Id: Ie28b98d122752144d561c685b72614cf6d51bbef
diff --git a/test/noise_model_test.cc b/test/noise_model_test.cc
index 992884a..4405d2e 100644
--- a/test/noise_model_test.cc
+++ b/test/noise_model_test.cc
@@ -602,6 +602,108 @@
}
}
+TEST_F(NoiseModelUpdateTest, NoiseStrengthChangeSignalsDifferentNoiseType) {
+ // Create a gradient image with std = 1 uncorrelated noise
+ const double kStd = 1;
+ for (int i = 0; i < kWidth * kHeight; ++i) {
+ data_ptr_[0][i] = (uint8_t)(aom_randn(kStd) + (i % kWidth) + 64);
+ data_ptr_[1][i] = (uint8_t)(aom_randn(kStd) + (i % kWidth) + 64);
+ data_ptr_[2][i] = (uint8_t)(aom_randn(kStd) + (i % kWidth) + 64);
+ }
+ flat_blocks_.assign(flat_blocks_.size(), 1);
+ EXPECT_EQ(AOM_NOISE_STATUS_OK,
+ aom_noise_model_update(&model_, data_ptr_, denoised_ptr_, kWidth,
+ kHeight, strides_, chroma_sub_,
+ &flat_blocks_[0], kBlockSize));
+ 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) {
+ data_ptr_[1][i] = (uint8_t)(data_ptr_[1][i] + aom_randn(0.2));
+ data_ptr_[2][i] = (uint8_t)(data_ptr_[2][i] + aom_randn(0.2));
+ }
+ EXPECT_EQ(AOM_NOISE_STATUS_OK,
+ aom_noise_model_update(&model_, data_ptr_, denoised_ptr_, kWidth,
+ kHeight, strides_, chroma_sub_,
+ &flat_blocks_[0], 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(2 * kNumBlocks,
+ model_.combined_state[0].strength_solver.num_equations);
+ EXPECT_EQ(2 * kNumBlocks,
+ model_.combined_state[1].strength_solver.num_equations);
+ EXPECT_EQ(2 * kNumBlocks,
+ model_.combined_state[2].strength_solver.num_equations);
+
+ // Bump up the noise strength on half the image for one channel by a
+ // significant amount.
+ for (int i = 0; i < kWidth * kHeight; ++i) {
+ if (i % kWidth < kWidth / 2)
+ data_ptr_[0][i] = (uint8_t)(data_ptr_[0][i] + aom_randn(0.5));
+ }
+ EXPECT_EQ(AOM_NOISE_STATUS_DIFFERENT_NOISE_TYPE,
+ aom_noise_model_update(&model_, data_ptr_, denoised_ptr_, kWidth,
+ kHeight, strides_, chroma_sub_,
+ &flat_blocks_[0], kBlockSize));
+ // 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_);
+ 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);
+}
+
+TEST_F(NoiseModelUpdateTest, NoiseCoeffsSignalsDifferentNoiseType) {
+ 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 }
+ };
+
+ aom_noise_synth(model_.params.lag, model_.n, model_.coords, kCoeffs[0],
+ noise_ptr_[0], kWidth, kHeight);
+ for (int i = 0; i < kWidth * kHeight; ++i) {
+ data_ptr_[0][i] = (uint8_t)(128 + noise_ptr_[0][i]);
+ }
+ flat_blocks_.assign(flat_blocks_.size(), 1);
+ EXPECT_EQ(AOM_NOISE_STATUS_OK,
+ aom_noise_model_update(&model_, data_ptr_, denoised_ptr_, kWidth,
+ kHeight, strides_, chroma_sub_,
+ &flat_blocks_[0], kBlockSize));
+
+ // Now try with the second set of AR coefficients
+ aom_noise_synth(model_.params.lag, model_.n, model_.coords, kCoeffs[1],
+ noise_ptr_[0], kWidth, kHeight);
+ for (int i = 0; i < kWidth * kHeight; ++i) {
+ data_ptr_[0][i] = (uint8_t)(128 + noise_ptr_[0][i]);
+ }
+ EXPECT_EQ(AOM_NOISE_STATUS_DIFFERENT_NOISE_TYPE,
+ aom_noise_model_update(&model_, data_ptr_, denoised_ptr_, kWidth,
+ kHeight, strides_, chroma_sub_,
+ &flat_blocks_[0], kBlockSize));
+}
+
TEST(NoiseModelGetGrainParameters, TestLagSize) {
aom_film_grain_t film_grain;
for (int lag = 1; lag <= 3; ++lag) {
@@ -757,6 +859,8 @@
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) {
