Fix estimation of correlated noise
Currently, correlation between luma and chroma is estimated
in the "scaled" space. But, during synthesis, the correlated
noise is in the template domain (after IIR filtering) but
before scaling is applied.
Also, change the noise strength estimation to directly fit
to the block noise variance.
Change-Id: Ic8fcadca0dbbf9a22d182dcde4da93a3a1c6a1b9
diff --git a/test/noise_model_test.cc b/test/noise_model_test.cc
index d9869ac..559e053 100644
--- a/test/noise_model_test.cc
+++ b/test/noise_model_test.cc
@@ -468,7 +468,7 @@
T::kBitDepth, T::kUseHighBD };
ASSERT_TRUE(aom_noise_model_init(&model_, params));
- random_.Reset(1071);
+ random_.Reset(100171);
data_.resize(kWidth * kHeight * 3);
denoised_.resize(kWidth * kHeight * 3);
@@ -799,19 +799,21 @@
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 + 0.1) + val)) << shift;
+ ((uint8_t)(this->noise_ptr_[0][i] * (kStd + 0.085) + val)) << shift;
}
EXPECT_EQ(AOM_NOISE_STATUS_OK, this->NoiseModelUpdate());
- 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);
+ 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.
@@ -833,12 +835,16 @@
// 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);
+ 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) {
@@ -994,17 +1000,17 @@
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, 0.25 };
- const double kInputCoeffsCR[] = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+ 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, 32 };
- 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, 64 };
+ 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] = {
@@ -1028,8 +1034,18 @@
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] = 2;
- model.combined_state[2].strength_solver.eqns.x[i] = 4;
+ 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;
@@ -1061,15 +1077,15 @@
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(64, film_grain.scaling_points_cb[0][1]);
- EXPECT_EQ(64, film_grain.scaling_points_cb[1][1]);
+ 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(128, film_grain.scaling_points_cr[0][1]);
- EXPECT_EQ(128, film_grain.scaling_points_cr[1][1]);
+ 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);
