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-rw-r--r--third_party/aom/aom_dsp/noise_model.c1460
1 files changed, 1460 insertions, 0 deletions
diff --git a/third_party/aom/aom_dsp/noise_model.c b/third_party/aom/aom_dsp/noise_model.c
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+++ b/third_party/aom/aom_dsp/noise_model.c
@@ -0,0 +1,1460 @@
+/*
+ * Copyright (c) 2017, 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 <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+
+#include "aom_dsp/aom_dsp_common.h"
+#include "aom_dsp/noise_model.h"
+#include "aom_dsp/noise_util.h"
+#include "aom_mem/aom_mem.h"
+#include "av1/common/common.h"
+#include "av1/encoder/mathutils.h"
+
+#define kLowPolyNumParams 3
+
+static const int kMaxLag = 4;
+
+// Defines a function that can be used to obtain the mean of a block for the
+// provided data type (uint8_t, or uint16_t)
+#define GET_BLOCK_MEAN(INT_TYPE, suffix) \
+ static double get_block_mean_##suffix(const INT_TYPE *data, int w, int h, \
+ int stride, int x_o, int y_o, \
+ int block_size) { \
+ const int max_h = AOMMIN(h - y_o, block_size); \
+ const int max_w = AOMMIN(w - x_o, block_size); \
+ double block_mean = 0; \
+ for (int y = 0; y < max_h; ++y) { \
+ for (int x = 0; x < max_w; ++x) { \
+ block_mean += data[(y_o + y) * stride + x_o + x]; \
+ } \
+ } \
+ return block_mean / (max_w * max_h); \
+ }
+
+GET_BLOCK_MEAN(uint8_t, lowbd);
+GET_BLOCK_MEAN(uint16_t, highbd);
+
+static INLINE double get_block_mean(const uint8_t *data, int w, int h,
+ int stride, int x_o, int y_o,
+ int block_size, int use_highbd) {
+ if (use_highbd)
+ return get_block_mean_highbd((const uint16_t *)data, w, h, stride, x_o, y_o,
+ block_size);
+ return get_block_mean_lowbd(data, w, h, stride, x_o, y_o, block_size);
+}
+
+// Defines a function that can be used to obtain the variance of a block
+// for the provided data type (uint8_t, or uint16_t)
+#define GET_NOISE_VAR(INT_TYPE, suffix) \
+ static double get_noise_var_##suffix( \
+ const INT_TYPE *data, const INT_TYPE *denoised, int stride, int w, \
+ int h, int x_o, int y_o, int block_size_x, int block_size_y) { \
+ const int max_h = AOMMIN(h - y_o, block_size_y); \
+ const int max_w = AOMMIN(w - x_o, block_size_x); \
+ double noise_var = 0; \
+ double noise_mean = 0; \
+ for (int y = 0; y < max_h; ++y) { \
+ for (int x = 0; x < max_w; ++x) { \
+ double noise = (double)data[(y_o + y) * stride + x_o + x] - \
+ denoised[(y_o + y) * stride + x_o + x]; \
+ noise_mean += noise; \
+ noise_var += noise * noise; \
+ } \
+ } \
+ noise_mean /= (max_w * max_h); \
+ return noise_var / (max_w * max_h) - noise_mean * noise_mean; \
+ }
+
+GET_NOISE_VAR(uint8_t, lowbd);
+GET_NOISE_VAR(uint16_t, highbd);
+
+static INLINE double get_noise_var(const uint8_t *data, const uint8_t *denoised,
+ int w, int h, int stride, int x_o, int y_o,
+ int block_size_x, int block_size_y,
+ int use_highbd) {
+ if (use_highbd)
+ return get_noise_var_highbd((const uint16_t *)data,
+ (const uint16_t *)denoised, w, h, stride, x_o,
+ y_o, block_size_x, block_size_y);
+ return get_noise_var_lowbd(data, denoised, w, h, stride, x_o, y_o,
+ block_size_x, block_size_y);
+}
+
+static void equation_system_clear(aom_equation_system_t *eqns) {
+ const int n = eqns->n;
+ memset(eqns->A, 0, sizeof(*eqns->A) * n * n);
+ memset(eqns->x, 0, sizeof(*eqns->x) * n);
+ memset(eqns->b, 0, sizeof(*eqns->b) * n);
+}
+
+static void equation_system_copy(aom_equation_system_t *dst,
+ const aom_equation_system_t *src) {
+ const int n = dst->n;
+ memcpy(dst->A, src->A, sizeof(*dst->A) * n * n);
+ memcpy(dst->x, src->x, sizeof(*dst->x) * n);
+ memcpy(dst->b, src->b, sizeof(*dst->b) * n);
+}
+
+static int equation_system_init(aom_equation_system_t *eqns, int n) {
+ eqns->A = (double *)aom_malloc(sizeof(*eqns->A) * n * n);
+ eqns->b = (double *)aom_malloc(sizeof(*eqns->b) * n);
+ eqns->x = (double *)aom_malloc(sizeof(*eqns->x) * n);
+ eqns->n = n;
+ if (!eqns->A || !eqns->b || !eqns->x) {
+ fprintf(stderr, "Failed to allocate system of equations of size %d\n", n);
+ aom_free(eqns->A);
+ aom_free(eqns->b);
+ aom_free(eqns->x);
+ memset(eqns, 0, sizeof(*eqns));
+ return 0;
+ }
+ equation_system_clear(eqns);
+ return 1;
+}
+
+static int equation_system_solve(aom_equation_system_t *eqns) {
+ const int n = eqns->n;
+ double *b = (double *)aom_malloc(sizeof(*b) * n);
+ double *A = (double *)aom_malloc(sizeof(*A) * n * n);
+ int ret = 0;
+ if (A == NULL || b == NULL) {
+ fprintf(stderr, "Unable to allocate temp values of size %dx%d\n", n, n);
+ aom_free(b);
+ aom_free(A);
+ return 0;
+ }
+ memcpy(A, eqns->A, sizeof(*eqns->A) * n * n);
+ memcpy(b, eqns->b, sizeof(*eqns->b) * n);
+ ret = linsolve(n, A, eqns->n, b, eqns->x);
+ aom_free(b);
+ aom_free(A);
+
+ if (ret == 0) {
+ return 0;
+ }
+ return 1;
+}
+
+static void equation_system_add(aom_equation_system_t *dest,
+ aom_equation_system_t *src) {
+ const int n = dest->n;
+ int i, j;
+ for (i = 0; i < n; ++i) {
+ for (j = 0; j < n; ++j) {
+ dest->A[i * n + j] += src->A[i * n + j];
+ }
+ dest->b[i] += src->b[i];
+ }
+}
+
+static void equation_system_free(aom_equation_system_t *eqns) {
+ if (!eqns) return;
+ aom_free(eqns->A);
+ aom_free(eqns->b);
+ aom_free(eqns->x);
+ memset(eqns, 0, sizeof(*eqns));
+}
+
+static void noise_strength_solver_clear(aom_noise_strength_solver_t *solver) {
+ equation_system_clear(&solver->eqns);
+ solver->num_equations = 0;
+ solver->total = 0;
+}
+
+static void noise_strength_solver_add(aom_noise_strength_solver_t *dest,
+ aom_noise_strength_solver_t *src) {
+ equation_system_add(&dest->eqns, &src->eqns);
+ dest->num_equations += src->num_equations;
+ dest->total += src->total;
+}
+
+// Return the number of coefficients required for the given parameters
+static int num_coeffs(const aom_noise_model_params_t params) {
+ const int n = 2 * params.lag + 1;
+ switch (params.shape) {
+ case AOM_NOISE_SHAPE_DIAMOND: return params.lag * (params.lag + 1);
+ case AOM_NOISE_SHAPE_SQUARE: return (n * n) / 2;
+ }
+ return 0;
+}
+
+static int noise_state_init(aom_noise_state_t *state, int n, int bit_depth) {
+ const int kNumBins = 20;
+ if (!equation_system_init(&state->eqns, n)) {
+ fprintf(stderr, "Failed initialization noise state with size %d\n", n);
+ return 0;
+ }
+ state->ar_gain = 1.0;
+ state->num_observations = 0;
+ return aom_noise_strength_solver_init(&state->strength_solver, kNumBins,
+ bit_depth);
+}
+
+static void set_chroma_coefficient_fallback_soln(aom_equation_system_t *eqns) {
+ const double kTolerance = 1e-6;
+ const int last = eqns->n - 1;
+ // Set all of the AR coefficients to zero, but try to solve for correlation
+ // with the luma channel
+ memset(eqns->x, 0, sizeof(*eqns->x) * eqns->n);
+ if (fabs(eqns->A[last * eqns->n + last]) > kTolerance) {
+ eqns->x[last] = eqns->b[last] / eqns->A[last * eqns->n + last];
+ }
+}
+
+int aom_noise_strength_lut_init(aom_noise_strength_lut_t *lut, int num_points) {
+ if (!lut) return 0;
+ lut->points = (double(*)[2])aom_malloc(num_points * sizeof(*lut->points));
+ if (!lut->points) return 0;
+ lut->num_points = num_points;
+ memset(lut->points, 0, sizeof(*lut->points) * num_points);
+ return 1;
+}
+
+void aom_noise_strength_lut_free(aom_noise_strength_lut_t *lut) {
+ if (!lut) return;
+ aom_free(lut->points);
+ memset(lut, 0, sizeof(*lut));
+}
+
+double aom_noise_strength_lut_eval(const aom_noise_strength_lut_t *lut,
+ double x) {
+ int i = 0;
+ // Constant extrapolation for x < x_0.
+ if (x < lut->points[0][0]) return lut->points[0][1];
+ for (i = 0; i < lut->num_points - 1; ++i) {
+ if (x >= lut->points[i][0] && x <= lut->points[i + 1][0]) {
+ const double a =
+ (x - lut->points[i][0]) / (lut->points[i + 1][0] - lut->points[i][0]);
+ return lut->points[i + 1][1] * a + lut->points[i][1] * (1.0 - a);
+ }
+ }
+ // Constant extrapolation for x > x_{n-1}
+ return lut->points[lut->num_points - 1][1];
+}
+
+static double noise_strength_solver_get_bin_index(
+ const aom_noise_strength_solver_t *solver, double value) {
+ const double val =
+ fclamp(value, solver->min_intensity, solver->max_intensity);
+ const double range = solver->max_intensity - solver->min_intensity;
+ return (solver->num_bins - 1) * (val - solver->min_intensity) / range;
+}
+
+static double noise_strength_solver_get_value(
+ const aom_noise_strength_solver_t *solver, double x) {
+ const double bin = noise_strength_solver_get_bin_index(solver, x);
+ const int bin_i0 = (int)floor(bin);
+ const int bin_i1 = AOMMIN(solver->num_bins - 1, bin_i0 + 1);
+ const double a = bin - bin_i0;
+ return (1.0 - a) * solver->eqns.x[bin_i0] + a * solver->eqns.x[bin_i1];
+}
+
+void aom_noise_strength_solver_add_measurement(
+ aom_noise_strength_solver_t *solver, double block_mean, double noise_std) {
+ const double bin = noise_strength_solver_get_bin_index(solver, block_mean);
+ const int bin_i0 = (int)floor(bin);
+ const int bin_i1 = AOMMIN(solver->num_bins - 1, bin_i0 + 1);
+ const double a = bin - bin_i0;
+ const int n = solver->num_bins;
+ solver->eqns.A[bin_i0 * n + bin_i0] += (1.0 - a) * (1.0 - a);
+ solver->eqns.A[bin_i1 * n + bin_i0] += a * (1.0 - a);
+ solver->eqns.A[bin_i1 * n + bin_i1] += a * a;
+ solver->eqns.A[bin_i0 * n + bin_i1] += a * (1.0 - a);
+ solver->eqns.b[bin_i0] += (1.0 - a) * noise_std;
+ solver->eqns.b[bin_i1] += a * noise_std;
+ solver->total += noise_std;
+ solver->num_equations++;
+}
+
+int aom_noise_strength_solver_solve(aom_noise_strength_solver_t *solver) {
+ // Add regularization proportional to the number of constraints
+ const int n = solver->num_bins;
+ const double kAlpha = 2.0 * (double)(solver->num_equations) / n;
+ int result = 0;
+ double mean = 0;
+
+ // Do this in a non-destructive manner so it is not confusing to the caller
+ double *old_A = solver->eqns.A;
+ double *A = (double *)aom_malloc(sizeof(*A) * n * n);
+ if (!A) {
+ fprintf(stderr, "Unable to allocate copy of A\n");
+ return 0;
+ }
+ memcpy(A, old_A, sizeof(*A) * n * n);
+
+ for (int i = 0; i < n; ++i) {
+ const int i_lo = AOMMAX(0, i - 1);
+ const int i_hi = AOMMIN(n - 1, i + 1);
+ A[i * n + i_lo] -= kAlpha;
+ A[i * n + i] += 2 * kAlpha;
+ A[i * n + i_hi] -= kAlpha;
+ }
+
+ // Small regularization to give average noise strength
+ mean = solver->total / solver->num_equations;
+ for (int i = 0; i < n; ++i) {
+ A[i * n + i] += 1.0 / 8192.;
+ solver->eqns.b[i] += mean / 8192.;
+ }
+ solver->eqns.A = A;
+ result = equation_system_solve(&solver->eqns);
+ solver->eqns.A = old_A;
+
+ aom_free(A);
+ return result;
+}
+
+int aom_noise_strength_solver_init(aom_noise_strength_solver_t *solver,
+ int num_bins, int bit_depth) {
+ if (!solver) return 0;
+ memset(solver, 0, sizeof(*solver));
+ solver->num_bins = num_bins;
+ solver->min_intensity = 0;
+ solver->max_intensity = (1 << bit_depth) - 1;
+ solver->total = 0;
+ solver->num_equations = 0;
+ return equation_system_init(&solver->eqns, num_bins);
+}
+
+void aom_noise_strength_solver_free(aom_noise_strength_solver_t *solver) {
+ if (!solver) return;
+ equation_system_free(&solver->eqns);
+}
+
+double aom_noise_strength_solver_get_center(
+ const aom_noise_strength_solver_t *solver, int i) {
+ const double range = solver->max_intensity - solver->min_intensity;
+ const int n = solver->num_bins;
+ return ((double)i) / (n - 1) * range + solver->min_intensity;
+}
+
+// Computes the residual if a point were to be removed from the lut. This is
+// calculated as the area between the output of the solver and the line segment
+// that would be formed between [x_{i - 1}, x_{i + 1}).
+static void update_piecewise_linear_residual(
+ const aom_noise_strength_solver_t *solver,
+ const aom_noise_strength_lut_t *lut, double *residual, int start, int end) {
+ const double dx = 255. / solver->num_bins;
+ for (int i = AOMMAX(start, 1); i < AOMMIN(end, lut->num_points - 1); ++i) {
+ const int lower = AOMMAX(0, (int)floor(noise_strength_solver_get_bin_index(
+ solver, lut->points[i - 1][0])));
+ const int upper = AOMMIN(solver->num_bins - 1,
+ (int)ceil(noise_strength_solver_get_bin_index(
+ solver, lut->points[i + 1][0])));
+ double r = 0;
+ for (int j = lower; j <= upper; ++j) {
+ const double x = aom_noise_strength_solver_get_center(solver, j);
+ if (x < lut->points[i - 1][0]) continue;
+ if (x >= lut->points[i + 1][0]) continue;
+ const double y = solver->eqns.x[j];
+ const double a = (x - lut->points[i - 1][0]) /
+ (lut->points[i + 1][0] - lut->points[i - 1][0]);
+ const double estimate_y =
+ lut->points[i - 1][1] * (1.0 - a) + lut->points[i + 1][1] * a;
+ r += fabs(y - estimate_y);
+ }
+ residual[i] = r * dx;
+ }
+}
+
+int aom_noise_strength_solver_fit_piecewise(
+ const aom_noise_strength_solver_t *solver, int max_output_points,
+ aom_noise_strength_lut_t *lut) {
+ // The tolerance is normalized to be give consistent results between
+ // different bit-depths.
+ const double kTolerance = solver->max_intensity * 0.00625 / 255.0;
+ if (!aom_noise_strength_lut_init(lut, solver->num_bins)) {
+ fprintf(stderr, "Failed to init lut\n");
+ return 0;
+ }
+ for (int i = 0; i < solver->num_bins; ++i) {
+ lut->points[i][0] = aom_noise_strength_solver_get_center(solver, i);
+ lut->points[i][1] = solver->eqns.x[i];
+ }
+ if (max_output_points < 0) {
+ max_output_points = solver->num_bins;
+ }
+
+ double *residual = aom_malloc(solver->num_bins * sizeof(*residual));
+ memset(residual, 0, sizeof(*residual) * solver->num_bins);
+
+ update_piecewise_linear_residual(solver, lut, residual, 0, solver->num_bins);
+
+ // Greedily remove points if there are too many or if it doesn't hurt local
+ // approximation (never remove the end points)
+ while (lut->num_points > 2) {
+ int min_index = 1;
+ for (int j = 1; j < lut->num_points - 1; ++j) {
+ if (residual[j] < residual[min_index]) {
+ min_index = j;
+ }
+ }
+ const double dx =
+ lut->points[min_index + 1][0] - lut->points[min_index - 1][0];
+ const double avg_residual = residual[min_index] / dx;
+ if (lut->num_points <= max_output_points && avg_residual > kTolerance) {
+ break;
+ }
+
+ const int num_remaining = lut->num_points - min_index - 1;
+ memmove(lut->points + min_index, lut->points + min_index + 1,
+ sizeof(lut->points[0]) * num_remaining);
+ lut->num_points--;
+
+ update_piecewise_linear_residual(solver, lut, residual, min_index - 1,
+ min_index + 1);
+ }
+ aom_free(residual);
+ return 1;
+}
+
+int aom_flat_block_finder_init(aom_flat_block_finder_t *block_finder,
+ int block_size, int bit_depth, int use_highbd) {
+ const int n = block_size * block_size;
+ aom_equation_system_t eqns;
+ double *AtA_inv = 0;
+ double *A = 0;
+ int x = 0, y = 0, i = 0, j = 0;
+ if (!equation_system_init(&eqns, kLowPolyNumParams)) {
+ fprintf(stderr, "Failed to init equation system for block_size=%d\n",
+ block_size);
+ return 0;
+ }
+
+ AtA_inv = (double *)aom_malloc(kLowPolyNumParams * kLowPolyNumParams *
+ sizeof(*AtA_inv));
+ A = (double *)aom_malloc(kLowPolyNumParams * n * sizeof(*A));
+ if (AtA_inv == NULL || A == NULL) {
+ fprintf(stderr, "Failed to alloc A or AtA_inv for block_size=%d\n",
+ block_size);
+ aom_free(AtA_inv);
+ aom_free(A);
+ equation_system_free(&eqns);
+ return 0;
+ }
+
+ block_finder->A = A;
+ block_finder->AtA_inv = AtA_inv;
+ block_finder->block_size = block_size;
+ block_finder->normalization = (1 << bit_depth) - 1;
+ block_finder->use_highbd = use_highbd;
+
+ for (y = 0; y < block_size; ++y) {
+ const double yd = ((double)y - block_size / 2.) / (block_size / 2.);
+ for (x = 0; x < block_size; ++x) {
+ const double xd = ((double)x - block_size / 2.) / (block_size / 2.);
+ const double coords[3] = { yd, xd, 1 };
+ const int row = y * block_size + x;
+ A[kLowPolyNumParams * row + 0] = yd;
+ A[kLowPolyNumParams * row + 1] = xd;
+ A[kLowPolyNumParams * row + 2] = 1;
+
+ for (i = 0; i < kLowPolyNumParams; ++i) {
+ for (j = 0; j < kLowPolyNumParams; ++j) {
+ eqns.A[kLowPolyNumParams * i + j] += coords[i] * coords[j];
+ }
+ }
+ }
+ }
+
+ // Lazy inverse using existing equation solver.
+ for (i = 0; i < kLowPolyNumParams; ++i) {
+ memset(eqns.b, 0, sizeof(*eqns.b) * kLowPolyNumParams);
+ eqns.b[i] = 1;
+ equation_system_solve(&eqns);
+
+ for (j = 0; j < kLowPolyNumParams; ++j) {
+ AtA_inv[j * kLowPolyNumParams + i] = eqns.x[j];
+ }
+ }
+ equation_system_free(&eqns);
+ return 1;
+}
+
+void aom_flat_block_finder_free(aom_flat_block_finder_t *block_finder) {
+ if (!block_finder) return;
+ aom_free(block_finder->A);
+ aom_free(block_finder->AtA_inv);
+ memset(block_finder, 0, sizeof(*block_finder));
+}
+
+void aom_flat_block_finder_extract_block(
+ const aom_flat_block_finder_t *block_finder, const uint8_t *const data,
+ int w, int h, int stride, int offsx, int offsy, double *plane,
+ double *block) {
+ const int block_size = block_finder->block_size;
+ const int n = block_size * block_size;
+ const double *A = block_finder->A;
+ const double *AtA_inv = block_finder->AtA_inv;
+ double plane_coords[kLowPolyNumParams];
+ double AtA_inv_b[kLowPolyNumParams];
+ int xi, yi, i;
+
+ if (block_finder->use_highbd) {
+ const uint16_t *const data16 = (const uint16_t *const)data;
+ for (yi = 0; yi < block_size; ++yi) {
+ const int y = clamp(offsy + yi, 0, h - 1);
+ for (xi = 0; xi < block_size; ++xi) {
+ const int x = clamp(offsx + xi, 0, w - 1);
+ block[yi * block_size + xi] =
+ ((double)data16[y * stride + x]) / block_finder->normalization;
+ }
+ }
+ } else {
+ for (yi = 0; yi < block_size; ++yi) {
+ const int y = clamp(offsy + yi, 0, h - 1);
+ for (xi = 0; xi < block_size; ++xi) {
+ const int x = clamp(offsx + xi, 0, w - 1);
+ block[yi * block_size + xi] =
+ ((double)data[y * stride + x]) / block_finder->normalization;
+ }
+ }
+ }
+ multiply_mat(block, A, AtA_inv_b, 1, n, kLowPolyNumParams);
+ multiply_mat(AtA_inv, AtA_inv_b, plane_coords, kLowPolyNumParams,
+ kLowPolyNumParams, 1);
+ multiply_mat(A, plane_coords, plane, n, kLowPolyNumParams, 1);
+
+ for (i = 0; i < n; ++i) {
+ block[i] -= plane[i];
+ }
+}
+
+typedef struct {
+ int index;
+ float score;
+} index_and_score_t;
+
+static int compare_scores(const void *a, const void *b) {
+ const float diff =
+ ((index_and_score_t *)a)->score - ((index_and_score_t *)b)->score;
+ if (diff < 0)
+ return -1;
+ else if (diff > 0)
+ return 1;
+ return 0;
+}
+
+int aom_flat_block_finder_run(const aom_flat_block_finder_t *block_finder,
+ const uint8_t *const data, int w, int h,
+ int stride, uint8_t *flat_blocks) {
+ // The gradient-based features used in this code are based on:
+ // A. Kokaram, D. Kelly, H. Denman and A. Crawford, "Measuring noise
+ // correlation for improved video denoising," 2012 19th, ICIP.
+ // The thresholds are more lenient to allow for correct grain modeling
+ // if extreme cases.
+ const int block_size = block_finder->block_size;
+ const int n = block_size * block_size;
+ const double kTraceThreshold = 0.15 / (32 * 32);
+ const double kRatioThreshold = 1.25;
+ const double kNormThreshold = 0.08 / (32 * 32);
+ const double kVarThreshold = 0.005 / (double)n;
+ const int num_blocks_w = (w + block_size - 1) / block_size;
+ const int num_blocks_h = (h + block_size - 1) / block_size;
+ int num_flat = 0;
+ int bx = 0, by = 0;
+ double *plane = (double *)aom_malloc(n * sizeof(*plane));
+ double *block = (double *)aom_malloc(n * sizeof(*block));
+ index_and_score_t *scores = (index_and_score_t *)aom_malloc(
+ num_blocks_w * num_blocks_h * sizeof(*scores));
+ if (plane == NULL || block == NULL || scores == NULL) {
+ fprintf(stderr, "Failed to allocate memory for block of size %d\n", n);
+ aom_free(plane);
+ aom_free(block);
+ aom_free(scores);
+ return -1;
+ }
+
+#ifdef NOISE_MODEL_LOG_SCORE
+ fprintf(stderr, "score = [");
+#endif
+ for (by = 0; by < num_blocks_h; ++by) {
+ for (bx = 0; bx < num_blocks_w; ++bx) {
+ // Compute gradient covariance matrix.
+ double Gxx = 0, Gxy = 0, Gyy = 0;
+ double var = 0;
+ double mean = 0;
+ int xi, yi;
+ aom_flat_block_finder_extract_block(block_finder, data, w, h, stride,
+ bx * block_size, by * block_size,
+ plane, block);
+
+ for (yi = 1; yi < block_size - 1; ++yi) {
+ for (xi = 1; xi < block_size - 1; ++xi) {
+ const double gx = (block[yi * block_size + xi + 1] -
+ block[yi * block_size + xi - 1]) /
+ 2;
+ const double gy = (block[yi * block_size + xi + block_size] -
+ block[yi * block_size + xi - block_size]) /
+ 2;
+ Gxx += gx * gx;
+ Gxy += gx * gy;
+ Gyy += gy * gy;
+
+ mean += block[yi * block_size + xi];
+ var += block[yi * block_size + xi] * block[yi * block_size + xi];
+ }
+ }
+ mean /= (block_size - 2) * (block_size - 2);
+
+ // Normalize gradients by block_size.
+ Gxx /= ((block_size - 2) * (block_size - 2));
+ Gxy /= ((block_size - 2) * (block_size - 2));
+ Gyy /= ((block_size - 2) * (block_size - 2));
+ var = var / ((block_size - 2) * (block_size - 2)) - mean * mean;
+
+ {
+ const double trace = Gxx + Gyy;
+ const double det = Gxx * Gyy - Gxy * Gxy;
+ const double e1 = (trace + sqrt(trace * trace - 4 * det)) / 2.;
+ const double e2 = (trace - sqrt(trace * trace - 4 * det)) / 2.;
+ const double norm = e1; // Spectral norm
+ const double ratio = (e1 / AOMMAX(e2, 1e-6));
+ const int is_flat = (trace < kTraceThreshold) &&
+ (ratio < kRatioThreshold) &&
+ (norm < kNormThreshold) && (var > kVarThreshold);
+ // The following weights are used to combine the above features to give
+ // a sigmoid score for flatness. If the input was normalized to [0,100]
+ // the magnitude of these values would be close to 1 (e.g., weights
+ // corresponding to variance would be a factor of 10000x smaller).
+ // The weights are given in the following order:
+ // [{var}, {ratio}, {trace}, {norm}, offset]
+ // with one of the most discriminative being simply the variance.
+ const double weights[5] = { -6682, -0.2056, 13087, -12434, 2.5694 };
+ const float score =
+ (float)(1.0 / (1 + exp(-(weights[0] * var + weights[1] * ratio +
+ weights[2] * trace + weights[3] * norm +
+ weights[4]))));
+ flat_blocks[by * num_blocks_w + bx] = is_flat ? 255 : 0;
+ scores[by * num_blocks_w + bx].score = var > kVarThreshold ? score : 0;
+ scores[by * num_blocks_w + bx].index = by * num_blocks_w + bx;
+#ifdef NOISE_MODEL_LOG_SCORE
+ fprintf(stderr, "%g %g %g %g %g %d ", score, var, ratio, trace, norm,
+ is_flat);
+#endif
+ num_flat += is_flat;
+ }
+ }
+#ifdef NOISE_MODEL_LOG_SCORE
+ fprintf(stderr, "\n");
+#endif
+ }
+#ifdef NOISE_MODEL_LOG_SCORE
+ fprintf(stderr, "];\n");
+#endif
+ // Find the top-scored blocks (most likely to be flat) and set the flat blocks
+ // be the union of the thresholded results and the top 10th percentile of the
+ // scored results.
+ qsort(scores, num_blocks_w * num_blocks_h, sizeof(*scores), &compare_scores);
+ const int top_nth_percentile = num_blocks_w * num_blocks_h * 90 / 100;
+ const float score_threshold = scores[top_nth_percentile].score;
+ for (int i = 0; i < num_blocks_w * num_blocks_h; ++i) {
+ if (scores[i].score >= score_threshold) {
+ num_flat += flat_blocks[scores[i].index] == 0;
+ flat_blocks[scores[i].index] |= 1;
+ }
+ }
+ aom_free(block);
+ aom_free(plane);
+ aom_free(scores);
+ return num_flat;
+}
+
+int aom_noise_model_init(aom_noise_model_t *model,
+ const aom_noise_model_params_t params) {
+ const int n = num_coeffs(params);
+ const int lag = params.lag;
+ const int bit_depth = params.bit_depth;
+ int x = 0, y = 0, i = 0, c = 0;
+
+ memset(model, 0, sizeof(*model));
+ if (params.lag < 1) {
+ fprintf(stderr, "Invalid noise param: lag = %d must be >= 1\n", params.lag);
+ return 0;
+ }
+ if (params.lag > kMaxLag) {
+ fprintf(stderr, "Invalid noise param: lag = %d must be <= %d\n", params.lag,
+ kMaxLag);
+ return 0;
+ }
+
+ memcpy(&model->params, &params, sizeof(params));
+ for (c = 0; c < 3; ++c) {
+ if (!noise_state_init(&model->combined_state[c], n + (c > 0), bit_depth)) {
+ fprintf(stderr, "Failed to allocate noise state for channel %d\n", c);
+ aom_noise_model_free(model);
+ return 0;
+ }
+ if (!noise_state_init(&model->latest_state[c], n + (c > 0), bit_depth)) {
+ fprintf(stderr, "Failed to allocate noise state for channel %d\n", c);
+ aom_noise_model_free(model);
+ return 0;
+ }
+ }
+ model->n = n;
+ model->coords = (int(*)[2])aom_malloc(sizeof(*model->coords) * n);
+
+ for (y = -lag; y <= 0; ++y) {
+ const int max_x = y == 0 ? -1 : lag;
+ for (x = -lag; x <= max_x; ++x) {
+ switch (params.shape) {
+ case AOM_NOISE_SHAPE_DIAMOND:
+ if (abs(x) <= y + lag) {
+ model->coords[i][0] = x;
+ model->coords[i][1] = y;
+ ++i;
+ }
+ break;
+ case AOM_NOISE_SHAPE_SQUARE:
+ model->coords[i][0] = x;
+ model->coords[i][1] = y;
+ ++i;
+ break;
+ default:
+ fprintf(stderr, "Invalid shape\n");
+ aom_noise_model_free(model);
+ return 0;
+ }
+ }
+ }
+ assert(i == n);
+ return 1;
+}
+
+void aom_noise_model_free(aom_noise_model_t *model) {
+ int c = 0;
+ if (!model) return;
+
+ aom_free(model->coords);
+ for (c = 0; c < 3; ++c) {
+ equation_system_free(&model->latest_state[c].eqns);
+ equation_system_free(&model->combined_state[c].eqns);
+
+ equation_system_free(&model->latest_state[c].strength_solver.eqns);
+ equation_system_free(&model->combined_state[c].strength_solver.eqns);
+ }
+ memset(model, 0, sizeof(*model));
+}
+
+// Extracts the neighborhood defined by coords around point (x, y) from
+// the difference between the data and denoised images. Also extracts the
+// entry (possibly downsampled) for (x, y) in the alt_data (e.g., luma).
+#define EXTRACT_AR_ROW(INT_TYPE, suffix) \
+ static double extract_ar_row_##suffix( \
+ int(*coords)[2], int num_coords, const INT_TYPE *const data, \
+ const INT_TYPE *const denoised, int stride, int sub_log2[2], \
+ const INT_TYPE *const alt_data, const INT_TYPE *const alt_denoised, \
+ int alt_stride, int x, int y, double *buffer) { \
+ for (int i = 0; i < num_coords; ++i) { \
+ const int x_i = x + coords[i][0], y_i = y + coords[i][1]; \
+ buffer[i] = \
+ (double)data[y_i * stride + x_i] - denoised[y_i * stride + x_i]; \
+ } \
+ const double val = \
+ (double)data[y * stride + x] - denoised[y * stride + x]; \
+ \
+ if (alt_data && alt_denoised) { \
+ double avg_data = 0, avg_denoised = 0; \
+ int num_samples = 0; \
+ for (int dy_i = 0; dy_i < (1 << sub_log2[1]); dy_i++) { \
+ const int y_up = (y << sub_log2[1]) + dy_i; \
+ for (int dx_i = 0; dx_i < (1 << sub_log2[0]); dx_i++) { \
+ const int x_up = (x << sub_log2[0]) + dx_i; \
+ avg_data += alt_data[y_up * alt_stride + x_up]; \
+ avg_denoised += alt_denoised[y_up * alt_stride + x_up]; \
+ num_samples++; \
+ } \
+ } \
+ buffer[num_coords] = (avg_data - avg_denoised) / num_samples; \
+ } \
+ return val; \
+ }
+
+EXTRACT_AR_ROW(uint8_t, lowbd);
+EXTRACT_AR_ROW(uint16_t, highbd);
+
+static int add_block_observations(
+ aom_noise_model_t *noise_model, int c, const uint8_t *const data,
+ const uint8_t *const denoised, int w, int h, int stride, int sub_log2[2],
+ const uint8_t *const alt_data, const uint8_t *const alt_denoised,
+ int alt_stride, const uint8_t *const flat_blocks, int block_size,
+ int num_blocks_w, int num_blocks_h) {
+ const int lag = noise_model->params.lag;
+ const int num_coords = noise_model->n;
+ const double normalization = (1 << noise_model->params.bit_depth) - 1;
+ double *A = noise_model->latest_state[c].eqns.A;
+ double *b = noise_model->latest_state[c].eqns.b;
+ double *buffer = (double *)aom_malloc(sizeof(*buffer) * (num_coords + 1));
+ const int n = noise_model->latest_state[c].eqns.n;
+
+ if (!buffer) {
+ fprintf(stderr, "Unable to allocate buffer of size %d\n", num_coords + 1);
+ return 0;
+ }
+ for (int by = 0; by < num_blocks_h; ++by) {
+ const int y_o = by * (block_size >> sub_log2[1]);
+ for (int bx = 0; bx < num_blocks_w; ++bx) {
+ const int x_o = bx * (block_size >> sub_log2[0]);
+ if (!flat_blocks[by * num_blocks_w + bx]) {
+ continue;
+ }
+ int y_start =
+ (by > 0 && flat_blocks[(by - 1) * num_blocks_w + bx]) ? 0 : lag;
+ int x_start =
+ (bx > 0 && flat_blocks[by * num_blocks_w + bx - 1]) ? 0 : lag;
+ int y_end = AOMMIN((h >> sub_log2[1]) - by * (block_size >> sub_log2[1]),
+ block_size >> sub_log2[1]);
+ int x_end = AOMMIN(
+ (w >> sub_log2[0]) - bx * (block_size >> sub_log2[0]) - lag,
+ (bx + 1 < num_blocks_w && flat_blocks[by * num_blocks_w + bx + 1])
+ ? (block_size >> sub_log2[0])
+ : ((block_size >> sub_log2[0]) - lag));
+ for (int y = y_start; y < y_end; ++y) {
+ for (int x = x_start; x < x_end; ++x) {
+ const double val =
+ noise_model->params.use_highbd
+ ? extract_ar_row_highbd(noise_model->coords, num_coords,
+ (const uint16_t *const)data,
+ (const uint16_t *const)denoised,
+ stride, sub_log2,
+ (const uint16_t *const)alt_data,
+ (const uint16_t *const)alt_denoised,
+ alt_stride, x + x_o, y + y_o, buffer)
+ : extract_ar_row_lowbd(noise_model->coords, num_coords, data,
+ denoised, stride, sub_log2, alt_data,
+ alt_denoised, alt_stride, x + x_o,
+ y + y_o, buffer);
+ for (int i = 0; i < n; ++i) {
+ for (int j = 0; j < n; ++j) {
+ A[i * n + j] +=
+ (buffer[i] * buffer[j]) / (normalization * normalization);
+ }
+ b[i] += (buffer[i] * val) / (normalization * normalization);
+ }
+ noise_model->latest_state[c].num_observations++;
+ }
+ }
+ }
+ }
+ aom_free(buffer);
+ return 1;
+}
+
+static void add_noise_std_observations(
+ aom_noise_model_t *noise_model, int c, const double *coeffs,
+ const uint8_t *const data, const uint8_t *const denoised, int w, int h,
+ int stride, int sub_log2[2], const uint8_t *const alt_data, int alt_stride,
+ const uint8_t *const flat_blocks, int block_size, int num_blocks_w,
+ int num_blocks_h) {
+ const int num_coords = noise_model->n;
+ aom_noise_strength_solver_t *noise_strength_solver =
+ &noise_model->latest_state[c].strength_solver;
+
+ const aom_noise_strength_solver_t *noise_strength_luma =
+ &noise_model->latest_state[0].strength_solver;
+ const double luma_gain = noise_model->latest_state[0].ar_gain;
+ const double noise_gain = noise_model->latest_state[c].ar_gain;
+ for (int by = 0; by < num_blocks_h; ++by) {
+ const int y_o = by * (block_size >> sub_log2[1]);
+ for (int bx = 0; bx < num_blocks_w; ++bx) {
+ const int x_o = bx * (block_size >> sub_log2[0]);
+ if (!flat_blocks[by * num_blocks_w + bx]) {
+ continue;
+ }
+ const int num_samples_h =
+ AOMMIN((h >> sub_log2[1]) - by * (block_size >> sub_log2[1]),
+ block_size >> sub_log2[1]);
+ const int num_samples_w =
+ AOMMIN((w >> sub_log2[0]) - bx * (block_size >> sub_log2[0]),
+ (block_size >> sub_log2[0]));
+ // Make sure that we have a reasonable amount of samples to consider the
+ // block
+ if (num_samples_w * num_samples_h > block_size) {
+ const double block_mean = get_block_mean(
+ alt_data ? alt_data : data, w, h, alt_data ? alt_stride : stride,
+ x_o << sub_log2[0], y_o << sub_log2[1], block_size,
+ noise_model->params.use_highbd);
+ const double noise_var = get_noise_var(
+ data, denoised, stride, w >> sub_log2[0], h >> sub_log2[1], x_o,
+ y_o, block_size >> sub_log2[0], block_size >> sub_log2[1],
+ noise_model->params.use_highbd);
+ // We want to remove the part of the noise that came from being
+ // correlated with luma. Note that the noise solver for luma must
+ // have already been run.
+ const double luma_strength =
+ c > 0 ? luma_gain * noise_strength_solver_get_value(
+ noise_strength_luma, block_mean)
+ : 0;
+ const double corr = c > 0 ? coeffs[num_coords] : 0;
+ // Chroma noise:
+ // N(0, noise_var) = N(0, uncorr_var) + corr * N(0, luma_strength^2)
+ // The uncorrelated component:
+ // uncorr_var = noise_var - (corr * luma_strength)^2
+ // But don't allow fully correlated noise (hence the max), since the
+ // synthesis cannot model it.
+ const double uncorr_std = sqrt(
+ AOMMAX(noise_var / 16, noise_var - pow(corr * luma_strength, 2)));
+ // After we've removed correlation with luma, undo the gain that will
+ // come from running the IIR filter.
+ const double adjusted_strength = uncorr_std / noise_gain;
+ aom_noise_strength_solver_add_measurement(
+ noise_strength_solver, block_mean, adjusted_strength);
+ }
+ }
+ }
+}
+
+// Return true if the noise estimate appears to be different from the combined
+// (multi-frame) estimate. The difference is measured by checking whether the
+// AR coefficients have diverged (using a threshold on normalized cross
+// correlation), or whether the noise strength has changed.
+static int is_noise_model_different(aom_noise_model_t *const noise_model) {
+ // These thresholds are kind of arbitrary and will likely need further tuning
+ // (or exported as parameters). The threshold on noise strength is a weighted
+ // difference between the noise strength histograms
+ const double kCoeffThreshold = 0.9;
+ const double kStrengthThreshold =
+ 0.005 * (1 << (noise_model->params.bit_depth - 8));
+ for (int c = 0; c < 1; ++c) {
+ const double corr =
+ aom_normalized_cross_correlation(noise_model->latest_state[c].eqns.x,
+ noise_model->combined_state[c].eqns.x,
+ noise_model->combined_state[c].eqns.n);
+ if (corr < kCoeffThreshold) return 1;
+
+ const double dx =
+ 1.0 / noise_model->latest_state[c].strength_solver.num_bins;
+
+ const aom_equation_system_t *latest_eqns =
+ &noise_model->latest_state[c].strength_solver.eqns;
+ const aom_equation_system_t *combined_eqns =
+ &noise_model->combined_state[c].strength_solver.eqns;
+ double diff = 0;
+ double total_weight = 0;
+ for (int j = 0; j < latest_eqns->n; ++j) {
+ double weight = 0;
+ for (int i = 0; i < latest_eqns->n; ++i) {
+ weight += latest_eqns->A[i * latest_eqns->n + j];
+ }
+ weight = sqrt(weight);
+ diff += weight * fabs(latest_eqns->x[j] - combined_eqns->x[j]);
+ total_weight += weight;
+ }
+ if (diff * dx / total_weight > kStrengthThreshold) return 1;
+ }
+ return 0;
+}
+
+static int ar_equation_system_solve(aom_noise_state_t *state, int is_chroma) {
+ const int ret = equation_system_solve(&state->eqns);
+ state->ar_gain = 1.0;
+ if (!ret) return ret;
+
+ // Update the AR gain from the equation system as it will be used to fit
+ // the noise strength as a function of intensity. In the Yule-Walker
+ // equations, the diagonal should be the variance of the correlated noise.
+ // In the case of the least squares estimate, there will be some variability
+ // in the diagonal. So use the mean of the diagonal as the estimate of
+ // overall variance (this works for least squares or Yule-Walker formulation).
+ double var = 0;
+ const int n = state->eqns.n;
+ for (int i = 0; i < (state->eqns.n - is_chroma); ++i) {
+ var += state->eqns.A[i * n + i] / state->num_observations;
+ }
+ var /= (n - is_chroma);
+
+ // Keep track of E(Y^2) = <b, x> + E(X^2)
+ // In the case that we are using chroma and have an estimate of correlation
+ // with luma we adjust that estimate slightly to remove the correlated bits by
+ // subtracting out the last column of a scaled by our correlation estimate
+ // from b. E(y^2) = <b - A(:, end)*x(end), x>
+ double sum_covar = 0;
+ for (int i = 0; i < state->eqns.n - is_chroma; ++i) {
+ double bi = state->eqns.b[i];
+ if (is_chroma) {
+ bi -= state->eqns.A[i * n + (n - 1)] * state->eqns.x[n - 1];
+ }
+ sum_covar += (bi * state->eqns.x[i]) / state->num_observations;
+ }
+ // Now, get an estimate of the variance of uncorrelated noise signal and use
+ // it to determine the gain of the AR filter.
+ const double noise_var = AOMMAX(var - sum_covar, 1e-6);
+ state->ar_gain = AOMMAX(1, sqrt(AOMMAX(var / noise_var, 1e-6)));
+ return ret;
+}
+
+aom_noise_status_t aom_noise_model_update(
+ aom_noise_model_t *const noise_model, const uint8_t *const data[3],
+ const uint8_t *const denoised[3], int w, int h, int stride[3],
+ int chroma_sub_log2[2], const uint8_t *const flat_blocks, int block_size) {
+ const int num_blocks_w = (w + block_size - 1) / block_size;
+ const int num_blocks_h = (h + block_size - 1) / block_size;
+ int y_model_different = 0;
+ int num_blocks = 0;
+ int i = 0, channel = 0;
+
+ if (block_size <= 1) {
+ fprintf(stderr, "block_size = %d must be > 1\n", block_size);
+ return AOM_NOISE_STATUS_INVALID_ARGUMENT;
+ }
+
+ if (block_size < noise_model->params.lag * 2 + 1) {
+ fprintf(stderr, "block_size = %d must be >= %d\n", block_size,
+ noise_model->params.lag * 2 + 1);
+ return AOM_NOISE_STATUS_INVALID_ARGUMENT;
+ }
+
+ // Clear the latest equation system
+ for (i = 0; i < 3; ++i) {
+ equation_system_clear(&noise_model->latest_state[i].eqns);
+ noise_model->latest_state[i].num_observations = 0;
+ noise_strength_solver_clear(&noise_model->latest_state[i].strength_solver);
+ }
+
+ // Check that we have enough flat blocks
+ for (i = 0; i < num_blocks_h * num_blocks_w; ++i) {
+ if (flat_blocks[i]) {
+ num_blocks++;
+ }
+ }
+
+ if (num_blocks <= 1) {
+ fprintf(stderr, "Not enough flat blocks to update noise estimate\n");
+ return AOM_NOISE_STATUS_INSUFFICIENT_FLAT_BLOCKS;
+ }
+
+ for (channel = 0; channel < 3; ++channel) {
+ int no_subsampling[2] = { 0, 0 };
+ const uint8_t *alt_data = channel > 0 ? data[0] : 0;
+ const uint8_t *alt_denoised = channel > 0 ? denoised[0] : 0;
+ int *sub = channel > 0 ? chroma_sub_log2 : no_subsampling;
+ const int is_chroma = channel != 0;
+ if (!data[channel] || !denoised[channel]) break;
+ if (!add_block_observations(noise_model, channel, data[channel],
+ denoised[channel], w, h, stride[channel], sub,
+ alt_data, alt_denoised, stride[0], flat_blocks,
+ block_size, num_blocks_w, num_blocks_h)) {
+ fprintf(stderr, "Adding block observation failed\n");
+ return AOM_NOISE_STATUS_INTERNAL_ERROR;
+ }
+
+ if (!ar_equation_system_solve(&noise_model->latest_state[channel],
+ is_chroma)) {
+ if (is_chroma) {
+ set_chroma_coefficient_fallback_soln(
+ &noise_model->latest_state[channel].eqns);
+ } else {
+ fprintf(stderr, "Solving latest noise equation system failed %d!\n",
+ channel);
+ return AOM_NOISE_STATUS_INTERNAL_ERROR;
+ }
+ }
+
+ add_noise_std_observations(
+ noise_model, channel, noise_model->latest_state[channel].eqns.x,
+ data[channel], denoised[channel], w, h, stride[channel], sub, alt_data,
+ stride[0], flat_blocks, block_size, num_blocks_w, num_blocks_h);
+
+ if (!aom_noise_strength_solver_solve(
+ &noise_model->latest_state[channel].strength_solver)) {
+ fprintf(stderr, "Solving latest noise strength failed!\n");
+ return AOM_NOISE_STATUS_INTERNAL_ERROR;
+ }
+
+ // Check noise characteristics and return if error.
+ if (channel == 0 &&
+ noise_model->combined_state[channel].strength_solver.num_equations >
+ 0 &&
+ is_noise_model_different(noise_model)) {
+ y_model_different = 1;
+ }
+
+ // Don't update the combined stats if the y model is different.
+ if (y_model_different) continue;
+
+ noise_model->combined_state[channel].num_observations +=
+ noise_model->latest_state[channel].num_observations;
+ equation_system_add(&noise_model->combined_state[channel].eqns,
+ &noise_model->latest_state[channel].eqns);
+ if (!ar_equation_system_solve(&noise_model->combined_state[channel],
+ is_chroma)) {
+ if (is_chroma) {
+ set_chroma_coefficient_fallback_soln(
+ &noise_model->combined_state[channel].eqns);
+ } else {
+ fprintf(stderr, "Solving combined noise equation system failed %d!\n",
+ channel);
+ return AOM_NOISE_STATUS_INTERNAL_ERROR;
+ }
+ }
+
+ noise_strength_solver_add(
+ &noise_model->combined_state[channel].strength_solver,
+ &noise_model->latest_state[channel].strength_solver);
+
+ if (!aom_noise_strength_solver_solve(
+ &noise_model->combined_state[channel].strength_solver)) {
+ fprintf(stderr, "Solving combined noise strength failed!\n");
+ return AOM_NOISE_STATUS_INTERNAL_ERROR;
+ }
+ }
+
+ return y_model_different ? AOM_NOISE_STATUS_DIFFERENT_NOISE_TYPE
+ : AOM_NOISE_STATUS_OK;
+}
+
+void aom_noise_model_save_latest(aom_noise_model_t *noise_model) {
+ for (int c = 0; c < 3; c++) {
+ equation_system_copy(&noise_model->combined_state[c].eqns,
+ &noise_model->latest_state[c].eqns);
+ equation_system_copy(&noise_model->combined_state[c].strength_solver.eqns,
+ &noise_model->latest_state[c].strength_solver.eqns);
+ noise_model->combined_state[c].strength_solver.num_equations =
+ noise_model->latest_state[c].strength_solver.num_equations;
+ noise_model->combined_state[c].num_observations =
+ noise_model->latest_state[c].num_observations;
+ noise_model->combined_state[c].ar_gain =
+ noise_model->latest_state[c].ar_gain;
+ }
+}
+
+int aom_noise_model_get_grain_parameters(aom_noise_model_t *const noise_model,
+ aom_film_grain_t *film_grain) {
+ if (noise_model->params.lag > 3) {
+ fprintf(stderr, "params.lag = %d > 3\n", noise_model->params.lag);
+ return 0;
+ }
+ memset(film_grain, 0, sizeof(*film_grain));
+
+ film_grain->apply_grain = 1;
+ film_grain->update_parameters = 1;
+
+ film_grain->ar_coeff_lag = noise_model->params.lag;
+
+ // Convert the scaling functions to 8 bit values
+ aom_noise_strength_lut_t scaling_points[3];
+ aom_noise_strength_solver_fit_piecewise(
+ &noise_model->combined_state[0].strength_solver, 14, scaling_points + 0);
+ aom_noise_strength_solver_fit_piecewise(
+ &noise_model->combined_state[1].strength_solver, 10, scaling_points + 1);
+ aom_noise_strength_solver_fit_piecewise(
+ &noise_model->combined_state[2].strength_solver, 10, scaling_points + 2);
+
+ // Both the domain and the range of the scaling functions in the film_grain
+ // are normalized to 8-bit (e.g., they are implicitly scaled during grain
+ // synthesis).
+ const double strength_divisor = 1 << (noise_model->params.bit_depth - 8);
+ double max_scaling_value = 1e-4;
+ for (int c = 0; c < 3; ++c) {
+ for (int i = 0; i < scaling_points[c].num_points; ++i) {
+ scaling_points[c].points[i][0] =
+ AOMMIN(255, scaling_points[c].points[i][0] / strength_divisor);
+ scaling_points[c].points[i][1] =
+ AOMMIN(255, scaling_points[c].points[i][1] / strength_divisor);
+ max_scaling_value =
+ AOMMAX(scaling_points[c].points[i][1], max_scaling_value);
+ }
+ }
+
+ // Scaling_shift values are in the range [8,11]
+ const int max_scaling_value_log2 =
+ clamp((int)floor(log2(max_scaling_value) + 1), 2, 5);
+ film_grain->scaling_shift = 5 + (8 - max_scaling_value_log2);
+
+ const double scale_factor = 1 << (8 - max_scaling_value_log2);
+ film_grain->num_y_points = scaling_points[0].num_points;
+ film_grain->num_cb_points = scaling_points[1].num_points;
+ film_grain->num_cr_points = scaling_points[2].num_points;
+
+ int(*film_grain_scaling[3])[2] = {
+ film_grain->scaling_points_y,
+ film_grain->scaling_points_cb,
+ film_grain->scaling_points_cr,
+ };
+ for (int c = 0; c < 3; c++) {
+ for (int i = 0; i < scaling_points[c].num_points; ++i) {
+ film_grain_scaling[c][i][0] = (int)(scaling_points[c].points[i][0] + 0.5);
+ film_grain_scaling[c][i][1] = clamp(
+ (int)(scale_factor * scaling_points[c].points[i][1] + 0.5), 0, 255);
+ }
+ }
+ aom_noise_strength_lut_free(scaling_points + 0);
+ aom_noise_strength_lut_free(scaling_points + 1);
+ aom_noise_strength_lut_free(scaling_points + 2);
+
+ // Convert the ar_coeffs into 8-bit values
+ const int n_coeff = noise_model->combined_state[0].eqns.n;
+ double max_coeff = 1e-4, min_coeff = -1e-4;
+ double y_corr[2] = { 0, 0 };
+ double avg_luma_strength = 0;
+ for (int c = 0; c < 3; c++) {
+ aom_equation_system_t *eqns = &noise_model->combined_state[c].eqns;
+ for (int i = 0; i < n_coeff; ++i) {
+ max_coeff = AOMMAX(max_coeff, eqns->x[i]);
+ min_coeff = AOMMIN(min_coeff, eqns->x[i]);
+ }
+ // Since the correlation between luma/chroma was computed in an already
+ // scaled space, we adjust it in the un-scaled space.
+ aom_noise_strength_solver_t *solver =
+ &noise_model->combined_state[c].strength_solver;
+ // Compute a weighted average of the strength for the channel.
+ double average_strength = 0, total_weight = 0;
+ for (int i = 0; i < solver->eqns.n; ++i) {
+ double w = 0;
+ for (int j = 0; j < solver->eqns.n; ++j) {
+ w += solver->eqns.A[i * solver->eqns.n + j];
+ }
+ w = sqrt(w);
+ average_strength += solver->eqns.x[i] * w;
+ total_weight += w;
+ }
+ if (total_weight == 0)
+ average_strength = 1;
+ else
+ average_strength /= total_weight;
+ if (c == 0) {
+ avg_luma_strength = average_strength;
+ } else {
+ y_corr[c - 1] = avg_luma_strength * eqns->x[n_coeff] / average_strength;
+ max_coeff = AOMMAX(max_coeff, y_corr[c - 1]);
+ min_coeff = AOMMIN(min_coeff, y_corr[c - 1]);
+ }
+ }
+ // Shift value: AR coeffs range (values 6-9)
+ // 6: [-2, 2), 7: [-1, 1), 8: [-0.5, 0.5), 9: [-0.25, 0.25)
+ film_grain->ar_coeff_shift =
+ clamp(7 - (int)AOMMAX(1 + floor(log2(max_coeff)), ceil(log2(-min_coeff))),
+ 6, 9);
+ double scale_ar_coeff = 1 << film_grain->ar_coeff_shift;
+ int *ar_coeffs[3] = {
+ film_grain->ar_coeffs_y,
+ film_grain->ar_coeffs_cb,
+ film_grain->ar_coeffs_cr,
+ };
+ for (int c = 0; c < 3; ++c) {
+ aom_equation_system_t *eqns = &noise_model->combined_state[c].eqns;
+ for (int i = 0; i < n_coeff; ++i) {
+ ar_coeffs[c][i] =
+ clamp((int)round(scale_ar_coeff * eqns->x[i]), -128, 127);
+ }
+ if (c > 0) {
+ ar_coeffs[c][n_coeff] =
+ clamp((int)round(scale_ar_coeff * y_corr[c - 1]), -128, 127);
+ }
+ }
+
+ // At the moment, the noise modeling code assumes that the chroma scaling
+ // functions are a function of luma.
+ film_grain->cb_mult = 128; // 8 bits
+ film_grain->cb_luma_mult = 192; // 8 bits
+ film_grain->cb_offset = 256; // 9 bits
+
+ film_grain->cr_mult = 128; // 8 bits
+ film_grain->cr_luma_mult = 192; // 8 bits
+ film_grain->cr_offset = 256; // 9 bits
+
+ film_grain->chroma_scaling_from_luma = 0;
+ film_grain->grain_scale_shift = 0;
+ film_grain->overlap_flag = 1;
+ return 1;
+}
+
+static void pointwise_multiply(const float *a, float *b, int n) {
+ for (int i = 0; i < n; ++i) {
+ b[i] *= a[i];
+ }
+}
+
+static float *get_half_cos_window(int block_size) {
+ float *window_function =
+ (float *)aom_malloc(block_size * block_size * sizeof(*window_function));
+ for (int y = 0; y < block_size; ++y) {
+ const double cos_yd = cos((.5 + y) * PI / block_size - PI / 2);
+ for (int x = 0; x < block_size; ++x) {
+ const double cos_xd = cos((.5 + x) * PI / block_size - PI / 2);
+ window_function[y * block_size + x] = (float)(cos_yd * cos_xd);
+ }
+ }
+ return window_function;
+}
+
+#define DITHER_AND_QUANTIZE(INT_TYPE, suffix) \
+ static void dither_and_quantize_##suffix( \
+ float *result, int result_stride, INT_TYPE *denoised, int w, int h, \
+ int stride, int chroma_sub_w, int chroma_sub_h, int block_size, \
+ float block_normalization) { \
+ for (int y = 0; y < (h >> chroma_sub_h); ++y) { \
+ for (int x = 0; x < (w >> chroma_sub_w); ++x) { \
+ const int result_idx = \
+ (y + (block_size >> chroma_sub_h)) * result_stride + x + \
+ (block_size >> chroma_sub_w); \
+ INT_TYPE new_val = (INT_TYPE)AOMMIN( \
+ AOMMAX(result[result_idx] * block_normalization + 0.5f, 0), \
+ block_normalization); \
+ const float err = \
+ -(((float)new_val) / block_normalization - result[result_idx]); \
+ denoised[y * stride + x] = new_val; \
+ if (x + 1 < (w >> chroma_sub_w)) { \
+ result[result_idx + 1] += err * 7.0f / 16.0f; \
+ } \
+ if (y + 1 < (h >> chroma_sub_h)) { \
+ if (x > 0) { \
+ result[result_idx + result_stride - 1] += err * 3.0f / 16.0f; \
+ } \
+ result[result_idx + result_stride] += err * 5.0f / 16.0f; \
+ if (x + 1 < (w >> chroma_sub_w)) { \
+ result[result_idx + result_stride + 1] += err * 1.0f / 16.0f; \
+ } \
+ } \
+ } \
+ } \
+ }
+
+DITHER_AND_QUANTIZE(uint8_t, lowbd);
+DITHER_AND_QUANTIZE(uint16_t, highbd);
+
+int aom_wiener_denoise_2d(const uint8_t *const data[3], uint8_t *denoised[3],
+ int w, int h, int stride[3], int chroma_sub[2],
+ float *noise_psd[3], int block_size, int bit_depth,
+ int use_highbd) {
+ float *plane = NULL, *block = NULL, *window_full = NULL,
+ *window_chroma = NULL;
+ double *block_d = NULL, *plane_d = NULL;
+ struct aom_noise_tx_t *tx_full = NULL;
+ struct aom_noise_tx_t *tx_chroma = NULL;
+ const int num_blocks_w = (w + block_size - 1) / block_size;
+ const int num_blocks_h = (h + block_size - 1) / block_size;
+ const int result_stride = (num_blocks_w + 2) * block_size;
+ const int result_height = (num_blocks_h + 2) * block_size;
+ float *result = NULL;
+ int init_success = 1;
+ aom_flat_block_finder_t block_finder_full;
+ aom_flat_block_finder_t block_finder_chroma;
+ const float kBlockNormalization = (float)((1 << bit_depth) - 1);
+ if (chroma_sub[0] != chroma_sub[1]) {
+ fprintf(stderr,
+ "aom_wiener_denoise_2d doesn't handle different chroma "
+ "subsampling");
+ return 0;
+ }
+ init_success &= aom_flat_block_finder_init(&block_finder_full, block_size,
+ bit_depth, use_highbd);
+ result = (float *)aom_malloc((num_blocks_h + 2) * block_size * result_stride *
+ sizeof(*result));
+ plane = (float *)aom_malloc(block_size * block_size * sizeof(*plane));
+ block =
+ (float *)aom_memalign(32, 2 * block_size * block_size * sizeof(*block));
+ block_d = (double *)aom_malloc(block_size * block_size * sizeof(*block_d));
+ plane_d = (double *)aom_malloc(block_size * block_size * sizeof(*plane_d));
+ window_full = get_half_cos_window(block_size);
+ tx_full = aom_noise_tx_malloc(block_size);
+
+ if (chroma_sub[0] != 0) {
+ init_success &= aom_flat_block_finder_init(&block_finder_chroma,
+ block_size >> chroma_sub[0],
+ bit_depth, use_highbd);
+ window_chroma = get_half_cos_window(block_size >> chroma_sub[0]);
+ tx_chroma = aom_noise_tx_malloc(block_size >> chroma_sub[0]);
+ } else {
+ window_chroma = window_full;
+ tx_chroma = tx_full;
+ }
+
+ init_success &= (tx_full != NULL) && (tx_chroma != NULL) && (plane != NULL) &&
+ (plane_d != NULL) && (block != NULL) && (block_d != NULL) &&
+ (window_full != NULL) && (window_chroma != NULL) &&
+ (result != NULL);
+ for (int c = init_success ? 0 : 3; c < 3; ++c) {
+ float *window_function = c == 0 ? window_full : window_chroma;
+ aom_flat_block_finder_t *block_finder = &block_finder_full;
+ const int chroma_sub_h = c > 0 ? chroma_sub[1] : 0;
+ const int chroma_sub_w = c > 0 ? chroma_sub[0] : 0;
+ struct aom_noise_tx_t *tx =
+ (c > 0 && chroma_sub[0] > 0) ? tx_chroma : tx_full;
+ if (!data[c] || !denoised[c]) continue;
+ if (c > 0 && chroma_sub[0] != 0) {
+ block_finder = &block_finder_chroma;
+ }
+ memset(result, 0, sizeof(*result) * result_stride * result_height);
+ // Do overlapped block processing (half overlapped). The block rows can
+ // easily be done in parallel
+ for (int offsy = 0; offsy < (block_size >> chroma_sub_h);
+ offsy += (block_size >> chroma_sub_h) / 2) {
+ for (int offsx = 0; offsx < (block_size >> chroma_sub_w);
+ offsx += (block_size >> chroma_sub_w) / 2) {
+ // Pad the boundary when processing each block-set.
+ for (int by = -1; by < num_blocks_h; ++by) {
+ for (int bx = -1; bx < num_blocks_w; ++bx) {
+ const int pixels_per_block =
+ (block_size >> chroma_sub_w) * (block_size >> chroma_sub_h);
+ aom_flat_block_finder_extract_block(
+ block_finder, data[c], w >> chroma_sub_w, h >> chroma_sub_h,
+ stride[c], bx * (block_size >> chroma_sub_w) + offsx,
+ by * (block_size >> chroma_sub_h) + offsy, plane_d, block_d);
+ for (int j = 0; j < pixels_per_block; ++j) {
+ block[j] = (float)block_d[j];
+ plane[j] = (float)plane_d[j];
+ }
+ pointwise_multiply(window_function, block, pixels_per_block);
+ aom_noise_tx_forward(tx, block);
+ aom_noise_tx_filter(tx, noise_psd[c]);
+ aom_noise_tx_inverse(tx, block);
+
+ // Apply window function to the plane approximation (we will apply
+ // it to the sum of plane + block when composing the results).
+ pointwise_multiply(window_function, plane, pixels_per_block);
+
+ for (int y = 0; y < (block_size >> chroma_sub_h); ++y) {
+ const int y_result =
+ y + (by + 1) * (block_size >> chroma_sub_h) + offsy;
+ for (int x = 0; x < (block_size >> chroma_sub_w); ++x) {
+ const int x_result =
+ x + (bx + 1) * (block_size >> chroma_sub_w) + offsx;
+ result[y_result * result_stride + x_result] +=
+ (block[y * (block_size >> chroma_sub_w) + x] +
+ plane[y * (block_size >> chroma_sub_w) + x]) *
+ window_function[y * (block_size >> chroma_sub_w) + x];
+ }
+ }
+ }
+ }
+ }
+ }
+ if (use_highbd) {
+ dither_and_quantize_highbd(result, result_stride, (uint16_t *)denoised[c],
+ w, h, stride[c], chroma_sub_w, chroma_sub_h,
+ block_size, kBlockNormalization);
+ } else {
+ dither_and_quantize_lowbd(result, result_stride, denoised[c], w, h,
+ stride[c], chroma_sub_w, chroma_sub_h,
+ block_size, kBlockNormalization);
+ }
+ }
+ aom_free(result);
+ aom_free(plane);
+ aom_free(block);
+ aom_free(plane_d);
+ aom_free(block_d);
+ aom_free(window_full);
+
+ aom_noise_tx_free(tx_full);
+
+ aom_flat_block_finder_free(&block_finder_full);
+ if (chroma_sub[0] != 0) {
+ aom_flat_block_finder_free(&block_finder_chroma);
+ aom_free(window_chroma);
+ aom_noise_tx_free(tx_chroma);
+ }
+ return init_success;
+}