| /* |
| * 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/mathutils.h" |
| #include "aom_dsp/noise_model.h" |
| #include "aom_dsp/noise_util.h" |
| #include "aom_mem/aom_mem.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; |
| if (num_points <= 0) return 0; |
| lut->num_points = 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)); |
| if (!residual) { |
| aom_noise_strength_lut_free(lut); |
| return 0; |
| } |
| 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; |
| block_finder->A = NULL; |
| block_finder->AtA_inv = NULL; |
| |
| 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; |
| |
| const double value = block[yi * block_size + xi]; |
| mean += value; |
| var += value * value; |
| } |
| } |
| 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 }; |
| double sum_weights = weights[0] * var + weights[1] * ratio + |
| weights[2] * trace + weights[3] * norm + |
| weights[4]; |
| // clamp the value to [-25.0, 100.0] to prevent overflow |
| sum_weights = fclamp(sum_weights, -25.0, 100.0); |
| const float score = (float)(1.0 / (1 + exp(-sum_weights))); |
| 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; |
| } |
| if (!(params.bit_depth == 8 || params.bit_depth == 10 || |
| params.bit_depth == 12)) { |
| return 0; |
| } |
| |
| memcpy(&model->params, ¶ms, 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); |
| if (!model->coords) { |
| aom_noise_model_free(model); |
| return 0; |
| } |
| |
| 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; |
| } |
| uint16_t random_seed = film_grain->random_seed; |
| memset(film_grain, 0, sizeof(*film_grain)); |
| film_grain->random_seed = random_seed; |
| |
| 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]; |
| if (!aom_noise_strength_solver_fit_piecewise( |
| &noise_model->combined_state[0].strength_solver, 14, |
| scaling_points + 0)) { |
| return 0; |
| } |
| if (!aom_noise_strength_solver_fit_piecewise( |
| &noise_model->combined_state[1].strength_solver, 10, |
| scaling_points + 1)) { |
| aom_noise_strength_lut_free(scaling_points + 0); |
| return 0; |
| } |
| if (!aom_noise_strength_solver_fit_piecewise( |
| &noise_model->combined_state[2].strength_solver, 10, |
| scaling_points + 2)) { |
| aom_noise_strength_lut_free(scaling_points + 0); |
| aom_noise_strength_lut_free(scaling_points + 1); |
| return 0; |
| } |
| |
| // 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)); |
| if (!window_function) return NULL; |
| 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\n"); |
| 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; |
| } |
| |
| struct aom_denoise_and_model_t { |
| int block_size; |
| int bit_depth; |
| float noise_level; |
| |
| // Size of current denoised buffer and flat_block buffer |
| int width; |
| int height; |
| int y_stride; |
| int uv_stride; |
| int num_blocks_w; |
| int num_blocks_h; |
| |
| // Buffers for image and noise_psd allocated on the fly |
| float *noise_psd[3]; |
| uint8_t *denoised[3]; |
| uint8_t *flat_blocks; |
| |
| aom_flat_block_finder_t flat_block_finder; |
| aom_noise_model_t noise_model; |
| }; |
| |
| struct aom_denoise_and_model_t *aom_denoise_and_model_alloc(int bit_depth, |
| int block_size, |
| float noise_level) { |
| struct aom_denoise_and_model_t *ctx = |
| (struct aom_denoise_and_model_t *)aom_malloc( |
| sizeof(struct aom_denoise_and_model_t)); |
| if (!ctx) { |
| fprintf(stderr, "Unable to allocate denoise_and_model struct\n"); |
| return NULL; |
| } |
| memset(ctx, 0, sizeof(*ctx)); |
| |
| ctx->block_size = block_size; |
| ctx->noise_level = noise_level; |
| ctx->bit_depth = bit_depth; |
| |
| ctx->noise_psd[0] = |
| aom_malloc(sizeof(*ctx->noise_psd[0]) * block_size * block_size); |
| ctx->noise_psd[1] = |
| aom_malloc(sizeof(*ctx->noise_psd[1]) * block_size * block_size); |
| ctx->noise_psd[2] = |
| aom_malloc(sizeof(*ctx->noise_psd[2]) * block_size * block_size); |
| if (!ctx->noise_psd[0] || !ctx->noise_psd[1] || !ctx->noise_psd[2]) { |
| fprintf(stderr, "Unable to allocate noise PSD buffers\n"); |
| aom_denoise_and_model_free(ctx); |
| return NULL; |
| } |
| return ctx; |
| } |
| |
| void aom_denoise_and_model_free(struct aom_denoise_and_model_t *ctx) { |
| aom_free(ctx->flat_blocks); |
| for (int i = 0; i < 3; ++i) { |
| aom_free(ctx->denoised[i]); |
| aom_free(ctx->noise_psd[i]); |
| } |
| aom_noise_model_free(&ctx->noise_model); |
| aom_flat_block_finder_free(&ctx->flat_block_finder); |
| aom_free(ctx); |
| } |
| |
| static int denoise_and_model_realloc_if_necessary( |
| struct aom_denoise_and_model_t *ctx, YV12_BUFFER_CONFIG *sd) { |
| if (ctx->width == sd->y_width && ctx->height == sd->y_height && |
| ctx->y_stride == sd->y_stride && ctx->uv_stride == sd->uv_stride) |
| return 1; |
| const int use_highbd = (sd->flags & YV12_FLAG_HIGHBITDEPTH) != 0; |
| const int block_size = ctx->block_size; |
| |
| ctx->width = sd->y_width; |
| ctx->height = sd->y_height; |
| ctx->y_stride = sd->y_stride; |
| ctx->uv_stride = sd->uv_stride; |
| |
| for (int i = 0; i < 3; ++i) { |
| aom_free(ctx->denoised[i]); |
| ctx->denoised[i] = NULL; |
| } |
| aom_free(ctx->flat_blocks); |
| ctx->flat_blocks = NULL; |
| |
| ctx->denoised[0] = aom_malloc((sd->y_stride * sd->y_height) << use_highbd); |
| ctx->denoised[1] = aom_malloc((sd->uv_stride * sd->uv_height) << use_highbd); |
| ctx->denoised[2] = aom_malloc((sd->uv_stride * sd->uv_height) << use_highbd); |
| if (!ctx->denoised[0] || !ctx->denoised[1] || !ctx->denoised[2]) { |
| fprintf(stderr, "Unable to allocate denoise buffers\n"); |
| return 0; |
| } |
| ctx->num_blocks_w = (sd->y_width + ctx->block_size - 1) / ctx->block_size; |
| ctx->num_blocks_h = (sd->y_height + ctx->block_size - 1) / ctx->block_size; |
| ctx->flat_blocks = aom_malloc(ctx->num_blocks_w * ctx->num_blocks_h); |
| if (!ctx->flat_blocks) { |
| fprintf(stderr, "Unable to allocate flat_blocks buffer\n"); |
| return 0; |
| } |
| |
| aom_flat_block_finder_free(&ctx->flat_block_finder); |
| if (!aom_flat_block_finder_init(&ctx->flat_block_finder, ctx->block_size, |
| ctx->bit_depth, use_highbd)) { |
| fprintf(stderr, "Unable to init flat block finder\n"); |
| return 0; |
| } |
| |
| const aom_noise_model_params_t params = { AOM_NOISE_SHAPE_SQUARE, 3, |
| ctx->bit_depth, use_highbd }; |
| aom_noise_model_free(&ctx->noise_model); |
| if (!aom_noise_model_init(&ctx->noise_model, params)) { |
| fprintf(stderr, "Unable to init noise model\n"); |
| return 0; |
| } |
| |
| // Simply use a flat PSD (although we could use the flat blocks to estimate |
| // PSD) those to estimate an actual noise PSD) |
| const float y_noise_level = |
| aom_noise_psd_get_default_value(ctx->block_size, ctx->noise_level); |
| const float uv_noise_level = aom_noise_psd_get_default_value( |
| ctx->block_size >> sd->subsampling_x, ctx->noise_level); |
| for (int i = 0; i < block_size * block_size; ++i) { |
| ctx->noise_psd[0][i] = y_noise_level; |
| ctx->noise_psd[1][i] = ctx->noise_psd[2][i] = uv_noise_level; |
| } |
| return 1; |
| } |
| |
| int aom_denoise_and_model_run(struct aom_denoise_and_model_t *ctx, |
| YV12_BUFFER_CONFIG *sd, |
| aom_film_grain_t *film_grain, int apply_denoise) { |
| const int block_size = ctx->block_size; |
| const int use_highbd = (sd->flags & YV12_FLAG_HIGHBITDEPTH) != 0; |
| uint8_t *raw_data[3] = { |
| use_highbd ? (uint8_t *)CONVERT_TO_SHORTPTR(sd->y_buffer) : sd->y_buffer, |
| use_highbd ? (uint8_t *)CONVERT_TO_SHORTPTR(sd->u_buffer) : sd->u_buffer, |
| use_highbd ? (uint8_t *)CONVERT_TO_SHORTPTR(sd->v_buffer) : sd->v_buffer, |
| }; |
| const uint8_t *const data[3] = { raw_data[0], raw_data[1], raw_data[2] }; |
| int strides[3] = { sd->y_stride, sd->uv_stride, sd->uv_stride }; |
| int chroma_sub_log2[2] = { sd->subsampling_x, sd->subsampling_y }; |
| |
| if (!denoise_and_model_realloc_if_necessary(ctx, sd)) { |
| fprintf(stderr, "Unable to realloc buffers\n"); |
| return 0; |
| } |
| |
| aom_flat_block_finder_run(&ctx->flat_block_finder, data[0], sd->y_width, |
| sd->y_height, strides[0], ctx->flat_blocks); |
| |
| if (!aom_wiener_denoise_2d(data, ctx->denoised, sd->y_width, sd->y_height, |
| strides, chroma_sub_log2, ctx->noise_psd, |
| block_size, ctx->bit_depth, use_highbd)) { |
| fprintf(stderr, "Unable to denoise image\n"); |
| return 0; |
| } |
| |
| const aom_noise_status_t status = aom_noise_model_update( |
| &ctx->noise_model, data, (const uint8_t *const *)ctx->denoised, |
| sd->y_width, sd->y_height, strides, chroma_sub_log2, ctx->flat_blocks, |
| block_size); |
| int have_noise_estimate = 0; |
| if (status == AOM_NOISE_STATUS_OK) { |
| have_noise_estimate = 1; |
| } else if (status == AOM_NOISE_STATUS_DIFFERENT_NOISE_TYPE) { |
| aom_noise_model_save_latest(&ctx->noise_model); |
| have_noise_estimate = 1; |
| } else { |
| // Unable to update noise model; proceed if we have a previous estimate. |
| have_noise_estimate = |
| (ctx->noise_model.combined_state[0].strength_solver.num_equations > 0); |
| } |
| |
| film_grain->apply_grain = 0; |
| if (have_noise_estimate) { |
| if (!aom_noise_model_get_grain_parameters(&ctx->noise_model, film_grain)) { |
| fprintf(stderr, "Unable to get grain parameters.\n"); |
| return 0; |
| } |
| if (!film_grain->random_seed) { |
| film_grain->random_seed = 7391; |
| } |
| if (apply_denoise) { |
| memcpy(raw_data[0], ctx->denoised[0], |
| (strides[0] * sd->y_height) << use_highbd); |
| memcpy(raw_data[1], ctx->denoised[1], |
| (strides[1] * sd->uv_height) << use_highbd); |
| memcpy(raw_data[2], ctx->denoised[2], |
| (strides[2] * sd->uv_height) << use_highbd); |
| } |
| } |
| return 1; |
| } |