/* * 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, ¶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); 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]; 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; } 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); 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) { 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; } 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; }