diff options
Diffstat (limited to 'third_party/aom/aom_dsp/noise_model.c')
| -rw-r--r-- | third_party/aom/aom_dsp/noise_model.c | 1460 |
1 files changed, 1460 insertions, 0 deletions
diff --git a/third_party/aom/aom_dsp/noise_model.c b/third_party/aom/aom_dsp/noise_model.c new file mode 100644 index 000000000..a1287f74f --- /dev/null +++ b/third_party/aom/aom_dsp/noise_model.c @@ -0,0 +1,1460 @@ +/* + * Copyright (c) 2017, Alliance for Open Media. All rights reserved + * + * This source code is subject to the terms of the BSD 2 Clause License and + * the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License + * was not distributed with this source code in the LICENSE file, you can + * obtain it at www.aomedia.org/license/software. If the Alliance for Open + * Media Patent License 1.0 was not distributed with this source code in the + * PATENTS file, you can obtain it at www.aomedia.org/license/patent. + */ + +#include <math.h> +#include <stdio.h> +#include <stdlib.h> +#include <string.h> + +#include "aom_dsp/aom_dsp_common.h" +#include "aom_dsp/noise_model.h" +#include "aom_dsp/noise_util.h" +#include "aom_mem/aom_mem.h" +#include "av1/common/common.h" +#include "av1/encoder/mathutils.h" + +#define kLowPolyNumParams 3 + +static const int kMaxLag = 4; + +// Defines a function that can be used to obtain the mean of a block for the +// provided data type (uint8_t, or uint16_t) +#define GET_BLOCK_MEAN(INT_TYPE, suffix) \ + static double get_block_mean_##suffix(const INT_TYPE *data, int w, int h, \ + int stride, int x_o, int y_o, \ + int block_size) { \ + const int max_h = AOMMIN(h - y_o, block_size); \ + const int max_w = AOMMIN(w - x_o, block_size); \ + double block_mean = 0; \ + for (int y = 0; y < max_h; ++y) { \ + for (int x = 0; x < max_w; ++x) { \ + block_mean += data[(y_o + y) * stride + x_o + x]; \ + } \ + } \ + return block_mean / (max_w * max_h); \ + } + +GET_BLOCK_MEAN(uint8_t, lowbd); +GET_BLOCK_MEAN(uint16_t, highbd); + +static INLINE double get_block_mean(const uint8_t *data, int w, int h, + int stride, int x_o, int y_o, + int block_size, int use_highbd) { + if (use_highbd) + return get_block_mean_highbd((const uint16_t *)data, w, h, stride, x_o, y_o, + block_size); + return get_block_mean_lowbd(data, w, h, stride, x_o, y_o, block_size); +} + +// Defines a function that can be used to obtain the variance of a block +// for the provided data type (uint8_t, or uint16_t) +#define GET_NOISE_VAR(INT_TYPE, suffix) \ + static double get_noise_var_##suffix( \ + const INT_TYPE *data, const INT_TYPE *denoised, int stride, int w, \ + int h, int x_o, int y_o, int block_size_x, int block_size_y) { \ + const int max_h = AOMMIN(h - y_o, block_size_y); \ + const int max_w = AOMMIN(w - x_o, block_size_x); \ + double noise_var = 0; \ + double noise_mean = 0; \ + for (int y = 0; y < max_h; ++y) { \ + for (int x = 0; x < max_w; ++x) { \ + double noise = (double)data[(y_o + y) * stride + x_o + x] - \ + denoised[(y_o + y) * stride + x_o + x]; \ + noise_mean += noise; \ + noise_var += noise * noise; \ + } \ + } \ + noise_mean /= (max_w * max_h); \ + return noise_var / (max_w * max_h) - noise_mean * noise_mean; \ + } + +GET_NOISE_VAR(uint8_t, lowbd); +GET_NOISE_VAR(uint16_t, highbd); + +static INLINE double get_noise_var(const uint8_t *data, const uint8_t *denoised, + int w, int h, int stride, int x_o, int y_o, + int block_size_x, int block_size_y, + int use_highbd) { + if (use_highbd) + return get_noise_var_highbd((const uint16_t *)data, + (const uint16_t *)denoised, w, h, stride, x_o, + y_o, block_size_x, block_size_y); + return get_noise_var_lowbd(data, denoised, w, h, stride, x_o, y_o, + block_size_x, block_size_y); +} + +static void equation_system_clear(aom_equation_system_t *eqns) { + const int n = eqns->n; + memset(eqns->A, 0, sizeof(*eqns->A) * n * n); + memset(eqns->x, 0, sizeof(*eqns->x) * n); + memset(eqns->b, 0, sizeof(*eqns->b) * n); +} + +static void equation_system_copy(aom_equation_system_t *dst, + const aom_equation_system_t *src) { + const int n = dst->n; + memcpy(dst->A, src->A, sizeof(*dst->A) * n * n); + memcpy(dst->x, src->x, sizeof(*dst->x) * n); + memcpy(dst->b, src->b, sizeof(*dst->b) * n); +} + +static int equation_system_init(aom_equation_system_t *eqns, int n) { + eqns->A = (double *)aom_malloc(sizeof(*eqns->A) * n * n); + eqns->b = (double *)aom_malloc(sizeof(*eqns->b) * n); + eqns->x = (double *)aom_malloc(sizeof(*eqns->x) * n); + eqns->n = n; + if (!eqns->A || !eqns->b || !eqns->x) { + fprintf(stderr, "Failed to allocate system of equations of size %d\n", n); + aom_free(eqns->A); + aom_free(eqns->b); + aom_free(eqns->x); + memset(eqns, 0, sizeof(*eqns)); + return 0; + } + equation_system_clear(eqns); + return 1; +} + +static int equation_system_solve(aom_equation_system_t *eqns) { + const int n = eqns->n; + double *b = (double *)aom_malloc(sizeof(*b) * n); + double *A = (double *)aom_malloc(sizeof(*A) * n * n); + int ret = 0; + if (A == NULL || b == NULL) { + fprintf(stderr, "Unable to allocate temp values of size %dx%d\n", n, n); + aom_free(b); + aom_free(A); + return 0; + } + memcpy(A, eqns->A, sizeof(*eqns->A) * n * n); + memcpy(b, eqns->b, sizeof(*eqns->b) * n); + ret = linsolve(n, A, eqns->n, b, eqns->x); + aom_free(b); + aom_free(A); + + if (ret == 0) { + return 0; + } + return 1; +} + +static void equation_system_add(aom_equation_system_t *dest, + aom_equation_system_t *src) { + const int n = dest->n; + int i, j; + for (i = 0; i < n; ++i) { + for (j = 0; j < n; ++j) { + dest->A[i * n + j] += src->A[i * n + j]; + } + dest->b[i] += src->b[i]; + } +} + +static void equation_system_free(aom_equation_system_t *eqns) { + if (!eqns) return; + aom_free(eqns->A); + aom_free(eqns->b); + aom_free(eqns->x); + memset(eqns, 0, sizeof(*eqns)); +} + +static void noise_strength_solver_clear(aom_noise_strength_solver_t *solver) { + equation_system_clear(&solver->eqns); + solver->num_equations = 0; + solver->total = 0; +} + +static void noise_strength_solver_add(aom_noise_strength_solver_t *dest, + aom_noise_strength_solver_t *src) { + equation_system_add(&dest->eqns, &src->eqns); + dest->num_equations += src->num_equations; + dest->total += src->total; +} + +// Return the number of coefficients required for the given parameters +static int num_coeffs(const aom_noise_model_params_t params) { + const int n = 2 * params.lag + 1; + switch (params.shape) { + case AOM_NOISE_SHAPE_DIAMOND: return params.lag * (params.lag + 1); + case AOM_NOISE_SHAPE_SQUARE: return (n * n) / 2; + } + return 0; +} + +static int noise_state_init(aom_noise_state_t *state, int n, int bit_depth) { + const int kNumBins = 20; + if (!equation_system_init(&state->eqns, n)) { + fprintf(stderr, "Failed initialization noise state with size %d\n", n); + return 0; + } + state->ar_gain = 1.0; + state->num_observations = 0; + return aom_noise_strength_solver_init(&state->strength_solver, kNumBins, + bit_depth); +} + +static void set_chroma_coefficient_fallback_soln(aom_equation_system_t *eqns) { + const double kTolerance = 1e-6; + const int last = eqns->n - 1; + // Set all of the AR coefficients to zero, but try to solve for correlation + // with the luma channel + memset(eqns->x, 0, sizeof(*eqns->x) * eqns->n); + if (fabs(eqns->A[last * eqns->n + last]) > kTolerance) { + eqns->x[last] = eqns->b[last] / eqns->A[last * eqns->n + last]; + } +} + +int aom_noise_strength_lut_init(aom_noise_strength_lut_t *lut, int num_points) { + if (!lut) return 0; + lut->points = (double(*)[2])aom_malloc(num_points * sizeof(*lut->points)); + if (!lut->points) return 0; + lut->num_points = num_points; + memset(lut->points, 0, sizeof(*lut->points) * num_points); + return 1; +} + +void aom_noise_strength_lut_free(aom_noise_strength_lut_t *lut) { + if (!lut) return; + aom_free(lut->points); + memset(lut, 0, sizeof(*lut)); +} + +double aom_noise_strength_lut_eval(const aom_noise_strength_lut_t *lut, + double x) { + int i = 0; + // Constant extrapolation for x < x_0. + if (x < lut->points[0][0]) return lut->points[0][1]; + for (i = 0; i < lut->num_points - 1; ++i) { + if (x >= lut->points[i][0] && x <= lut->points[i + 1][0]) { + const double a = + (x - lut->points[i][0]) / (lut->points[i + 1][0] - lut->points[i][0]); + return lut->points[i + 1][1] * a + lut->points[i][1] * (1.0 - a); + } + } + // Constant extrapolation for x > x_{n-1} + return lut->points[lut->num_points - 1][1]; +} + +static double noise_strength_solver_get_bin_index( + const aom_noise_strength_solver_t *solver, double value) { + const double val = + fclamp(value, solver->min_intensity, solver->max_intensity); + const double range = solver->max_intensity - solver->min_intensity; + return (solver->num_bins - 1) * (val - solver->min_intensity) / range; +} + +static double noise_strength_solver_get_value( + const aom_noise_strength_solver_t *solver, double x) { + const double bin = noise_strength_solver_get_bin_index(solver, x); + const int bin_i0 = (int)floor(bin); + const int bin_i1 = AOMMIN(solver->num_bins - 1, bin_i0 + 1); + const double a = bin - bin_i0; + return (1.0 - a) * solver->eqns.x[bin_i0] + a * solver->eqns.x[bin_i1]; +} + +void aom_noise_strength_solver_add_measurement( + aom_noise_strength_solver_t *solver, double block_mean, double noise_std) { + const double bin = noise_strength_solver_get_bin_index(solver, block_mean); + const int bin_i0 = (int)floor(bin); + const int bin_i1 = AOMMIN(solver->num_bins - 1, bin_i0 + 1); + const double a = bin - bin_i0; + const int n = solver->num_bins; + solver->eqns.A[bin_i0 * n + bin_i0] += (1.0 - a) * (1.0 - a); + solver->eqns.A[bin_i1 * n + bin_i0] += a * (1.0 - a); + solver->eqns.A[bin_i1 * n + bin_i1] += a * a; + solver->eqns.A[bin_i0 * n + bin_i1] += a * (1.0 - a); + solver->eqns.b[bin_i0] += (1.0 - a) * noise_std; + solver->eqns.b[bin_i1] += a * noise_std; + solver->total += noise_std; + solver->num_equations++; +} + +int aom_noise_strength_solver_solve(aom_noise_strength_solver_t *solver) { + // Add regularization proportional to the number of constraints + const int n = solver->num_bins; + const double kAlpha = 2.0 * (double)(solver->num_equations) / n; + int result = 0; + double mean = 0; + + // Do this in a non-destructive manner so it is not confusing to the caller + double *old_A = solver->eqns.A; + double *A = (double *)aom_malloc(sizeof(*A) * n * n); + if (!A) { + fprintf(stderr, "Unable to allocate copy of A\n"); + return 0; + } + memcpy(A, old_A, sizeof(*A) * n * n); + + for (int i = 0; i < n; ++i) { + const int i_lo = AOMMAX(0, i - 1); + const int i_hi = AOMMIN(n - 1, i + 1); + A[i * n + i_lo] -= kAlpha; + A[i * n + i] += 2 * kAlpha; + A[i * n + i_hi] -= kAlpha; + } + + // Small regularization to give average noise strength + mean = solver->total / solver->num_equations; + for (int i = 0; i < n; ++i) { + A[i * n + i] += 1.0 / 8192.; + solver->eqns.b[i] += mean / 8192.; + } + solver->eqns.A = A; + result = equation_system_solve(&solver->eqns); + solver->eqns.A = old_A; + + aom_free(A); + return result; +} + +int aom_noise_strength_solver_init(aom_noise_strength_solver_t *solver, + int num_bins, int bit_depth) { + if (!solver) return 0; + memset(solver, 0, sizeof(*solver)); + solver->num_bins = num_bins; + solver->min_intensity = 0; + solver->max_intensity = (1 << bit_depth) - 1; + solver->total = 0; + solver->num_equations = 0; + return equation_system_init(&solver->eqns, num_bins); +} + +void aom_noise_strength_solver_free(aom_noise_strength_solver_t *solver) { + if (!solver) return; + equation_system_free(&solver->eqns); +} + +double aom_noise_strength_solver_get_center( + const aom_noise_strength_solver_t *solver, int i) { + const double range = solver->max_intensity - solver->min_intensity; + const int n = solver->num_bins; + return ((double)i) / (n - 1) * range + solver->min_intensity; +} + +// Computes the residual if a point were to be removed from the lut. This is +// calculated as the area between the output of the solver and the line segment +// that would be formed between [x_{i - 1}, x_{i + 1}). +static void update_piecewise_linear_residual( + const aom_noise_strength_solver_t *solver, + const aom_noise_strength_lut_t *lut, double *residual, int start, int end) { + const double dx = 255. / solver->num_bins; + for (int i = AOMMAX(start, 1); i < AOMMIN(end, lut->num_points - 1); ++i) { + const int lower = AOMMAX(0, (int)floor(noise_strength_solver_get_bin_index( + solver, lut->points[i - 1][0]))); + const int upper = AOMMIN(solver->num_bins - 1, + (int)ceil(noise_strength_solver_get_bin_index( + solver, lut->points[i + 1][0]))); + double r = 0; + for (int j = lower; j <= upper; ++j) { + const double x = aom_noise_strength_solver_get_center(solver, j); + if (x < lut->points[i - 1][0]) continue; + if (x >= lut->points[i + 1][0]) continue; + const double y = solver->eqns.x[j]; + const double a = (x - lut->points[i - 1][0]) / + (lut->points[i + 1][0] - lut->points[i - 1][0]); + const double estimate_y = + lut->points[i - 1][1] * (1.0 - a) + lut->points[i + 1][1] * a; + r += fabs(y - estimate_y); + } + residual[i] = r * dx; + } +} + +int aom_noise_strength_solver_fit_piecewise( + const aom_noise_strength_solver_t *solver, int max_output_points, + aom_noise_strength_lut_t *lut) { + // The tolerance is normalized to be give consistent results between + // different bit-depths. + const double kTolerance = solver->max_intensity * 0.00625 / 255.0; + if (!aom_noise_strength_lut_init(lut, solver->num_bins)) { + fprintf(stderr, "Failed to init lut\n"); + return 0; + } + for (int i = 0; i < solver->num_bins; ++i) { + lut->points[i][0] = aom_noise_strength_solver_get_center(solver, i); + lut->points[i][1] = solver->eqns.x[i]; + } + if (max_output_points < 0) { + max_output_points = solver->num_bins; + } + + double *residual = aom_malloc(solver->num_bins * sizeof(*residual)); + memset(residual, 0, sizeof(*residual) * solver->num_bins); + + update_piecewise_linear_residual(solver, lut, residual, 0, solver->num_bins); + + // Greedily remove points if there are too many or if it doesn't hurt local + // approximation (never remove the end points) + while (lut->num_points > 2) { + int min_index = 1; + for (int j = 1; j < lut->num_points - 1; ++j) { + if (residual[j] < residual[min_index]) { + min_index = j; + } + } + const double dx = + lut->points[min_index + 1][0] - lut->points[min_index - 1][0]; + const double avg_residual = residual[min_index] / dx; + if (lut->num_points <= max_output_points && avg_residual > kTolerance) { + break; + } + + const int num_remaining = lut->num_points - min_index - 1; + memmove(lut->points + min_index, lut->points + min_index + 1, + sizeof(lut->points[0]) * num_remaining); + lut->num_points--; + + update_piecewise_linear_residual(solver, lut, residual, min_index - 1, + min_index + 1); + } + aom_free(residual); + return 1; +} + +int aom_flat_block_finder_init(aom_flat_block_finder_t *block_finder, + int block_size, int bit_depth, int use_highbd) { + const int n = block_size * block_size; + aom_equation_system_t eqns; + double *AtA_inv = 0; + double *A = 0; + int x = 0, y = 0, i = 0, j = 0; + if (!equation_system_init(&eqns, kLowPolyNumParams)) { + fprintf(stderr, "Failed to init equation system for block_size=%d\n", + block_size); + return 0; + } + + AtA_inv = (double *)aom_malloc(kLowPolyNumParams * kLowPolyNumParams * + sizeof(*AtA_inv)); + A = (double *)aom_malloc(kLowPolyNumParams * n * sizeof(*A)); + if (AtA_inv == NULL || A == NULL) { + fprintf(stderr, "Failed to alloc A or AtA_inv for block_size=%d\n", + block_size); + aom_free(AtA_inv); + aom_free(A); + equation_system_free(&eqns); + return 0; + } + + block_finder->A = A; + block_finder->AtA_inv = AtA_inv; + block_finder->block_size = block_size; + block_finder->normalization = (1 << bit_depth) - 1; + block_finder->use_highbd = use_highbd; + + for (y = 0; y < block_size; ++y) { + const double yd = ((double)y - block_size / 2.) / (block_size / 2.); + for (x = 0; x < block_size; ++x) { + const double xd = ((double)x - block_size / 2.) / (block_size / 2.); + const double coords[3] = { yd, xd, 1 }; + const int row = y * block_size + x; + A[kLowPolyNumParams * row + 0] = yd; + A[kLowPolyNumParams * row + 1] = xd; + A[kLowPolyNumParams * row + 2] = 1; + + for (i = 0; i < kLowPolyNumParams; ++i) { + for (j = 0; j < kLowPolyNumParams; ++j) { + eqns.A[kLowPolyNumParams * i + j] += coords[i] * coords[j]; + } + } + } + } + + // Lazy inverse using existing equation solver. + for (i = 0; i < kLowPolyNumParams; ++i) { + memset(eqns.b, 0, sizeof(*eqns.b) * kLowPolyNumParams); + eqns.b[i] = 1; + equation_system_solve(&eqns); + + for (j = 0; j < kLowPolyNumParams; ++j) { + AtA_inv[j * kLowPolyNumParams + i] = eqns.x[j]; + } + } + equation_system_free(&eqns); + return 1; +} + +void aom_flat_block_finder_free(aom_flat_block_finder_t *block_finder) { + if (!block_finder) return; + aom_free(block_finder->A); + aom_free(block_finder->AtA_inv); + memset(block_finder, 0, sizeof(*block_finder)); +} + +void aom_flat_block_finder_extract_block( + const aom_flat_block_finder_t *block_finder, const uint8_t *const data, + int w, int h, int stride, int offsx, int offsy, double *plane, + double *block) { + const int block_size = block_finder->block_size; + const int n = block_size * block_size; + const double *A = block_finder->A; + const double *AtA_inv = block_finder->AtA_inv; + double plane_coords[kLowPolyNumParams]; + double AtA_inv_b[kLowPolyNumParams]; + int xi, yi, i; + + if (block_finder->use_highbd) { + const uint16_t *const data16 = (const uint16_t *const)data; + for (yi = 0; yi < block_size; ++yi) { + const int y = clamp(offsy + yi, 0, h - 1); + for (xi = 0; xi < block_size; ++xi) { + const int x = clamp(offsx + xi, 0, w - 1); + block[yi * block_size + xi] = + ((double)data16[y * stride + x]) / block_finder->normalization; + } + } + } else { + for (yi = 0; yi < block_size; ++yi) { + const int y = clamp(offsy + yi, 0, h - 1); + for (xi = 0; xi < block_size; ++xi) { + const int x = clamp(offsx + xi, 0, w - 1); + block[yi * block_size + xi] = + ((double)data[y * stride + x]) / block_finder->normalization; + } + } + } + multiply_mat(block, A, AtA_inv_b, 1, n, kLowPolyNumParams); + multiply_mat(AtA_inv, AtA_inv_b, plane_coords, kLowPolyNumParams, + kLowPolyNumParams, 1); + multiply_mat(A, plane_coords, plane, n, kLowPolyNumParams, 1); + + for (i = 0; i < n; ++i) { + block[i] -= plane[i]; + } +} + +typedef struct { + int index; + float score; +} index_and_score_t; + +static int compare_scores(const void *a, const void *b) { + const float diff = + ((index_and_score_t *)a)->score - ((index_and_score_t *)b)->score; + if (diff < 0) + return -1; + else if (diff > 0) + return 1; + return 0; +} + +int aom_flat_block_finder_run(const aom_flat_block_finder_t *block_finder, + const uint8_t *const data, int w, int h, + int stride, uint8_t *flat_blocks) { + // The gradient-based features used in this code are based on: + // A. Kokaram, D. Kelly, H. Denman and A. Crawford, "Measuring noise + // correlation for improved video denoising," 2012 19th, ICIP. + // The thresholds are more lenient to allow for correct grain modeling + // if extreme cases. + const int block_size = block_finder->block_size; + const int n = block_size * block_size; + const double kTraceThreshold = 0.15 / (32 * 32); + const double kRatioThreshold = 1.25; + const double kNormThreshold = 0.08 / (32 * 32); + const double kVarThreshold = 0.005 / (double)n; + const int num_blocks_w = (w + block_size - 1) / block_size; + const int num_blocks_h = (h + block_size - 1) / block_size; + int num_flat = 0; + int bx = 0, by = 0; + double *plane = (double *)aom_malloc(n * sizeof(*plane)); + double *block = (double *)aom_malloc(n * sizeof(*block)); + index_and_score_t *scores = (index_and_score_t *)aom_malloc( + num_blocks_w * num_blocks_h * sizeof(*scores)); + if (plane == NULL || block == NULL || scores == NULL) { + fprintf(stderr, "Failed to allocate memory for block of size %d\n", n); + aom_free(plane); + aom_free(block); + aom_free(scores); + return -1; + } + +#ifdef NOISE_MODEL_LOG_SCORE + fprintf(stderr, "score = ["); +#endif + for (by = 0; by < num_blocks_h; ++by) { + for (bx = 0; bx < num_blocks_w; ++bx) { + // Compute gradient covariance matrix. + double Gxx = 0, Gxy = 0, Gyy = 0; + double var = 0; + double mean = 0; + int xi, yi; + aom_flat_block_finder_extract_block(block_finder, data, w, h, stride, + bx * block_size, by * block_size, + plane, block); + + for (yi = 1; yi < block_size - 1; ++yi) { + for (xi = 1; xi < block_size - 1; ++xi) { + const double gx = (block[yi * block_size + xi + 1] - + block[yi * block_size + xi - 1]) / + 2; + const double gy = (block[yi * block_size + xi + block_size] - + block[yi * block_size + xi - block_size]) / + 2; + Gxx += gx * gx; + Gxy += gx * gy; + Gyy += gy * gy; + + mean += block[yi * block_size + xi]; + var += block[yi * block_size + xi] * block[yi * block_size + xi]; + } + } + mean /= (block_size - 2) * (block_size - 2); + + // Normalize gradients by block_size. + Gxx /= ((block_size - 2) * (block_size - 2)); + Gxy /= ((block_size - 2) * (block_size - 2)); + Gyy /= ((block_size - 2) * (block_size - 2)); + var = var / ((block_size - 2) * (block_size - 2)) - mean * mean; + + { + const double trace = Gxx + Gyy; + const double det = Gxx * Gyy - Gxy * Gxy; + const double e1 = (trace + sqrt(trace * trace - 4 * det)) / 2.; + const double e2 = (trace - sqrt(trace * trace - 4 * det)) / 2.; + const double norm = e1; // Spectral norm + const double ratio = (e1 / AOMMAX(e2, 1e-6)); + const int is_flat = (trace < kTraceThreshold) && + (ratio < kRatioThreshold) && + (norm < kNormThreshold) && (var > kVarThreshold); + // The following weights are used to combine the above features to give + // a sigmoid score for flatness. If the input was normalized to [0,100] + // the magnitude of these values would be close to 1 (e.g., weights + // corresponding to variance would be a factor of 10000x smaller). + // The weights are given in the following order: + // [{var}, {ratio}, {trace}, {norm}, offset] + // with one of the most discriminative being simply the variance. + const double weights[5] = { -6682, -0.2056, 13087, -12434, 2.5694 }; + const float score = + (float)(1.0 / (1 + exp(-(weights[0] * var + weights[1] * ratio + + weights[2] * trace + weights[3] * norm + + weights[4])))); + flat_blocks[by * num_blocks_w + bx] = is_flat ? 255 : 0; + scores[by * num_blocks_w + bx].score = var > kVarThreshold ? score : 0; + scores[by * num_blocks_w + bx].index = by * num_blocks_w + bx; +#ifdef NOISE_MODEL_LOG_SCORE + fprintf(stderr, "%g %g %g %g %g %d ", score, var, ratio, trace, norm, + is_flat); +#endif + num_flat += is_flat; + } + } +#ifdef NOISE_MODEL_LOG_SCORE + fprintf(stderr, "\n"); +#endif + } +#ifdef NOISE_MODEL_LOG_SCORE + fprintf(stderr, "];\n"); +#endif + // Find the top-scored blocks (most likely to be flat) and set the flat blocks + // be the union of the thresholded results and the top 10th percentile of the + // scored results. + qsort(scores, num_blocks_w * num_blocks_h, sizeof(*scores), &compare_scores); + const int top_nth_percentile = num_blocks_w * num_blocks_h * 90 / 100; + const float score_threshold = scores[top_nth_percentile].score; + for (int i = 0; i < num_blocks_w * num_blocks_h; ++i) { + if (scores[i].score >= score_threshold) { + num_flat += flat_blocks[scores[i].index] == 0; + flat_blocks[scores[i].index] |= 1; + } + } + aom_free(block); + aom_free(plane); + aom_free(scores); + return num_flat; +} + +int aom_noise_model_init(aom_noise_model_t *model, + const aom_noise_model_params_t params) { + const int n = num_coeffs(params); + const int lag = params.lag; + const int bit_depth = params.bit_depth; + int x = 0, y = 0, i = 0, c = 0; + + memset(model, 0, sizeof(*model)); + if (params.lag < 1) { + fprintf(stderr, "Invalid noise param: lag = %d must be >= 1\n", params.lag); + return 0; + } + if (params.lag > kMaxLag) { + fprintf(stderr, "Invalid noise param: lag = %d must be <= %d\n", params.lag, + kMaxLag); + return 0; + } + + memcpy(&model->params, ¶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; + } + memset(film_grain, 0, sizeof(*film_grain)); + + film_grain->apply_grain = 1; + film_grain->update_parameters = 1; + + film_grain->ar_coeff_lag = noise_model->params.lag; + + // Convert the scaling functions to 8 bit values + aom_noise_strength_lut_t scaling_points[3]; + aom_noise_strength_solver_fit_piecewise( + &noise_model->combined_state[0].strength_solver, 14, scaling_points + 0); + aom_noise_strength_solver_fit_piecewise( + &noise_model->combined_state[1].strength_solver, 10, scaling_points + 1); + aom_noise_strength_solver_fit_piecewise( + &noise_model->combined_state[2].strength_solver, 10, scaling_points + 2); + + // Both the domain and the range of the scaling functions in the film_grain + // are normalized to 8-bit (e.g., they are implicitly scaled during grain + // synthesis). + const double strength_divisor = 1 << (noise_model->params.bit_depth - 8); + double max_scaling_value = 1e-4; + for (int c = 0; c < 3; ++c) { + for (int i = 0; i < scaling_points[c].num_points; ++i) { + scaling_points[c].points[i][0] = + AOMMIN(255, scaling_points[c].points[i][0] / strength_divisor); + scaling_points[c].points[i][1] = + AOMMIN(255, scaling_points[c].points[i][1] / strength_divisor); + max_scaling_value = + AOMMAX(scaling_points[c].points[i][1], max_scaling_value); + } + } + + // Scaling_shift values are in the range [8,11] + const int max_scaling_value_log2 = + clamp((int)floor(log2(max_scaling_value) + 1), 2, 5); + film_grain->scaling_shift = 5 + (8 - max_scaling_value_log2); + + const double scale_factor = 1 << (8 - max_scaling_value_log2); + film_grain->num_y_points = scaling_points[0].num_points; + film_grain->num_cb_points = scaling_points[1].num_points; + film_grain->num_cr_points = scaling_points[2].num_points; + + int(*film_grain_scaling[3])[2] = { + film_grain->scaling_points_y, + film_grain->scaling_points_cb, + film_grain->scaling_points_cr, + }; + for (int c = 0; c < 3; c++) { + for (int i = 0; i < scaling_points[c].num_points; ++i) { + film_grain_scaling[c][i][0] = (int)(scaling_points[c].points[i][0] + 0.5); + film_grain_scaling[c][i][1] = clamp( + (int)(scale_factor * scaling_points[c].points[i][1] + 0.5), 0, 255); + } + } + aom_noise_strength_lut_free(scaling_points + 0); + aom_noise_strength_lut_free(scaling_points + 1); + aom_noise_strength_lut_free(scaling_points + 2); + + // Convert the ar_coeffs into 8-bit values + const int n_coeff = noise_model->combined_state[0].eqns.n; + double max_coeff = 1e-4, min_coeff = -1e-4; + double y_corr[2] = { 0, 0 }; + double avg_luma_strength = 0; + for (int c = 0; c < 3; c++) { + aom_equation_system_t *eqns = &noise_model->combined_state[c].eqns; + for (int i = 0; i < n_coeff; ++i) { + max_coeff = AOMMAX(max_coeff, eqns->x[i]); + min_coeff = AOMMIN(min_coeff, eqns->x[i]); + } + // Since the correlation between luma/chroma was computed in an already + // scaled space, we adjust it in the un-scaled space. + aom_noise_strength_solver_t *solver = + &noise_model->combined_state[c].strength_solver; + // Compute a weighted average of the strength for the channel. + double average_strength = 0, total_weight = 0; + for (int i = 0; i < solver->eqns.n; ++i) { + double w = 0; + for (int j = 0; j < solver->eqns.n; ++j) { + w += solver->eqns.A[i * solver->eqns.n + j]; + } + w = sqrt(w); + average_strength += solver->eqns.x[i] * w; + total_weight += w; + } + if (total_weight == 0) + average_strength = 1; + else + average_strength /= total_weight; + if (c == 0) { + avg_luma_strength = average_strength; + } else { + y_corr[c - 1] = avg_luma_strength * eqns->x[n_coeff] / average_strength; + max_coeff = AOMMAX(max_coeff, y_corr[c - 1]); + min_coeff = AOMMIN(min_coeff, y_corr[c - 1]); + } + } + // Shift value: AR coeffs range (values 6-9) + // 6: [-2, 2), 7: [-1, 1), 8: [-0.5, 0.5), 9: [-0.25, 0.25) + film_grain->ar_coeff_shift = + clamp(7 - (int)AOMMAX(1 + floor(log2(max_coeff)), ceil(log2(-min_coeff))), + 6, 9); + double scale_ar_coeff = 1 << film_grain->ar_coeff_shift; + int *ar_coeffs[3] = { + film_grain->ar_coeffs_y, + film_grain->ar_coeffs_cb, + film_grain->ar_coeffs_cr, + }; + for (int c = 0; c < 3; ++c) { + aom_equation_system_t *eqns = &noise_model->combined_state[c].eqns; + for (int i = 0; i < n_coeff; ++i) { + ar_coeffs[c][i] = + clamp((int)round(scale_ar_coeff * eqns->x[i]), -128, 127); + } + if (c > 0) { + ar_coeffs[c][n_coeff] = + clamp((int)round(scale_ar_coeff * y_corr[c - 1]), -128, 127); + } + } + + // At the moment, the noise modeling code assumes that the chroma scaling + // functions are a function of luma. + film_grain->cb_mult = 128; // 8 bits + film_grain->cb_luma_mult = 192; // 8 bits + film_grain->cb_offset = 256; // 9 bits + + film_grain->cr_mult = 128; // 8 bits + film_grain->cr_luma_mult = 192; // 8 bits + film_grain->cr_offset = 256; // 9 bits + + film_grain->chroma_scaling_from_luma = 0; + film_grain->grain_scale_shift = 0; + film_grain->overlap_flag = 1; + return 1; +} + +static void pointwise_multiply(const float *a, float *b, int n) { + for (int i = 0; i < n; ++i) { + b[i] *= a[i]; + } +} + +static float *get_half_cos_window(int block_size) { + float *window_function = + (float *)aom_malloc(block_size * block_size * sizeof(*window_function)); + for (int y = 0; y < block_size; ++y) { + const double cos_yd = cos((.5 + y) * PI / block_size - PI / 2); + for (int x = 0; x < block_size; ++x) { + const double cos_xd = cos((.5 + x) * PI / block_size - PI / 2); + window_function[y * block_size + x] = (float)(cos_yd * cos_xd); + } + } + return window_function; +} + +#define DITHER_AND_QUANTIZE(INT_TYPE, suffix) \ + static void dither_and_quantize_##suffix( \ + float *result, int result_stride, INT_TYPE *denoised, int w, int h, \ + int stride, int chroma_sub_w, int chroma_sub_h, int block_size, \ + float block_normalization) { \ + for (int y = 0; y < (h >> chroma_sub_h); ++y) { \ + for (int x = 0; x < (w >> chroma_sub_w); ++x) { \ + const int result_idx = \ + (y + (block_size >> chroma_sub_h)) * result_stride + x + \ + (block_size >> chroma_sub_w); \ + INT_TYPE new_val = (INT_TYPE)AOMMIN( \ + AOMMAX(result[result_idx] * block_normalization + 0.5f, 0), \ + block_normalization); \ + const float err = \ + -(((float)new_val) / block_normalization - result[result_idx]); \ + denoised[y * stride + x] = new_val; \ + if (x + 1 < (w >> chroma_sub_w)) { \ + result[result_idx + 1] += err * 7.0f / 16.0f; \ + } \ + if (y + 1 < (h >> chroma_sub_h)) { \ + if (x > 0) { \ + result[result_idx + result_stride - 1] += err * 3.0f / 16.0f; \ + } \ + result[result_idx + result_stride] += err * 5.0f / 16.0f; \ + if (x + 1 < (w >> chroma_sub_w)) { \ + result[result_idx + result_stride + 1] += err * 1.0f / 16.0f; \ + } \ + } \ + } \ + } \ + } + +DITHER_AND_QUANTIZE(uint8_t, lowbd); +DITHER_AND_QUANTIZE(uint16_t, highbd); + +int aom_wiener_denoise_2d(const uint8_t *const data[3], uint8_t *denoised[3], + int w, int h, int stride[3], int chroma_sub[2], + float *noise_psd[3], int block_size, int bit_depth, + int use_highbd) { + float *plane = NULL, *block = NULL, *window_full = NULL, + *window_chroma = NULL; + double *block_d = NULL, *plane_d = NULL; + struct aom_noise_tx_t *tx_full = NULL; + struct aom_noise_tx_t *tx_chroma = NULL; + const int num_blocks_w = (w + block_size - 1) / block_size; + const int num_blocks_h = (h + block_size - 1) / block_size; + const int result_stride = (num_blocks_w + 2) * block_size; + const int result_height = (num_blocks_h + 2) * block_size; + float *result = NULL; + int init_success = 1; + aom_flat_block_finder_t block_finder_full; + aom_flat_block_finder_t block_finder_chroma; + const float kBlockNormalization = (float)((1 << bit_depth) - 1); + if (chroma_sub[0] != chroma_sub[1]) { + fprintf(stderr, + "aom_wiener_denoise_2d doesn't handle different chroma " + "subsampling"); + return 0; + } + init_success &= aom_flat_block_finder_init(&block_finder_full, block_size, + bit_depth, use_highbd); + result = (float *)aom_malloc((num_blocks_h + 2) * block_size * result_stride * + sizeof(*result)); + plane = (float *)aom_malloc(block_size * block_size * sizeof(*plane)); + block = + (float *)aom_memalign(32, 2 * block_size * block_size * sizeof(*block)); + block_d = (double *)aom_malloc(block_size * block_size * sizeof(*block_d)); + plane_d = (double *)aom_malloc(block_size * block_size * sizeof(*plane_d)); + window_full = get_half_cos_window(block_size); + tx_full = aom_noise_tx_malloc(block_size); + + if (chroma_sub[0] != 0) { + init_success &= aom_flat_block_finder_init(&block_finder_chroma, + block_size >> chroma_sub[0], + bit_depth, use_highbd); + window_chroma = get_half_cos_window(block_size >> chroma_sub[0]); + tx_chroma = aom_noise_tx_malloc(block_size >> chroma_sub[0]); + } else { + window_chroma = window_full; + tx_chroma = tx_full; + } + + init_success &= (tx_full != NULL) && (tx_chroma != NULL) && (plane != NULL) && + (plane_d != NULL) && (block != NULL) && (block_d != NULL) && + (window_full != NULL) && (window_chroma != NULL) && + (result != NULL); + for (int c = init_success ? 0 : 3; c < 3; ++c) { + float *window_function = c == 0 ? window_full : window_chroma; + aom_flat_block_finder_t *block_finder = &block_finder_full; + const int chroma_sub_h = c > 0 ? chroma_sub[1] : 0; + const int chroma_sub_w = c > 0 ? chroma_sub[0] : 0; + struct aom_noise_tx_t *tx = + (c > 0 && chroma_sub[0] > 0) ? tx_chroma : tx_full; + if (!data[c] || !denoised[c]) continue; + if (c > 0 && chroma_sub[0] != 0) { + block_finder = &block_finder_chroma; + } + memset(result, 0, sizeof(*result) * result_stride * result_height); + // Do overlapped block processing (half overlapped). The block rows can + // easily be done in parallel + for (int offsy = 0; offsy < (block_size >> chroma_sub_h); + offsy += (block_size >> chroma_sub_h) / 2) { + for (int offsx = 0; offsx < (block_size >> chroma_sub_w); + offsx += (block_size >> chroma_sub_w) / 2) { + // Pad the boundary when processing each block-set. + for (int by = -1; by < num_blocks_h; ++by) { + for (int bx = -1; bx < num_blocks_w; ++bx) { + const int pixels_per_block = + (block_size >> chroma_sub_w) * (block_size >> chroma_sub_h); + aom_flat_block_finder_extract_block( + block_finder, data[c], w >> chroma_sub_w, h >> chroma_sub_h, + stride[c], bx * (block_size >> chroma_sub_w) + offsx, + by * (block_size >> chroma_sub_h) + offsy, plane_d, block_d); + for (int j = 0; j < pixels_per_block; ++j) { + block[j] = (float)block_d[j]; + plane[j] = (float)plane_d[j]; + } + pointwise_multiply(window_function, block, pixels_per_block); + aom_noise_tx_forward(tx, block); + aom_noise_tx_filter(tx, noise_psd[c]); + aom_noise_tx_inverse(tx, block); + + // Apply window function to the plane approximation (we will apply + // it to the sum of plane + block when composing the results). + pointwise_multiply(window_function, plane, pixels_per_block); + + for (int y = 0; y < (block_size >> chroma_sub_h); ++y) { + const int y_result = + y + (by + 1) * (block_size >> chroma_sub_h) + offsy; + for (int x = 0; x < (block_size >> chroma_sub_w); ++x) { + const int x_result = + x + (bx + 1) * (block_size >> chroma_sub_w) + offsx; + result[y_result * result_stride + x_result] += + (block[y * (block_size >> chroma_sub_w) + x] + + plane[y * (block_size >> chroma_sub_w) + x]) * + window_function[y * (block_size >> chroma_sub_w) + x]; + } + } + } + } + } + } + if (use_highbd) { + dither_and_quantize_highbd(result, result_stride, (uint16_t *)denoised[c], + w, h, stride[c], chroma_sub_w, chroma_sub_h, + block_size, kBlockNormalization); + } else { + dither_and_quantize_lowbd(result, result_stride, denoised[c], w, h, + stride[c], chroma_sub_w, chroma_sub_h, + block_size, kBlockNormalization); + } + } + aom_free(result); + aom_free(plane); + aom_free(block); + aom_free(plane_d); + aom_free(block_d); + aom_free(window_full); + + aom_noise_tx_free(tx_full); + + aom_flat_block_finder_free(&block_finder_full); + if (chroma_sub[0] != 0) { + aom_flat_block_finder_free(&block_finder_chroma); + aom_free(window_chroma); + aom_noise_tx_free(tx_chroma); + } + return init_success; +} |