Google Git
Sign in
aomedia / avm / 1b4a29491d764f1220538969fc1ecc865cf1b3ad / . / aom_dsp / noise_model.c
blob: ec9aacdd2e6996f78bfc119255ee0fbe1c356640 [file] [log] [blame]
/*
* Copyright (c) 2021, Alliance for Open Media. All rights reserved
*
* This source code is subject to the terms of the BSD 3-Clause Clear License
* and the Alliance for Open Media Patent License 1.0. If the BSD 3-Clause Clear
* License was not distributed with this source code in the LICENSE file, you
* can obtain it at aomedia.org/license/software-license/bsd-3-c-c/. 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
* aomedia.org/license/patent-license/.
*/
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include "aom_dsp/aom_dsp_common.h"
#include "aom_dsp/mathutils.h"
#include "aom_dsp/noise_model.h"
#include "aom_dsp/noise_util.h"
#include "aom_mem/aom_mem.h"
#include "av1/common/common.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(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) {
return get_block_mean_highbd((const uint16_t *)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(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) {
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);
}
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->num_points = 0;
lut->points = (double(*)[2])aom_malloc(num_points * sizeof(*lut->points));
if (!lut->points) return 0;
lut->num_points = num_points;
memset(lut->points, 0, sizeof(*lut->points) * num_points);
return 1;
}
void aom_noise_strength_lut_free(aom_noise_strength_lut_t *lut) {
if (!lut) return;
aom_free(lut->points);
memset(lut, 0, sizeof(*lut));
}
double aom_noise_strength_lut_eval(const aom_noise_strength_lut_t *lut,
double x) {
int i = 0;
// Constant extrapolation for x < x_0.
if (x < lut->points[0][0]) return lut->points[0][1];
for (i = 0; i < lut->num_points - 1; ++i) {
if (x >= lut->points[i][0] && x <= lut->points[i + 1][0]) {
const double a =
(x - lut->points[i][0]) / (lut->points[i + 1][0] - lut->points[i][0]);
return lut->points[i + 1][1] * a + lut->points[i][1] * (1.0 - a);
}
}
// Constant extrapolation for x > x_{n-1}
return lut->points[lut->num_points - 1][1];
}
static double noise_strength_solver_get_bin_index(
const aom_noise_strength_solver_t *solver, double value) {
const double val =
fclamp(value, solver->min_intensity, solver->max_intensity);
const double range = solver->max_intensity - solver->min_intensity;
return (solver->num_bins - 1) * (val - solver->min_intensity) / range;
}
static double noise_strength_solver_get_value(
const aom_noise_strength_solver_t *solver, double x) {
const double bin = noise_strength_solver_get_bin_index(solver, x);
const int bin_i0 = (int)floor(bin);
const int bin_i1 = AOMMIN(solver->num_bins - 1, bin_i0 + 1);
const double a = bin - bin_i0;
return (1.0 - a) * solver->eqns.x[bin_i0] + a * solver->eqns.x[bin_i1];
}
void aom_noise_strength_solver_add_measurement(
aom_noise_strength_solver_t *solver, double block_mean, double noise_std) {
const double bin = noise_strength_solver_get_bin_index(solver, block_mean);
const int bin_i0 = (int)floor(bin);
const int bin_i1 = AOMMIN(solver->num_bins - 1, bin_i0 + 1);
const double a = bin - bin_i0;
const int n = solver->num_bins;
solver->eqns.A[bin_i0 * n + bin_i0] += (1.0 - a) * (1.0 - a);
solver->eqns.A[bin_i1 * n + bin_i0] += a * (1.0 - a);
solver->eqns.A[bin_i1 * n + bin_i1] += a * a;
solver->eqns.A[bin_i0 * n + bin_i1] += a * (1.0 - a);
solver->eqns.b[bin_i0] += (1.0 - a) * noise_std;
solver->eqns.b[bin_i1] += a * noise_std;
solver->total += noise_std;
solver->num_equations++;
}
int aom_noise_strength_solver_solve(aom_noise_strength_solver_t *solver) {
// Add regularization proportional to the number of constraints
const int n = solver->num_bins;
const double kAlpha = 2.0 * (double)(solver->num_equations) / n;
int result = 0;
double mean = 0;
// Do this in a non-destructive manner so it is not confusing to the caller
double *old_A = solver->eqns.A;
double *A = (double *)aom_malloc(sizeof(*A) * n * n);
if (!A) {
fprintf(stderr, "Unable to allocate copy of A\n");
return 0;
}
memcpy(A, old_A, sizeof(*A) * n * n);
for (int i = 0; i < n; ++i) {
const int i_lo = AOMMAX(0, i - 1);
const int i_hi = AOMMIN(n - 1, i + 1);
A[i * n + i_lo] -= kAlpha;
A[i * n + i] += 2 * kAlpha;
A[i * n + i_hi] -= kAlpha;
}
// Small regularization to give average noise strength
mean = solver->total / solver->num_equations;
for (int i = 0; i < n; ++i) {
A[i * n + i] += 1.0 / 8192.;
solver->eqns.b[i] += mean / 8192.;
}
solver->eqns.A = A;
result = equation_system_solve(&solver->eqns);
solver->eqns.A = old_A;
aom_free(A);
return result;
}
int aom_noise_strength_solver_init(aom_noise_strength_solver_t *solver,
int num_bins, int bit_depth) {
if (!solver) return 0;
memset(solver, 0, sizeof(*solver));
solver->num_bins = num_bins;
solver->min_intensity = 0;
solver->max_intensity = (1 << bit_depth) - 1;
solver->total = 0;
solver->num_equations = 0;
return equation_system_init(&solver->eqns, num_bins);
}
void aom_noise_strength_solver_free(aom_noise_strength_solver_t *solver) {
if (!solver) return;
equation_system_free(&solver->eqns);
}
double aom_noise_strength_solver_get_center(
const aom_noise_strength_solver_t *solver, int i) {
const double range = solver->max_intensity - solver->min_intensity;
const int n = solver->num_bins;
return ((double)i) / (n - 1) * range + solver->min_intensity;
}
// Computes the residual if a point were to be removed from the lut. This is
// calculated as the area between the output of the solver and the line segment
// that would be formed between [x_{i - 1}, x_{i + 1}).
static void update_piecewise_linear_residual(
const aom_noise_strength_solver_t *solver,
const aom_noise_strength_lut_t *lut, double *residual, int start, int end) {
const double dx = 255. / solver->num_bins;
for (int i = AOMMAX(start, 1); i < AOMMIN(end, lut->num_points - 1); ++i) {
const int lower = AOMMAX(0, (int)floor(noise_strength_solver_get_bin_index(
solver, lut->points[i - 1][0])));
const int upper = AOMMIN(solver->num_bins - 1,
(int)ceil(noise_strength_solver_get_bin_index(
solver, lut->points[i + 1][0])));
double r = 0;
for (int j = lower; j <= upper; ++j) {
const double x = aom_noise_strength_solver_get_center(solver, j);
if (x < lut->points[i - 1][0]) continue;
if (x >= lut->points[i + 1][0]) continue;
const double y = solver->eqns.x[j];
const double a = (x - lut->points[i - 1][0]) /
(lut->points[i + 1][0] - lut->points[i - 1][0]);
const double estimate_y =
lut->points[i - 1][1] * (1.0 - a) + lut->points[i + 1][1] * a;
r += fabs(y - estimate_y);
}
residual[i] = r * dx;
}
}
int aom_noise_strength_solver_fit_piecewise(
const aom_noise_strength_solver_t *solver, int max_output_points,
aom_noise_strength_lut_t *lut) {
// The tolerance is normalized to be give consistent results between
// different bit-depths.
const double kTolerance = solver->max_intensity * 0.00625 / 255.0;
if (!aom_noise_strength_lut_init(lut, solver->num_bins)) {
fprintf(stderr, "Failed to init lut\n");
return 0;
}
for (int i = 0; i < solver->num_bins; ++i) {
lut->points[i][0] = aom_noise_strength_solver_get_center(solver, i);
lut->points[i][1] = solver->eqns.x[i];
}
if (max_output_points < 0) {
max_output_points = solver->num_bins;
}
double *residual = aom_malloc(solver->num_bins * sizeof(*residual));
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) {
const int n = block_size * block_size;
aom_equation_system_t eqns;
double *AtA_inv = 0;
double *A = 0;
int x = 0, y = 0, i = 0, j = 0;
block_finder->A = NULL;
block_finder->AtA_inv = NULL;
if (!equation_system_init(&eqns, kLowPolyNumParams)) {
fprintf(stderr, "Failed to init equation system for block_size=%d\n",
block_size);
return 0;
}
AtA_inv = (double *)aom_malloc(kLowPolyNumParams * kLowPolyNumParams *
sizeof(*AtA_inv));
A = (double *)aom_malloc(kLowPolyNumParams * n * sizeof(*A));
if (AtA_inv == NULL || A == NULL) {
fprintf(stderr, "Failed to alloc A or AtA_inv for block_size=%d\n",
block_size);
aom_free(AtA_inv);
aom_free(A);
equation_system_free(&eqns);
return 0;
}
block_finder->A = A;
block_finder->AtA_inv = AtA_inv;
block_finder->block_size = block_size;
block_finder->normalization = (1 << bit_depth) - 1;
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;
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;
}
}
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 };
double sum_weights = weights[0] * var + weights[1] * ratio +
weights[2] * trace + weights[3] * norm +
weights[4];
// clamp the value to [-25.0, 100.0] to prevent overflow
sum_weights = fclamp(sum_weights, -25.0, 100.0);
const float score = (float)(1.0 / (1 + exp(-sum_weights)));
flat_blocks[by * num_blocks_w + bx] = is_flat ? 255 : 0;
scores[by * num_blocks_w + bx].score = var > kVarThreshold ? score : 0;
scores[by * num_blocks_w + bx].index = by * num_blocks_w + bx;
#ifdef NOISE_MODEL_LOG_SCORE
fprintf(stderr, "%g %g %g %g %g %d ", score, var, ratio, trace, norm,
is_flat);
#endif
num_flat += is_flat;
}
}
#ifdef NOISE_MODEL_LOG_SCORE
fprintf(stderr, "\n");
#endif
}
#ifdef NOISE_MODEL_LOG_SCORE
fprintf(stderr, "];\n");
#endif
// Find the top-scored blocks (most likely to be flat) and set the flat blocks
// be the union of the thresholded results and the top 10th percentile of the
// scored results.
qsort(scores, num_blocks_w * num_blocks_h, sizeof(*scores), &compare_scores);
const int top_nth_percentile = num_blocks_w * num_blocks_h * 90 / 100;
const float score_threshold = scores[top_nth_percentile].score;
for (int i = 0; i < num_blocks_w * num_blocks_h; ++i) {
if (scores[i].score >= score_threshold) {
num_flat += flat_blocks[scores[i].index] == 0;
flat_blocks[scores[i].index] |= 1;
}
}
aom_free(block);
aom_free(plane);
aom_free(scores);
return num_flat;
}
int aom_noise_model_init(aom_noise_model_t *model,
const aom_noise_model_params_t params) {
const int n = num_coeffs(params);
const int lag = params.lag;
const int bit_depth = params.bit_depth;
int x = 0, y = 0, i = 0, c = 0;
memset(model, 0, sizeof(*model));
if (params.lag < 1) {
fprintf(stderr, "Invalid noise param: lag = %d must be >= 1\n", params.lag);
return 0;
}
if (params.lag > kMaxLag) {
fprintf(stderr, "Invalid noise param: lag = %d must be <= %d\n", params.lag,
kMaxLag);
return 0;
}
memcpy(&model->params, &params, sizeof(params));
for (c = 0; c < 3; ++c) {
if (!noise_state_init(&model->combined_state[c], n + (c > 0), bit_depth)) {
fprintf(stderr, "Failed to allocate noise state for channel %d\n", c);
aom_noise_model_free(model);
return 0;
}
if (!noise_state_init(&model->latest_state[c], n + (c > 0), bit_depth)) {
fprintf(stderr, "Failed to allocate noise state for channel %d\n", c);
aom_noise_model_free(model);
return 0;
}
}
model->n = n;
model->coords = (int(*)[2])aom_malloc(sizeof(*model->coords) * n);
for (y = -lag; y <= 0; ++y) {
const int max_x = y == 0 ? -1 : lag;
for (x = -lag; x <= max_x; ++x) {
switch (params.shape) {
case AOM_NOISE_SHAPE_DIAMOND:
if (abs(x) <= y + lag) {
model->coords[i][0] = x;
model->coords[i][1] = y;
++i;
}
break;
case AOM_NOISE_SHAPE_SQUARE:
model->coords[i][0] = x;
model->coords[i][1] = y;
++i;
break;
default:
fprintf(stderr, "Invalid shape\n");
aom_noise_model_free(model);
return 0;
}
}
}
assert(i == n);
return 1;
}
void aom_noise_model_free(aom_noise_model_t *model) {
int c = 0;
if (!model) return;
aom_free(model->coords);
for (c = 0; c < 3; ++c) {
equation_system_free(&model->latest_state[c].eqns);
equation_system_free(&model->combined_state[c].eqns);
equation_system_free(&model->latest_state[c].strength_solver.eqns);
equation_system_free(&model->combined_state[c].strength_solver.eqns);
}
memset(model, 0, sizeof(*model));
}
// Extracts the neighborhood defined by coords around point (x, y) from
// the difference between the data and denoised images. Also extracts the
// entry (possibly downsampled) for (x, y) in the alt_data (e.g., luma).
#define EXTRACT_AR_ROW(INT_TYPE, suffix) \
static double extract_ar_row_##suffix( \
int(*coords)[2], int num_coords, const INT_TYPE *const data, \
const INT_TYPE *const denoised, int stride, int sub_log2[2], \
const INT_TYPE *const alt_data, const INT_TYPE *const alt_denoised, \
int alt_stride, int x, int y, double *buffer) { \
for (int i = 0; i < num_coords; ++i) { \
const int x_i = x + coords[i][0], y_i = y + coords[i][1]; \
buffer[i] = \
(double)data[y_i * stride + x_i] - denoised[y_i * stride + x_i]; \
} \
const double val = \
(double)data[y * stride + x] - denoised[y * stride + x]; \
\
if (alt_data && alt_denoised) { \
double avg_data = 0, avg_denoised = 0; \
int num_samples = 0; \
for (int dy_i = 0; dy_i < (1 << sub_log2[1]); dy_i++) { \
const int y_up = (y << sub_log2[1]) + dy_i; \
for (int dx_i = 0; dx_i < (1 << sub_log2[0]); dx_i++) { \
const int x_up = (x << sub_log2[0]) + dx_i; \
avg_data += alt_data[y_up * alt_stride + x_up]; \
avg_denoised += alt_denoised[y_up * alt_stride + x_up]; \
num_samples++; \
} \
} \
buffer[num_coords] = (avg_data - avg_denoised) / num_samples; \
} \
return val; \
}
EXTRACT_AR_ROW(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 = 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);
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);
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]);
// 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(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) {
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);
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);
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];
}
}
}
}
}
}
dither_and_quantize_highbd(result, result_stride, (uint16_t *)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 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) << 1);
ctx->denoised[1] = aom_malloc((sd->uv_stride * sd->uv_height) << 1);
ctx->denoised[2] = aom_malloc((sd->uv_stride * sd->uv_height) << 1);
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)) {
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 };
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;
uint8_t *raw_data[3] = {
(uint8_t *)(sd->y_buffer),
(uint8_t *)(sd->u_buffer),
(uint8_t *)(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)) {
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) << 1);
memcpy(raw_data[1], ctx->denoised[1], (strides[1] * sd->uv_height) << 1);
memcpy(raw_data[2], ctx->denoised[2], (strides[2] * sd->uv_height) << 1);
}
return 1;
}