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/*
* 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/noise_util.h"
#include "aom_dsp/fft_common.h"
#include "aom_mem/aom_mem.h"
#include "config/aom_dsp_rtcd.h"
float aom_noise_psd_get_default_value(int block_size, float factor) {
return (factor * factor / 10000) * block_size * block_size / 8;
}
// Internal representation of noise transform. It keeps track of the
// transformed data and a temporary working buffer to use during the
// transform.
struct aom_noise_tx_t {
float *tx_block;
float *temp;
int block_size;
void (*fft)(const float *, float *, float *);
void (*ifft)(const float *, float *, float *);
};
struct aom_noise_tx_t *aom_noise_tx_malloc(int block_size) {
struct aom_noise_tx_t *noise_tx =
(struct aom_noise_tx_t *)aom_malloc(sizeof(struct aom_noise_tx_t));
if (!noise_tx) return NULL;
memset(noise_tx, 0, sizeof(*noise_tx));
switch (block_size) {
case 2:
noise_tx->fft = aom_fft2x2_float;
noise_tx->ifft = aom_ifft2x2_float;
break;
case 4:
noise_tx->fft = aom_fft4x4_float;
noise_tx->ifft = aom_ifft4x4_float;
break;
case 8:
noise_tx->fft = aom_fft8x8_float;
noise_tx->ifft = aom_ifft8x8_float;
break;
case 16:
noise_tx->fft = aom_fft16x16_float;
noise_tx->ifft = aom_ifft16x16_float;
break;
case 32:
noise_tx->fft = aom_fft32x32_float;
noise_tx->ifft = aom_ifft32x32_float;
break;
default:
aom_free(noise_tx);
fprintf(stderr, "Unsupported block size %d\n", block_size);
return NULL;
}
noise_tx->block_size = block_size;
noise_tx->tx_block = (float *)aom_memalign(
32, 2 * sizeof(*noise_tx->tx_block) * block_size * block_size);
noise_tx->temp = (float *)aom_memalign(
32, 2 * sizeof(*noise_tx->temp) * block_size * block_size);
if (!noise_tx->tx_block || !noise_tx->temp) {
aom_noise_tx_free(noise_tx);
return NULL;
}
// Clear the buffers up front. Some outputs of the forward transform are
// real only (the imaginary component will never be touched)
memset(noise_tx->tx_block, 0,
2 * sizeof(*noise_tx->tx_block) * block_size * block_size);
memset(noise_tx->temp, 0,
2 * sizeof(*noise_tx->temp) * block_size * block_size);
return noise_tx;
}
void aom_noise_tx_forward(struct aom_noise_tx_t *noise_tx, const float *data) {
noise_tx->fft(data, noise_tx->temp, noise_tx->tx_block);
}
void aom_noise_tx_filter(struct aom_noise_tx_t *noise_tx, const float *psd) {
const int block_size = noise_tx->block_size;
const float kBeta = 1.1f;
const float kEps = 1e-6f;
for (int y = 0; y < block_size; ++y) {
for (int x = 0; x < block_size; ++x) {
int i = y * block_size + x;
float *c = noise_tx->tx_block + 2 * i;
const float c0 = AOMMAX((float)fabs(c[0]), 1e-8f);
const float c1 = AOMMAX((float)fabs(c[1]), 1e-8f);
const float p = c0 * c0 + c1 * c1;
if (p > kBeta * psd[i] && p > 1e-6) {
noise_tx->tx_block[2 * i + 0] *= (p - psd[i]) / AOMMAX(p, kEps);
noise_tx->tx_block[2 * i + 1] *= (p - psd[i]) / AOMMAX(p, kEps);
} else {
noise_tx->tx_block[2 * i + 0] *= (kBeta - 1.0f) / kBeta;
noise_tx->tx_block[2 * i + 1] *= (kBeta - 1.0f) / kBeta;
}
}
}
}
void aom_noise_tx_inverse(struct aom_noise_tx_t *noise_tx, float *data) {
const int n = noise_tx->block_size * noise_tx->block_size;
noise_tx->ifft(noise_tx->tx_block, noise_tx->temp, data);
for (int i = 0; i < n; ++i) {
data[i] /= n;
}
}
void aom_noise_tx_add_energy(const struct aom_noise_tx_t *noise_tx,
float *psd) {
const int block_size = noise_tx->block_size;
for (int yb = 0; yb < block_size; ++yb) {
for (int xb = 0; xb <= block_size / 2; ++xb) {
float *c = noise_tx->tx_block + 2 * (yb * block_size + xb);
psd[yb * block_size + xb] += c[0] * c[0] + c[1] * c[1];
}
}
}
void aom_noise_tx_free(struct aom_noise_tx_t *noise_tx) {
if (!noise_tx) return;
aom_free(noise_tx->tx_block);
aom_free(noise_tx->temp);
aom_free(noise_tx);
}
double aom_normalized_cross_correlation(const double *a, const double *b,
int n) {
double c = 0;
double a_len = 0;
double b_len = 0;
for (int i = 0; i < n; ++i) {
a_len += a[i] * a[i];
b_len += b[i] * b[i];
c += a[i] * b[i];
}
return c / (sqrt(a_len) * sqrt(b_len));
}
int aom_noise_data_validate(const double *data, int w, int h) {
const double kVarianceThreshold = 2;
const double kMeanThreshold = 2;
int x = 0, y = 0;
int ret_value = 1;
double var = 0, mean = 0;
double *mean_x, *mean_y, *var_x, *var_y;
// Check that noise variance is not increasing in x or y
// and that the data is zero mean.
mean_x = (double *)aom_calloc(w, sizeof(*mean_x));
var_x = (double *)aom_calloc(w, sizeof(*var_x));
mean_y = (double *)aom_calloc(h, sizeof(*mean_x));
var_y = (double *)aom_calloc(h, sizeof(*var_y));
if (!(mean_x && var_x && mean_y && var_y)) {
aom_free(mean_x);
aom_free(mean_y);
aom_free(var_x);
aom_free(var_y);
return 0;
}
for (y = 0; y < h; ++y) {
for (x = 0; x < w; ++x) {
const double d = data[y * w + x];
var_x[x] += d * d;
var_y[y] += d * d;
mean_x[x] += d;
mean_y[y] += d;
var += d * d;
mean += d;
}
}
mean /= (w * h);
var = var / (w * h) - mean * mean;
for (y = 0; y < h; ++y) {
mean_y[y] /= h;
var_y[y] = var_y[y] / h - mean_y[y] * mean_y[y];
if (fabs(var_y[y] - var) >= kVarianceThreshold) {
fprintf(stderr, "Variance distance too large %f %f\n", var_y[y], var);
ret_value = 0;
break;
}
if (fabs(mean_y[y] - mean) >= kMeanThreshold) {
fprintf(stderr, "Mean distance too large %f %f\n", mean_y[y], mean);
ret_value = 0;
break;
}
}
for (x = 0; x < w; ++x) {
mean_x[x] /= w;
var_x[x] = var_x[x] / w - mean_x[x] * mean_x[x];
if (fabs(var_x[x] - var) >= kVarianceThreshold) {
fprintf(stderr, "Variance distance too large %f %f\n", var_x[x], var);
ret_value = 0;
break;
}
if (fabs(mean_x[x] - mean) >= kMeanThreshold) {
fprintf(stderr, "Mean distance too large %f %f\n", mean_x[x], mean);
ret_value = 0;
break;
}
}
aom_free(mean_x);
aom_free(mean_y);
aom_free(var_x);
aom_free(var_y);
return ret_value;
}