diff options
Diffstat (limited to '')
| -rw-r--r-- | third_party/aom/aom_dsp/noise_util.c | 221 |
1 files changed, 221 insertions, 0 deletions
diff --git a/third_party/aom/aom_dsp/noise_util.c b/third_party/aom/aom_dsp/noise_util.c new file mode 100644 index 0000000000..87e8e9fecc --- /dev/null +++ b/third_party/aom/aom_dsp/noise_util.c @@ -0,0 +1,221 @@ +/* + * 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 p = c[0] * c[0] + c[1] * c[1]; + 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_malloc(sizeof(*mean_x) * w); + var_x = (double *)aom_malloc(sizeof(*var_x) * w); + mean_y = (double *)aom_malloc(sizeof(*mean_x) * h); + var_y = (double *)aom_malloc(sizeof(*var_y) * h); + + memset(mean_x, 0, sizeof(*mean_x) * w); + memset(var_x, 0, sizeof(*var_x) * w); + memset(mean_y, 0, sizeof(*mean_y) * h); + memset(var_y, 0, sizeof(*var_y) * h); + + 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; +} |