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/*
* Copyright (c) 2021, 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 <assert.h>
#include "config/aom_config.h"
#include "aom_util/aom_pthread.h"
#if CONFIG_TFLITE
#include "tensorflow/lite/c/c_api.h"
#include "av1/encoder/deltaq4_model.c"
#endif
#include "av1/common/common_data.h"
#include "av1/common/enums.h"
#include "av1/common/idct.h"
#include "av1/common/reconinter.h"
#include "av1/encoder/allintra_vis.h"
#include "av1/encoder/aq_variance.h"
#include "av1/encoder/encoder.h"
#include "av1/encoder/ethread.h"
#include "av1/encoder/hybrid_fwd_txfm.h"
#include "av1/encoder/model_rd.h"
#include "av1/encoder/rdopt_utils.h"
#define MB_WIENER_PRED_BLOCK_SIZE BLOCK_128X128
#define MB_WIENER_PRED_BUF_STRIDE 128
// Maximum delta-q range allowed for Variance Boost after scaling
#define VAR_BOOST_MAX_DELTAQ_RANGE 80
// Maximum quantization step boost allowed for Variance Boost
#define VAR_BOOST_MAX_BOOST 8.0
void av1_alloc_mb_wiener_var_pred_buf(AV1_COMMON *cm, ThreadData *td) {
const int is_high_bitdepth = is_cur_buf_hbd(&td->mb.e_mbd);
assert(MB_WIENER_PRED_BLOCK_SIZE < BLOCK_SIZES_ALL);
const int buf_width = block_size_wide[MB_WIENER_PRED_BLOCK_SIZE];
const int buf_height = block_size_high[MB_WIENER_PRED_BLOCK_SIZE];
assert(buf_width == MB_WIENER_PRED_BUF_STRIDE);
const size_t buf_size =
(buf_width * buf_height * sizeof(*td->wiener_tmp_pred_buf))
<< is_high_bitdepth;
CHECK_MEM_ERROR(cm, td->wiener_tmp_pred_buf, aom_memalign(32, buf_size));
}
void av1_dealloc_mb_wiener_var_pred_buf(ThreadData *td) {
aom_free(td->wiener_tmp_pred_buf);
td->wiener_tmp_pred_buf = NULL;
}
void av1_init_mb_wiener_var_buffer(AV1_COMP *cpi) {
AV1_COMMON *cm = &cpi->common;
// This block size is also used to determine number of workers in
// multi-threading. If it is changed, one needs to change it accordingly in
// "compute_num_ai_workers()".
cpi->weber_bsize = BLOCK_8X8;
if (cpi->oxcf.enable_rate_guide_deltaq) {
if (cpi->mb_weber_stats && cpi->prep_rate_estimates &&
cpi->ext_rate_distribution)
return;
} else {
if (cpi->mb_weber_stats) return;
}
CHECK_MEM_ERROR(cm, cpi->mb_weber_stats,
aom_calloc(cpi->frame_info.mi_rows * cpi->frame_info.mi_cols,
sizeof(*cpi->mb_weber_stats)));
if (cpi->oxcf.enable_rate_guide_deltaq) {
CHECK_MEM_ERROR(
cm, cpi->prep_rate_estimates,
aom_calloc(cpi->frame_info.mi_rows * cpi->frame_info.mi_cols,
sizeof(*cpi->prep_rate_estimates)));
CHECK_MEM_ERROR(
cm, cpi->ext_rate_distribution,
aom_calloc(cpi->frame_info.mi_rows * cpi->frame_info.mi_cols,
sizeof(*cpi->ext_rate_distribution)));
}
}
static int64_t get_satd(AV1_COMP *const cpi, BLOCK_SIZE bsize, int mi_row,
int mi_col) {
AV1_COMMON *const cm = &cpi->common;
const int mi_wide = mi_size_wide[bsize];
const int mi_high = mi_size_high[bsize];
const int mi_step = mi_size_wide[cpi->weber_bsize];
int mb_stride = cpi->frame_info.mi_cols;
int mb_count = 0;
int64_t satd = 0;
for (int row = mi_row; row < mi_row + mi_high; row += mi_step) {
for (int col = mi_col; col < mi_col + mi_wide; col += mi_step) {
if (row >= cm->mi_params.mi_rows || col >= cm->mi_params.mi_cols)
continue;
satd += cpi->mb_weber_stats[(row / mi_step) * mb_stride + (col / mi_step)]
.satd;
++mb_count;
}
}
if (mb_count) satd = (int)(satd / mb_count);
satd = AOMMAX(1, satd);
return (int)satd;
}
static int64_t get_sse(AV1_COMP *const cpi, BLOCK_SIZE bsize, int mi_row,
int mi_col) {
AV1_COMMON *const cm = &cpi->common;
const int mi_wide = mi_size_wide[bsize];
const int mi_high = mi_size_high[bsize];
const int mi_step = mi_size_wide[cpi->weber_bsize];
int mb_stride = cpi->frame_info.mi_cols;
int mb_count = 0;
int64_t distortion = 0;
for (int row = mi_row; row < mi_row + mi_high; row += mi_step) {
for (int col = mi_col; col < mi_col + mi_wide; col += mi_step) {
if (row >= cm->mi_params.mi_rows || col >= cm->mi_params.mi_cols)
continue;
distortion +=
cpi->mb_weber_stats[(row / mi_step) * mb_stride + (col / mi_step)]
.distortion;
++mb_count;
}
}
if (mb_count) distortion = (int)(distortion / mb_count);
distortion = AOMMAX(1, distortion);
return (int)distortion;
}
static double get_max_scale(const AV1_COMP *const cpi, BLOCK_SIZE bsize,
int mi_row, int mi_col) {
const AV1_COMMON *const cm = &cpi->common;
const int mi_wide = mi_size_wide[bsize];
const int mi_high = mi_size_high[bsize];
const int mi_step = mi_size_wide[cpi->weber_bsize];
int mb_stride = cpi->frame_info.mi_cols;
double min_max_scale = 10.0;
for (int row = mi_row; row < mi_row + mi_high; row += mi_step) {
for (int col = mi_col; col < mi_col + mi_wide; col += mi_step) {
if (row >= cm->mi_params.mi_rows || col >= cm->mi_params.mi_cols)
continue;
const WeberStats *weber_stats =
&cpi->mb_weber_stats[(row / mi_step) * mb_stride + (col / mi_step)];
if (weber_stats->max_scale < 1.0) continue;
if (weber_stats->max_scale < min_max_scale)
min_max_scale = weber_stats->max_scale;
}
}
return min_max_scale;
}
static int get_window_wiener_var(const AV1_COMP *const cpi, BLOCK_SIZE bsize,
int mi_row, int mi_col) {
const AV1_COMMON *const cm = &cpi->common;
const int mi_wide = mi_size_wide[bsize];
const int mi_high = mi_size_high[bsize];
const int mi_step = mi_size_wide[cpi->weber_bsize];
int sb_wiener_var = 0;
int mb_stride = cpi->frame_info.mi_cols;
int mb_count = 0;
double base_num = 1;
double base_den = 1;
double base_reg = 1;
for (int row = mi_row; row < mi_row + mi_high; row += mi_step) {
for (int col = mi_col; col < mi_col + mi_wide; col += mi_step) {
if (row >= cm->mi_params.mi_rows || col >= cm->mi_params.mi_cols)
continue;
const WeberStats *weber_stats =
&cpi->mb_weber_stats[(row / mi_step) * mb_stride + (col / mi_step)];
base_num += ((double)weber_stats->distortion) *
sqrt((double)weber_stats->src_variance) *
weber_stats->rec_pix_max;
base_den += fabs(
weber_stats->rec_pix_max * sqrt((double)weber_stats->src_variance) -
weber_stats->src_pix_max * sqrt((double)weber_stats->rec_variance));
base_reg += sqrt((double)weber_stats->distortion) *
sqrt((double)weber_stats->src_pix_max) * 0.1;
++mb_count;
}
}
sb_wiener_var =
(int)(((base_num + base_reg) / (base_den + base_reg)) / mb_count);
sb_wiener_var = AOMMAX(1, sb_wiener_var);
return (int)sb_wiener_var;
}
static int get_var_perceptual_ai(const AV1_COMP *const cpi, BLOCK_SIZE bsize,
int mi_row, int mi_col) {
const AV1_COMMON *const cm = &cpi->common;
const int mi_wide = mi_size_wide[bsize];
const int mi_high = mi_size_high[bsize];
int sb_wiener_var = get_window_wiener_var(cpi, bsize, mi_row, mi_col);
if (mi_row >= (mi_high / 2)) {
sb_wiener_var =
AOMMIN(sb_wiener_var,
get_window_wiener_var(cpi, bsize, mi_row - mi_high / 2, mi_col));
}
if (mi_row <= (cm->mi_params.mi_rows - mi_high - (mi_high / 2))) {
sb_wiener_var =
AOMMIN(sb_wiener_var,
get_window_wiener_var(cpi, bsize, mi_row + mi_high / 2, mi_col));
}
if (mi_col >= (mi_wide / 2)) {
sb_wiener_var =
AOMMIN(sb_wiener_var,
get_window_wiener_var(cpi, bsize, mi_row, mi_col - mi_wide / 2));
}
if (mi_col <= (cm->mi_params.mi_cols - mi_wide - (mi_wide / 2))) {
sb_wiener_var =
AOMMIN(sb_wiener_var,
get_window_wiener_var(cpi, bsize, mi_row, mi_col + mi_wide / 2));
}
return sb_wiener_var;
}
static int rate_estimator(const tran_low_t *qcoeff, int eob, TX_SIZE tx_size) {
const SCAN_ORDER *const scan_order = &av1_scan_orders[tx_size][DCT_DCT];
assert((1 << num_pels_log2_lookup[txsize_to_bsize[tx_size]]) >= eob);
int rate_cost = 1;
for (int idx = 0; idx < eob; ++idx) {
int abs_level = abs(qcoeff[scan_order->scan[idx]]);
rate_cost += (int)(log1p(abs_level) / log(2.0)) + 1 + (abs_level > 0);
}
return (rate_cost << AV1_PROB_COST_SHIFT);
}
void av1_calc_mb_wiener_var_row(AV1_COMP *const cpi, MACROBLOCK *x,
MACROBLOCKD *xd, const int mi_row,
int16_t *src_diff, tran_low_t *coeff,
tran_low_t *qcoeff, tran_low_t *dqcoeff,
double *sum_rec_distortion,
double *sum_est_rate, uint8_t *pred_buffer) {
AV1_COMMON *const cm = &cpi->common;
uint8_t *buffer = cpi->source->y_buffer;
int buf_stride = cpi->source->y_stride;
MB_MODE_INFO mbmi;
memset(&mbmi, 0, sizeof(mbmi));
MB_MODE_INFO *mbmi_ptr = &mbmi;
xd->mi = &mbmi_ptr;
const BLOCK_SIZE bsize = cpi->weber_bsize;
const TX_SIZE tx_size = max_txsize_lookup[bsize];
const int block_size = tx_size_wide[tx_size];
const int coeff_count = block_size * block_size;
const int mb_step = mi_size_wide[bsize];
const BitDepthInfo bd_info = get_bit_depth_info(xd);
const MultiThreadInfo *const mt_info = &cpi->mt_info;
const AV1EncAllIntraMultiThreadInfo *const intra_mt = &mt_info->intra_mt;
AV1EncRowMultiThreadSync *const intra_row_mt_sync =
&cpi->ppi->intra_row_mt_sync;
const int mi_cols = cm->mi_params.mi_cols;
const int mt_thread_id = mi_row / mb_step;
// TODO(chengchen): test different unit step size
const int mt_unit_step = mi_size_wide[MB_WIENER_MT_UNIT_SIZE];
const int mt_unit_cols = (mi_cols + (mt_unit_step >> 1)) / mt_unit_step;
int mt_unit_col = 0;
const int is_high_bitdepth = is_cur_buf_hbd(xd);
uint8_t *dst_buffer = pred_buffer;
const int dst_buffer_stride = MB_WIENER_PRED_BUF_STRIDE;
if (is_high_bitdepth) {
uint16_t *pred_buffer_16 = (uint16_t *)pred_buffer;
dst_buffer = CONVERT_TO_BYTEPTR(pred_buffer_16);
}
for (int mi_col = 0; mi_col < mi_cols; mi_col += mb_step) {
if (mi_col % mt_unit_step == 0) {
intra_mt->intra_sync_read_ptr(intra_row_mt_sync, mt_thread_id,
mt_unit_col);
#if CONFIG_MULTITHREAD
const int num_workers =
AOMMIN(mt_info->num_mod_workers[MOD_AI], mt_info->num_workers);
if (num_workers > 1) {
const AV1EncRowMultiThreadInfo *const enc_row_mt = &mt_info->enc_row_mt;
pthread_mutex_lock(enc_row_mt->mutex_);
const bool exit = enc_row_mt->mb_wiener_mt_exit;
pthread_mutex_unlock(enc_row_mt->mutex_);
// Stop further processing in case any worker has encountered an error.
if (exit) break;
}
#endif
}
PREDICTION_MODE best_mode = DC_PRED;
int best_intra_cost = INT_MAX;
const int mi_width = mi_size_wide[bsize];
const int mi_height = mi_size_high[bsize];
set_mode_info_offsets(&cpi->common.mi_params, &cpi->mbmi_ext_info, x, xd,
mi_row, mi_col);
set_mi_row_col(xd, &xd->tile, mi_row, mi_height, mi_col, mi_width,
AOMMIN(mi_row + mi_height, cm->mi_params.mi_rows),
AOMMIN(mi_col + mi_width, cm->mi_params.mi_cols));
set_plane_n4(xd, mi_size_wide[bsize], mi_size_high[bsize],
av1_num_planes(cm));
xd->mi[0]->bsize = bsize;
xd->mi[0]->motion_mode = SIMPLE_TRANSLATION;
// Set above and left mbmi to NULL as they are not available in the
// preprocessing stage.
// They are used to detemine intra edge filter types in intra prediction.
if (xd->up_available) {
xd->above_mbmi = NULL;
}
if (xd->left_available) {
xd->left_mbmi = NULL;
}
uint8_t *mb_buffer =
buffer + mi_row * MI_SIZE * buf_stride + mi_col * MI_SIZE;
for (PREDICTION_MODE mode = INTRA_MODE_START; mode < INTRA_MODE_END;
++mode) {
// TODO(chengchen): Here we use src instead of reconstructed frame as
// the intra predictor to make single and multithread version match.
// Ideally we want to use the reconstructed.
av1_predict_intra_block(
xd, cm->seq_params->sb_size, cm->seq_params->enable_intra_edge_filter,
block_size, block_size, tx_size, mode, 0, 0, FILTER_INTRA_MODES,
mb_buffer, buf_stride, dst_buffer, dst_buffer_stride, 0, 0, 0);
av1_subtract_block(bd_info, block_size, block_size, src_diff, block_size,
mb_buffer, buf_stride, dst_buffer, dst_buffer_stride);
av1_quick_txfm(0, tx_size, bd_info, src_diff, block_size, coeff);
int intra_cost = aom_satd(coeff, coeff_count);
if (intra_cost < best_intra_cost) {
best_intra_cost = intra_cost;
best_mode = mode;
}
}
av1_predict_intra_block(
xd, cm->seq_params->sb_size, cm->seq_params->enable_intra_edge_filter,
block_size, block_size, tx_size, best_mode, 0, 0, FILTER_INTRA_MODES,
mb_buffer, buf_stride, dst_buffer, dst_buffer_stride, 0, 0, 0);
av1_subtract_block(bd_info, block_size, block_size, src_diff, block_size,
mb_buffer, buf_stride, dst_buffer, dst_buffer_stride);
av1_quick_txfm(0, tx_size, bd_info, src_diff, block_size, coeff);
const struct macroblock_plane *const p = &x->plane[0];
uint16_t eob;
const SCAN_ORDER *const scan_order = &av1_scan_orders[tx_size][DCT_DCT];
QUANT_PARAM quant_param;
int pix_num = 1 << num_pels_log2_lookup[txsize_to_bsize[tx_size]];
av1_setup_quant(tx_size, 0, AV1_XFORM_QUANT_FP, 0, &quant_param);
#if CONFIG_AV1_HIGHBITDEPTH
if (is_cur_buf_hbd(xd)) {
av1_highbd_quantize_fp_facade(coeff, pix_num, p, qcoeff, dqcoeff, &eob,
scan_order, &quant_param);
} else {
av1_quantize_fp_facade(coeff, pix_num, p, qcoeff, dqcoeff, &eob,
scan_order, &quant_param);
}
#else
av1_quantize_fp_facade(coeff, pix_num, p, qcoeff, dqcoeff, &eob, scan_order,
&quant_param);
#endif // CONFIG_AV1_HIGHBITDEPTH
if (cpi->oxcf.enable_rate_guide_deltaq) {
const int rate_cost = rate_estimator(qcoeff, eob, tx_size);
cpi->prep_rate_estimates[(mi_row / mb_step) * cpi->frame_info.mi_cols +
(mi_col / mb_step)] = rate_cost;
}
av1_inverse_transform_block(xd, dqcoeff, 0, DCT_DCT, tx_size, dst_buffer,
dst_buffer_stride, eob, 0);
WeberStats *weber_stats =
&cpi->mb_weber_stats[(mi_row / mb_step) * cpi->frame_info.mi_cols +
(mi_col / mb_step)];
weber_stats->rec_pix_max = 1;
weber_stats->rec_variance = 0;
weber_stats->src_pix_max = 1;
weber_stats->src_variance = 0;
weber_stats->distortion = 0;
int64_t src_mean = 0;
int64_t rec_mean = 0;
int64_t dist_mean = 0;
for (int pix_row = 0; pix_row < block_size; ++pix_row) {
for (int pix_col = 0; pix_col < block_size; ++pix_col) {
int src_pix, rec_pix;
#if CONFIG_AV1_HIGHBITDEPTH
if (is_cur_buf_hbd(xd)) {
uint16_t *src = CONVERT_TO_SHORTPTR(mb_buffer);
uint16_t *rec = CONVERT_TO_SHORTPTR(dst_buffer);
src_pix = src[pix_row * buf_stride + pix_col];
rec_pix = rec[pix_row * dst_buffer_stride + pix_col];
} else {
src_pix = mb_buffer[pix_row * buf_stride + pix_col];
rec_pix = dst_buffer[pix_row * dst_buffer_stride + pix_col];
}
#else
src_pix = mb_buffer[pix_row * buf_stride + pix_col];
rec_pix = dst_buffer[pix_row * dst_buffer_stride + pix_col];
#endif
src_mean += src_pix;
rec_mean += rec_pix;
dist_mean += src_pix - rec_pix;
weber_stats->src_variance += src_pix * src_pix;
weber_stats->rec_variance += rec_pix * rec_pix;
weber_stats->src_pix_max = AOMMAX(weber_stats->src_pix_max, src_pix);
weber_stats->rec_pix_max = AOMMAX(weber_stats->rec_pix_max, rec_pix);
weber_stats->distortion += (src_pix - rec_pix) * (src_pix - rec_pix);
}
}
if (cpi->oxcf.intra_mode_cfg.auto_intra_tools_off) {
*sum_rec_distortion += weber_stats->distortion;
int est_block_rate = 0;
int64_t est_block_dist = 0;
model_rd_sse_fn[MODELRD_LEGACY](cpi, x, bsize, 0, weber_stats->distortion,
pix_num, &est_block_rate,
&est_block_dist);
*sum_est_rate += est_block_rate;
}
weber_stats->src_variance -= (src_mean * src_mean) / pix_num;
weber_stats->rec_variance -= (rec_mean * rec_mean) / pix_num;
weber_stats->distortion -= (dist_mean * dist_mean) / pix_num;
weber_stats->satd = best_intra_cost;
qcoeff[0] = 0;
int max_scale = 0;
for (int idx = 1; idx < coeff_count; ++idx) {
const int abs_qcoeff = abs(qcoeff[idx]);
max_scale = AOMMAX(max_scale, abs_qcoeff);
}
weber_stats->max_scale = max_scale;
if ((mi_col + mb_step) % mt_unit_step == 0 ||
(mi_col + mb_step) >= mi_cols) {
intra_mt->intra_sync_write_ptr(intra_row_mt_sync, mt_thread_id,
mt_unit_col, mt_unit_cols);
++mt_unit_col;
}
}
// Set the pointer to null since mbmi is only allocated inside this function.
xd->mi = NULL;
}
static void calc_mb_wiener_var(AV1_COMP *const cpi, double *sum_rec_distortion,
double *sum_est_rate) {
MACROBLOCK *x = &cpi->td.mb;
MACROBLOCKD *xd = &x->e_mbd;
const BLOCK_SIZE bsize = cpi->weber_bsize;
const int mb_step = mi_size_wide[bsize];
DECLARE_ALIGNED(32, int16_t, src_diff[32 * 32]);
DECLARE_ALIGNED(32, tran_low_t, coeff[32 * 32]);
DECLARE_ALIGNED(32, tran_low_t, qcoeff[32 * 32]);
DECLARE_ALIGNED(32, tran_low_t, dqcoeff[32 * 32]);
for (int mi_row = 0; mi_row < cpi->frame_info.mi_rows; mi_row += mb_step) {
av1_calc_mb_wiener_var_row(cpi, x, xd, mi_row, src_diff, coeff, qcoeff,
dqcoeff, sum_rec_distortion, sum_est_rate,
cpi->td.wiener_tmp_pred_buf);
}
}
static int64_t estimate_wiener_var_norm(AV1_COMP *const cpi,
const BLOCK_SIZE norm_block_size) {
const AV1_COMMON *const cm = &cpi->common;
int64_t norm_factor = 1;
assert(norm_block_size >= BLOCK_16X16 && norm_block_size <= BLOCK_128X128);
const int norm_step = mi_size_wide[norm_block_size];
double sb_wiener_log = 0;
double sb_count = 0;
for (int mi_row = 0; mi_row < cm->mi_params.mi_rows; mi_row += norm_step) {
for (int mi_col = 0; mi_col < cm->mi_params.mi_cols; mi_col += norm_step) {
const int sb_wiener_var =
get_var_perceptual_ai(cpi, norm_block_size, mi_row, mi_col);
const int64_t satd = get_satd(cpi, norm_block_size, mi_row, mi_col);
const int64_t sse = get_sse(cpi, norm_block_size, mi_row, mi_col);
const double scaled_satd = (double)satd / sqrt((double)sse);
sb_wiener_log += scaled_satd * log(sb_wiener_var);
sb_count += scaled_satd;
}
}
if (sb_count > 0) norm_factor = (int64_t)(exp(sb_wiener_log / sb_count));
norm_factor = AOMMAX(1, norm_factor);
return norm_factor;
}
static void automatic_intra_tools_off(AV1_COMP *cpi,
const double sum_rec_distortion,
const double sum_est_rate) {
if (!cpi->oxcf.intra_mode_cfg.auto_intra_tools_off) return;
// Thresholds
const int high_quality_qindex = 128;
const double high_quality_bpp = 2.0;
const double high_quality_dist_per_pix = 4.0;
AV1_COMMON *const cm = &cpi->common;
const int qindex = cm->quant_params.base_qindex;
const double dist_per_pix =
(double)sum_rec_distortion / (cm->width * cm->height);
// The estimate bpp is not accurate, an empirical constant 100 is divided.
const double estimate_bpp = sum_est_rate / (cm->width * cm->height * 100);
if (qindex < high_quality_qindex && estimate_bpp > high_quality_bpp &&
dist_per_pix < high_quality_dist_per_pix) {
cpi->oxcf.intra_mode_cfg.enable_smooth_intra = 0;
cpi->oxcf.intra_mode_cfg.enable_paeth_intra = 0;
cpi->oxcf.intra_mode_cfg.enable_cfl_intra = 0;
cpi->oxcf.intra_mode_cfg.enable_diagonal_intra = 0;
}
}
static void ext_rate_guided_quantization(AV1_COMP *cpi) {
// Calculation uses 8x8.
const int mb_step = mi_size_wide[cpi->weber_bsize];
// Accumulate to 16x16, step size is in the unit of mi.
const int block_step = 4;
const char *filename = cpi->oxcf.rate_distribution_info;
FILE *pfile = fopen(filename, "r");
if (pfile == NULL) {
assert(pfile != NULL);
return;
}
double ext_rate_sum = 0.0;
for (int row = 0; row < cpi->frame_info.mi_rows; row += block_step) {
for (int col = 0; col < cpi->frame_info.mi_cols; col += block_step) {
float val;
const int fields_converted = fscanf(pfile, "%f", &val);
if (fields_converted != 1) {
assert(fields_converted == 1);
fclose(pfile);
return;
}
ext_rate_sum += val;
cpi->ext_rate_distribution[(row / mb_step) * cpi->frame_info.mi_cols +
(col / mb_step)] = val;
}
}
fclose(pfile);
int uniform_rate_sum = 0;
for (int row = 0; row < cpi->frame_info.mi_rows; row += block_step) {
for (int col = 0; col < cpi->frame_info.mi_cols; col += block_step) {
int rate_sum = 0;
for (int r = 0; r < block_step; r += mb_step) {
for (int c = 0; c < block_step; c += mb_step) {
const int mi_row = row + r;
const int mi_col = col + c;
rate_sum += cpi->prep_rate_estimates[(mi_row / mb_step) *
cpi->frame_info.mi_cols +
(mi_col / mb_step)];
}
}
uniform_rate_sum += rate_sum;
}
}
const double scale = uniform_rate_sum / ext_rate_sum;
cpi->ext_rate_scale = scale;
}
void av1_set_mb_wiener_variance(AV1_COMP *cpi) {
AV1_COMMON *const cm = &cpi->common;
const SequenceHeader *const seq_params = cm->seq_params;
if (aom_realloc_frame_buffer(
&cm->cur_frame->buf, cm->width, cm->height, seq_params->subsampling_x,
seq_params->subsampling_y, seq_params->use_highbitdepth,
cpi->oxcf.border_in_pixels, cm->features.byte_alignment, NULL, NULL,
NULL, cpi->alloc_pyramid, 0))
aom_internal_error(cm->error, AOM_CODEC_MEM_ERROR,
"Failed to allocate frame buffer");
av1_alloc_mb_wiener_var_pred_buf(&cpi->common, &cpi->td);
cpi->norm_wiener_variance = 0;
MACROBLOCK *x = &cpi->td.mb;
MACROBLOCKD *xd = &x->e_mbd;
// xd->mi needs to be setup since it is used in av1_frame_init_quantizer.
MB_MODE_INFO mbmi;
memset(&mbmi, 0, sizeof(mbmi));
MB_MODE_INFO *mbmi_ptr = &mbmi;
xd->mi = &mbmi_ptr;
cm->quant_params.base_qindex = cpi->oxcf.rc_cfg.cq_level;
av1_frame_init_quantizer(cpi);
double sum_rec_distortion = 0.0;
double sum_est_rate = 0.0;
MultiThreadInfo *const mt_info = &cpi->mt_info;
const int num_workers =
AOMMIN(mt_info->num_mod_workers[MOD_AI], mt_info->num_workers);
AV1EncAllIntraMultiThreadInfo *const intra_mt = &mt_info->intra_mt;
intra_mt->intra_sync_read_ptr = av1_row_mt_sync_read_dummy;
intra_mt->intra_sync_write_ptr = av1_row_mt_sync_write_dummy;
// Calculate differential contrast for each block for the entire image.
// TODO(chengchen): properly accumulate the distortion and rate in
// av1_calc_mb_wiener_var_mt(). Until then, call calc_mb_wiener_var() if
// auto_intra_tools_off is true.
if (num_workers > 1 && !cpi->oxcf.intra_mode_cfg.auto_intra_tools_off) {
intra_mt->intra_sync_read_ptr = av1_row_mt_sync_read;
intra_mt->intra_sync_write_ptr = av1_row_mt_sync_write;
av1_calc_mb_wiener_var_mt(cpi, num_workers, &sum_rec_distortion,
&sum_est_rate);
} else {
calc_mb_wiener_var(cpi, &sum_rec_distortion, &sum_est_rate);
}
// Determine whether to turn off several intra coding tools.
automatic_intra_tools_off(cpi, sum_rec_distortion, sum_est_rate);
// Read external rate distribution and use it to guide delta quantization
if (cpi->oxcf.enable_rate_guide_deltaq) ext_rate_guided_quantization(cpi);
const BLOCK_SIZE norm_block_size = cm->seq_params->sb_size;
cpi->norm_wiener_variance = estimate_wiener_var_norm(cpi, norm_block_size);
const int norm_step = mi_size_wide[norm_block_size];
double sb_wiener_log = 0;
double sb_count = 0;
for (int its_cnt = 0; its_cnt < 2; ++its_cnt) {
sb_wiener_log = 0;
sb_count = 0;
for (int mi_row = 0; mi_row < cm->mi_params.mi_rows; mi_row += norm_step) {
for (int mi_col = 0; mi_col < cm->mi_params.mi_cols;
mi_col += norm_step) {
int sb_wiener_var =
get_var_perceptual_ai(cpi, norm_block_size, mi_row, mi_col);
double beta = (double)cpi->norm_wiener_variance / sb_wiener_var;
double min_max_scale = AOMMAX(
1.0, get_max_scale(cpi, cm->seq_params->sb_size, mi_row, mi_col));
beta = AOMMIN(beta, 4);
beta = AOMMAX(beta, 0.25);
if (beta < 1 / min_max_scale) continue;
sb_wiener_var = (int)(cpi->norm_wiener_variance / beta);
int64_t satd = get_satd(cpi, norm_block_size, mi_row, mi_col);
int64_t sse = get_sse(cpi, norm_block_size, mi_row, mi_col);
double scaled_satd = (double)satd / sqrt((double)sse);
sb_wiener_log += scaled_satd * log(sb_wiener_var);
sb_count += scaled_satd;
}
}
if (sb_count > 0)
cpi->norm_wiener_variance = (int64_t)(exp(sb_wiener_log / sb_count));
cpi->norm_wiener_variance = AOMMAX(1, cpi->norm_wiener_variance);
}
// Set the pointer to null since mbmi is only allocated inside this function.
xd->mi = NULL;
aom_free_frame_buffer(&cm->cur_frame->buf);
av1_dealloc_mb_wiener_var_pred_buf(&cpi->td);
}
static int get_rate_guided_quantizer(const AV1_COMP *const cpi,
BLOCK_SIZE bsize, int mi_row, int mi_col) {
// Calculation uses 8x8.
const int mb_step = mi_size_wide[cpi->weber_bsize];
// Accumulate to 16x16
const int block_step = mi_size_wide[BLOCK_16X16];
double sb_rate_hific = 0.0;
double sb_rate_uniform = 0.0;
for (int row = mi_row; row < mi_row + mi_size_wide[bsize];
row += block_step) {
for (int col = mi_col; col < mi_col + mi_size_high[bsize];
col += block_step) {
sb_rate_hific +=
cpi->ext_rate_distribution[(row / mb_step) * cpi->frame_info.mi_cols +
(col / mb_step)];
for (int r = 0; r < block_step; r += mb_step) {
for (int c = 0; c < block_step; c += mb_step) {
const int this_row = row + r;
const int this_col = col + c;
sb_rate_uniform +=
cpi->prep_rate_estimates[(this_row / mb_step) *
cpi->frame_info.mi_cols +
(this_col / mb_step)];
}
}
}
}
sb_rate_hific *= cpi->ext_rate_scale;
const double weight = 1.0;
const double rate_diff =
weight * (sb_rate_hific - sb_rate_uniform) / sb_rate_uniform;
double scale = pow(2, rate_diff);
scale = scale * scale;
double min_max_scale = AOMMAX(1.0, get_max_scale(cpi, bsize, mi_row, mi_col));
scale = 1.0 / AOMMIN(1.0 / scale, min_max_scale);
const AV1_COMMON *const cm = &cpi->common;
const int base_qindex = cm->quant_params.base_qindex;
int offset =
av1_get_deltaq_offset(cm->seq_params->bit_depth, base_qindex, scale);
const DeltaQInfo *const delta_q_info = &cm->delta_q_info;
const int max_offset = delta_q_info->delta_q_res * 10;
offset = AOMMIN(offset, max_offset - 1);
offset = AOMMAX(offset, -max_offset + 1);
int qindex = cm->quant_params.base_qindex + offset;
qindex = AOMMIN(qindex, MAXQ);
qindex = AOMMAX(qindex, MINQ);
if (base_qindex > MINQ) qindex = AOMMAX(qindex, MINQ + 1);
return qindex;
}
int av1_get_sbq_perceptual_ai(const AV1_COMP *const cpi, BLOCK_SIZE bsize,
int mi_row, int mi_col) {
if (cpi->oxcf.enable_rate_guide_deltaq) {
return get_rate_guided_quantizer(cpi, bsize, mi_row, mi_col);
}
const AV1_COMMON *const cm = &cpi->common;
const int base_qindex = cm->quant_params.base_qindex;
int sb_wiener_var = get_var_perceptual_ai(cpi, bsize, mi_row, mi_col);
int offset = 0;
double beta = (double)cpi->norm_wiener_variance / sb_wiener_var;
double min_max_scale = AOMMAX(1.0, get_max_scale(cpi, bsize, mi_row, mi_col));
beta = 1.0 / AOMMIN(1.0 / beta, min_max_scale);
// Cap beta such that the delta q value is not much far away from the base q.
beta = AOMMIN(beta, 4);
beta = AOMMAX(beta, 0.25);
offset = av1_get_deltaq_offset(cm->seq_params->bit_depth, base_qindex, beta);
const DeltaQInfo *const delta_q_info = &cm->delta_q_info;
offset = AOMMIN(offset, delta_q_info->delta_q_res * 20 - 1);
offset = AOMMAX(offset, -delta_q_info->delta_q_res * 20 + 1);
int qindex = cm->quant_params.base_qindex + offset;
qindex = AOMMIN(qindex, MAXQ);
qindex = AOMMAX(qindex, MINQ);
if (base_qindex > MINQ) qindex = AOMMAX(qindex, MINQ + 1);
return qindex;
}
void av1_init_mb_ur_var_buffer(AV1_COMP *cpi) {
AV1_COMMON *cm = &cpi->common;
if (cpi->mb_delta_q) return;
CHECK_MEM_ERROR(cm, cpi->mb_delta_q,
aom_calloc(cpi->frame_info.mb_rows * cpi->frame_info.mb_cols,
sizeof(*cpi->mb_delta_q)));
}
#if CONFIG_TFLITE
static int model_predict(BLOCK_SIZE block_size, int num_cols, int num_rows,
int bit_depth, uint8_t *y_buffer, int y_stride,
float *predicts0, float *predicts1) {
// Create the model and interpreter options.
TfLiteModel *model =
TfLiteModelCreate(av1_deltaq4_model_file, av1_deltaq4_model_fsize);
if (model == NULL) return 1;
TfLiteInterpreterOptions *options = TfLiteInterpreterOptionsCreate();
TfLiteInterpreterOptionsSetNumThreads(options, 2);
if (options == NULL) {
TfLiteModelDelete(model);
return 1;
}
// Create the interpreter.
TfLiteInterpreter *interpreter = TfLiteInterpreterCreate(model, options);
if (interpreter == NULL) {
TfLiteInterpreterOptionsDelete(options);
TfLiteModelDelete(model);
return 1;
}
// Allocate tensors and populate the input tensor data.
TfLiteInterpreterAllocateTensors(interpreter);
TfLiteTensor *input_tensor = TfLiteInterpreterGetInputTensor(interpreter, 0);
if (input_tensor == NULL) {
TfLiteInterpreterDelete(interpreter);
TfLiteInterpreterOptionsDelete(options);
TfLiteModelDelete(model);
return 1;
}
size_t input_size = TfLiteTensorByteSize(input_tensor);
float *input_data = aom_calloc(input_size, 1);
if (input_data == NULL) {
TfLiteInterpreterDelete(interpreter);
TfLiteInterpreterOptionsDelete(options);
TfLiteModelDelete(model);
return 1;
}
const int num_mi_w = mi_size_wide[block_size];
const int num_mi_h = mi_size_high[block_size];
for (int row = 0; row < num_rows; ++row) {
for (int col = 0; col < num_cols; ++col) {
const int row_offset = (row * num_mi_h) << 2;
const int col_offset = (col * num_mi_w) << 2;
uint8_t *buf = y_buffer + row_offset * y_stride + col_offset;
int r = row_offset, pos = 0;
const float base = (float)((1 << bit_depth) - 1);
while (r < row_offset + (num_mi_h << 2)) {
for (int c = 0; c < (num_mi_w << 2); ++c) {
input_data[pos++] = bit_depth > 8
? (float)*CONVERT_TO_SHORTPTR(buf + c) / base
: (float)*(buf + c) / base;
}
buf += y_stride;
++r;
}
TfLiteTensorCopyFromBuffer(input_tensor, input_data, input_size);
// Execute inference.
if (TfLiteInterpreterInvoke(interpreter) != kTfLiteOk) {
TfLiteInterpreterDelete(interpreter);
TfLiteInterpreterOptionsDelete(options);
TfLiteModelDelete(model);
return 1;
}
// Extract the output tensor data.
const TfLiteTensor *output_tensor =
TfLiteInterpreterGetOutputTensor(interpreter, 0);
if (output_tensor == NULL) {
TfLiteInterpreterDelete(interpreter);
TfLiteInterpreterOptionsDelete(options);
TfLiteModelDelete(model);
return 1;
}
size_t output_size = TfLiteTensorByteSize(output_tensor);
float output_data[2];
TfLiteTensorCopyToBuffer(output_tensor, output_data, output_size);
predicts0[row * num_cols + col] = output_data[0];
predicts1[row * num_cols + col] = output_data[1];
}
}
// Dispose of the model and interpreter objects.
TfLiteInterpreterDelete(interpreter);
TfLiteInterpreterOptionsDelete(options);
TfLiteModelDelete(model);
aom_free(input_data);
return 0;
}
void av1_set_mb_ur_variance(AV1_COMP *cpi) {
const AV1_COMMON *cm = &cpi->common;
const CommonModeInfoParams *const mi_params = &cm->mi_params;
uint8_t *y_buffer = cpi->source->y_buffer;
const int y_stride = cpi->source->y_stride;
const int block_size = cpi->common.seq_params->sb_size;
const uint32_t bit_depth = cpi->td.mb.e_mbd.bd;
const int num_mi_w = mi_size_wide[block_size];
const int num_mi_h = mi_size_high[block_size];
const int num_cols = (mi_params->mi_cols + num_mi_w - 1) / num_mi_w;
const int num_rows = (mi_params->mi_rows + num_mi_h - 1) / num_mi_h;
// TODO(sdeng): fit a better model_1; disable it at this time.
float *mb_delta_q0, *mb_delta_q1, delta_q_avg0 = 0.0f;
CHECK_MEM_ERROR(cm, mb_delta_q0,
aom_calloc(num_rows * num_cols, sizeof(float)));
CHECK_MEM_ERROR(cm, mb_delta_q1,
aom_calloc(num_rows * num_cols, sizeof(float)));
if (model_predict(block_size, num_cols, num_rows, bit_depth, y_buffer,
y_stride, mb_delta_q0, mb_delta_q1)) {
aom_internal_error(cm->error, AOM_CODEC_ERROR,
"Failed to call TFlite functions.");
}
// Loop through each SB block.
for (int row = 0; row < num_rows; ++row) {
for (int col = 0; col < num_cols; ++col) {
const int index = row * num_cols + col;
delta_q_avg0 += mb_delta_q0[index];
}
}
delta_q_avg0 /= (float)(num_rows * num_cols);
float scaling_factor;
const float cq_level = (float)cpi->oxcf.rc_cfg.cq_level / (float)MAXQ;
if (cq_level < delta_q_avg0) {
scaling_factor = cq_level / delta_q_avg0;
} else {
scaling_factor = 1.0f - (cq_level - delta_q_avg0) / (1.0f - delta_q_avg0);
}
for (int row = 0; row < num_rows; ++row) {
for (int col = 0; col < num_cols; ++col) {
const int index = row * num_cols + col;
cpi->mb_delta_q[index] =
RINT((float)cpi->oxcf.q_cfg.deltaq_strength / 100.0f * (float)MAXQ *
scaling_factor * (mb_delta_q0[index] - delta_q_avg0));
}
}
aom_free(mb_delta_q0);
aom_free(mb_delta_q1);
}
#else // !CONFIG_TFLITE
void av1_set_mb_ur_variance(AV1_COMP *cpi) {
const AV1_COMMON *cm = &cpi->common;
const CommonModeInfoParams *const mi_params = &cm->mi_params;
const MACROBLOCKD *const xd = &cpi->td.mb.e_mbd;
uint8_t *y_buffer = cpi->source->y_buffer;
const int y_stride = cpi->source->y_stride;
const int block_size = cpi->common.seq_params->sb_size;
const int num_mi_w = mi_size_wide[block_size];
const int num_mi_h = mi_size_high[block_size];
const int num_cols = (mi_params->mi_cols + num_mi_w - 1) / num_mi_w;
const int num_rows = (mi_params->mi_rows + num_mi_h - 1) / num_mi_h;
int *mb_delta_q[2];
CHECK_MEM_ERROR(cm, mb_delta_q[0],
aom_calloc(num_rows * num_cols, sizeof(*mb_delta_q[0])));
CHECK_MEM_ERROR(cm, mb_delta_q[1],
aom_calloc(num_rows * num_cols, sizeof(*mb_delta_q[1])));
// Approximates the model change between current version (Spet 2021) and the
// baseline (July 2021).
const double model_change[] = { 3.0, 3.0 };
// The following parameters are fitted from user labeled data.
const double a[] = { -24.50 * 4.0, -17.20 * 4.0 };
const double b[] = { 0.004898, 0.003093 };
const double c[] = { (29.932 + model_change[0]) * 4.0,
(42.100 + model_change[1]) * 4.0 };
int delta_q_avg[2] = { 0, 0 };
// Loop through each SB block.
for (int row = 0; row < num_rows; ++row) {
for (int col = 0; col < num_cols; ++col) {
double var = 0.0, num_of_var = 0.0;
const int index = row * num_cols + col;
// Loop through each 8x8 block.
for (int mi_row = row * num_mi_h;
mi_row < mi_params->mi_rows && mi_row < (row + 1) * num_mi_h;
mi_row += 2) {
for (int mi_col = col * num_mi_w;
mi_col < mi_params->mi_cols && mi_col < (col + 1) * num_mi_w;
mi_col += 2) {
struct buf_2d buf;
const int row_offset_y = mi_row << 2;
const int col_offset_y = mi_col << 2;
buf.buf = y_buffer + row_offset_y * y_stride + col_offset_y;
buf.stride = y_stride;
unsigned int block_variance;
block_variance = av1_get_perpixel_variance_facade(
cpi, xd, &buf, BLOCK_8X8, AOM_PLANE_Y);
block_variance = AOMMAX(block_variance, 1);
var += log((double)block_variance);
num_of_var += 1.0;
}
}
var = exp(var / num_of_var);
mb_delta_q[0][index] = RINT(a[0] * exp(-b[0] * var) + c[0]);
mb_delta_q[1][index] = RINT(a[1] * exp(-b[1] * var) + c[1]);
delta_q_avg[0] += mb_delta_q[0][index];
delta_q_avg[1] += mb_delta_q[1][index];
}
}
delta_q_avg[0] = RINT((double)delta_q_avg[0] / (num_rows * num_cols));
delta_q_avg[1] = RINT((double)delta_q_avg[1] / (num_rows * num_cols));
int model_idx;
double scaling_factor;
const int cq_level = cpi->oxcf.rc_cfg.cq_level;
if (cq_level < delta_q_avg[0]) {
model_idx = 0;
scaling_factor = (double)cq_level / delta_q_avg[0];
} else if (cq_level < delta_q_avg[1]) {
model_idx = 2;
scaling_factor =
(double)(cq_level - delta_q_avg[0]) / (delta_q_avg[1] - delta_q_avg[0]);
} else {
model_idx = 1;
scaling_factor = (double)(MAXQ - cq_level) / (MAXQ - delta_q_avg[1]);
}
const double new_delta_q_avg =
delta_q_avg[0] + scaling_factor * (delta_q_avg[1] - delta_q_avg[0]);
for (int row = 0; row < num_rows; ++row) {
for (int col = 0; col < num_cols; ++col) {
const int index = row * num_cols + col;
if (model_idx == 2) {
const double delta_q =
mb_delta_q[0][index] +
scaling_factor * (mb_delta_q[1][index] - mb_delta_q[0][index]);
cpi->mb_delta_q[index] = RINT((double)cpi->oxcf.q_cfg.deltaq_strength /
100.0 * (delta_q - new_delta_q_avg));
} else {
cpi->mb_delta_q[index] = RINT(
(double)cpi->oxcf.q_cfg.deltaq_strength / 100.0 * scaling_factor *
(mb_delta_q[model_idx][index] - delta_q_avg[model_idx]));
}
}
}
aom_free(mb_delta_q[0]);
aom_free(mb_delta_q[1]);
}
#endif
int av1_get_sbq_user_rating_based(const AV1_COMP *const cpi, int mi_row,
int mi_col) {
const BLOCK_SIZE bsize = cpi->common.seq_params->sb_size;
const CommonModeInfoParams *const mi_params = &cpi->common.mi_params;
const AV1_COMMON *const cm = &cpi->common;
const int base_qindex = cm->quant_params.base_qindex;
if (base_qindex == MINQ || base_qindex == MAXQ) return base_qindex;
const int num_mi_w = mi_size_wide[bsize];
const int num_mi_h = mi_size_high[bsize];
const int num_cols = (mi_params->mi_cols + num_mi_w - 1) / num_mi_w;
const int index = (mi_row / num_mi_h) * num_cols + (mi_col / num_mi_w);
const int delta_q = cpi->mb_delta_q[index];
int qindex = base_qindex + delta_q;
qindex = AOMMIN(qindex, MAXQ);
qindex = AOMMAX(qindex, MINQ + 1);
return qindex;
}
#if !CONFIG_REALTIME_ONLY
// Variance Boost: a variance adaptive quantization implementation
// SVT-AV1 appendix with an overview and a graphical, step-by-step explanation
// of the implementation
int av1_get_sbq_variance_boost(const AV1_COMP *cpi, const MACROBLOCK *x) {
const AV1_COMMON *cm = &cpi->common;
const int base_qindex = cm->quant_params.base_qindex;
const aom_bit_depth_t bit_depth = cm->seq_params->bit_depth;
// Variance Boost only supports 64x64 SBs.
assert(cm->seq_params->sb_size == BLOCK_64X64);
// Strength is currently hard-coded and optimized for still pictures. In the
// future, we might want to expose this as a parameter that can be fine-tuned
// by the caller.
const int strength = 3;
unsigned int variance = av1_get_variance_boost_block_variance(cpi, x);
// Variance = 0 areas are either completely flat patches or have very fine
// gradients. Boost these blocks as if they have a variance of 1.
if (variance == 0) {
variance = 1;
}
// Compute a boost based on a fast-growing formula.
// High and medium variance SBs essentially get no boost, while lower variance
// SBs get increasingly stronger boosts.
assert(strength >= 1 && strength <= 4);
// Still picture curve, with variance crossover point at 1024.
double qstep_ratio = 0.15 * strength * (-log2((double)variance) + 10.0) + 1.0;
qstep_ratio = fclamp(qstep_ratio, 1.0, VAR_BOOST_MAX_BOOST);
double base_q = av1_convert_qindex_to_q(base_qindex, bit_depth);
double target_q = base_q / qstep_ratio;
int target_qindex = av1_convert_q_to_qindex(target_q, bit_depth);
// Determine the SB's delta_q boost by computing an (unscaled) delta_q from
// the base and target q values, then scale that delta_q according to the
// frame's base qindex.
// The scaling coefficients were chosen empirically to maximize SSIMULACRA2
// scores, 10th percentile scores, and subjective quality. Boosts become
// smaller (for a given variance) the lower the base qindex.
int boost = (int)round((base_qindex + 544.0) * (base_qindex - target_qindex) /
1279.0);
boost = AOMMIN(VAR_BOOST_MAX_DELTAQ_RANGE, boost);
// Variance Boost was designed to always operate in the lossy domain, so MINQ
// is excluded.
int sb_qindex = AOMMAX(base_qindex - boost, MINQ + 1);
return sb_qindex;
}
#endif