| /* |
| * 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/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 |
| |
| 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(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; |
| 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; |
| 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(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 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; |
| |
| 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(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]; |
| |
| 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(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); |
| |
| 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(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); |
| } |
| |
| 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(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; |
| 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; |
| } |
