/* * 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 #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; }