| /* |
| * Copyright (c) 2012 The WebM project authors. All Rights Reserved. |
| * |
| * Use of this source code is governed by a BSD-style license |
| * that can be found in the LICENSE file in the root of the source |
| * tree. An additional intellectual property rights grant can be found |
| * in the file PATENTS. All contributing project authors may |
| * be found in the AUTHORS file in the root of the source tree. |
| */ |
| |
| #include <limits.h> |
| |
| #include "aom_mem/vpx_mem.h" |
| |
| #include "av1/common/pred_common.h" |
| #include "av1/common/tile_common.h" |
| |
| #include "av1/encoder/cost.h" |
| #include "av1/encoder/segmentation.h" |
| #include "av1/encoder/subexp.h" |
| |
| void vp10_enable_segmentation(struct segmentation *seg) { |
| seg->enabled = 1; |
| seg->update_map = 1; |
| seg->update_data = 1; |
| } |
| |
| void vp10_disable_segmentation(struct segmentation *seg) { |
| seg->enabled = 0; |
| seg->update_map = 0; |
| seg->update_data = 0; |
| } |
| |
| void vp10_set_segment_data(struct segmentation *seg, signed char *feature_data, |
| unsigned char abs_delta) { |
| seg->abs_delta = abs_delta; |
| |
| memcpy(seg->feature_data, feature_data, sizeof(seg->feature_data)); |
| } |
| void vp10_disable_segfeature(struct segmentation *seg, int segment_id, |
| SEG_LVL_FEATURES feature_id) { |
| seg->feature_mask[segment_id] &= ~(1 << feature_id); |
| } |
| |
| void vp10_clear_segdata(struct segmentation *seg, int segment_id, |
| SEG_LVL_FEATURES feature_id) { |
| seg->feature_data[segment_id][feature_id] = 0; |
| } |
| |
| // Based on set of segment counts calculate a probability tree |
| static void calc_segtree_probs(unsigned *segcounts, |
| vpx_prob *segment_tree_probs, |
| const vpx_prob *cur_tree_probs) { |
| // Work out probabilities of each segment |
| const unsigned cc[4] = { segcounts[0] + segcounts[1], |
| segcounts[2] + segcounts[3], |
| segcounts[4] + segcounts[5], |
| segcounts[6] + segcounts[7] }; |
| const unsigned ccc[2] = { cc[0] + cc[1], cc[2] + cc[3] }; |
| int i; |
| |
| segment_tree_probs[0] = get_binary_prob(ccc[0], ccc[1]); |
| segment_tree_probs[1] = get_binary_prob(cc[0], cc[1]); |
| segment_tree_probs[2] = get_binary_prob(cc[2], cc[3]); |
| segment_tree_probs[3] = get_binary_prob(segcounts[0], segcounts[1]); |
| segment_tree_probs[4] = get_binary_prob(segcounts[2], segcounts[3]); |
| segment_tree_probs[5] = get_binary_prob(segcounts[4], segcounts[5]); |
| segment_tree_probs[6] = get_binary_prob(segcounts[6], segcounts[7]); |
| |
| for (i = 0; i < 7; i++) { |
| const unsigned *ct = |
| i == 0 ? ccc : i < 3 ? cc + (i & 2) : segcounts + (i - 3) * 2; |
| vp10_prob_diff_update_savings_search( |
| ct, cur_tree_probs[i], &segment_tree_probs[i], DIFF_UPDATE_PROB); |
| } |
| } |
| |
| // Based on set of segment counts and probabilities calculate a cost estimate |
| static int cost_segmap(unsigned *segcounts, vpx_prob *probs) { |
| const int c01 = segcounts[0] + segcounts[1]; |
| const int c23 = segcounts[2] + segcounts[3]; |
| const int c45 = segcounts[4] + segcounts[5]; |
| const int c67 = segcounts[6] + segcounts[7]; |
| const int c0123 = c01 + c23; |
| const int c4567 = c45 + c67; |
| |
| // Cost the top node of the tree |
| int cost = c0123 * vp10_cost_zero(probs[0]) + c4567 * vp10_cost_one(probs[0]); |
| |
| // Cost subsequent levels |
| if (c0123 > 0) { |
| cost += c01 * vp10_cost_zero(probs[1]) + c23 * vp10_cost_one(probs[1]); |
| |
| if (c01 > 0) |
| cost += segcounts[0] * vp10_cost_zero(probs[3]) + |
| segcounts[1] * vp10_cost_one(probs[3]); |
| if (c23 > 0) |
| cost += segcounts[2] * vp10_cost_zero(probs[4]) + |
| segcounts[3] * vp10_cost_one(probs[4]); |
| } |
| |
| if (c4567 > 0) { |
| cost += c45 * vp10_cost_zero(probs[2]) + c67 * vp10_cost_one(probs[2]); |
| |
| if (c45 > 0) |
| cost += segcounts[4] * vp10_cost_zero(probs[5]) + |
| segcounts[5] * vp10_cost_one(probs[5]); |
| if (c67 > 0) |
| cost += segcounts[6] * vp10_cost_zero(probs[6]) + |
| segcounts[7] * vp10_cost_one(probs[6]); |
| } |
| |
| return cost; |
| } |
| |
| static void count_segs(const VP10_COMMON *cm, MACROBLOCKD *xd, |
| const TileInfo *tile, MODE_INFO **mi, |
| unsigned *no_pred_segcounts, |
| unsigned (*temporal_predictor_count)[2], |
| unsigned *t_unpred_seg_counts, int bw, int bh, |
| int mi_row, int mi_col) { |
| int segment_id; |
| |
| if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) return; |
| |
| xd->mi = mi; |
| segment_id = xd->mi[0]->mbmi.segment_id; |
| |
| set_mi_row_col(xd, tile, mi_row, bh, mi_col, bw, cm->mi_rows, cm->mi_cols); |
| |
| // Count the number of hits on each segment with no prediction |
| no_pred_segcounts[segment_id]++; |
| |
| // Temporal prediction not allowed on key frames |
| if (cm->frame_type != KEY_FRAME) { |
| const BLOCK_SIZE bsize = xd->mi[0]->mbmi.sb_type; |
| // Test to see if the segment id matches the predicted value. |
| const int pred_segment_id = |
| get_segment_id(cm, cm->last_frame_seg_map, bsize, mi_row, mi_col); |
| const int pred_flag = pred_segment_id == segment_id; |
| const int pred_context = vp10_get_pred_context_seg_id(xd); |
| |
| // Store the prediction status for this mb and update counts |
| // as appropriate |
| xd->mi[0]->mbmi.seg_id_predicted = pred_flag; |
| temporal_predictor_count[pred_context][pred_flag]++; |
| |
| // Update the "unpredicted" segment count |
| if (!pred_flag) t_unpred_seg_counts[segment_id]++; |
| } |
| } |
| |
| static void count_segs_sb(const VP10_COMMON *cm, MACROBLOCKD *xd, |
| const TileInfo *tile, MODE_INFO **mi, |
| unsigned *no_pred_segcounts, |
| unsigned (*temporal_predictor_count)[2], |
| unsigned *t_unpred_seg_counts, int mi_row, int mi_col, |
| BLOCK_SIZE bsize) { |
| const int mis = cm->mi_stride; |
| const int bs = num_8x8_blocks_wide_lookup[bsize], hbs = bs / 2; |
| #if CONFIG_EXT_PARTITION_TYPES |
| PARTITION_TYPE partition; |
| #else |
| int bw, bh; |
| #endif // CONFIG_EXT_PARTITION_TYPES |
| |
| if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) return; |
| |
| #if CONFIG_EXT_PARTITION_TYPES |
| if (bsize == BLOCK_8X8) |
| partition = PARTITION_NONE; |
| else |
| partition = get_partition(cm, mi_row, mi_col, bsize); |
| switch (partition) { |
| case PARTITION_NONE: |
| count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count, |
| t_unpred_seg_counts, bs, bs, mi_row, mi_col); |
| break; |
| case PARTITION_HORZ: |
| count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count, |
| t_unpred_seg_counts, bs, hbs, mi_row, mi_col); |
| count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts, |
| temporal_predictor_count, t_unpred_seg_counts, bs, hbs, |
| mi_row + hbs, mi_col); |
| break; |
| case PARTITION_VERT: |
| count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count, |
| t_unpred_seg_counts, hbs, bs, mi_row, mi_col); |
| count_segs(cm, xd, tile, mi + hbs, no_pred_segcounts, |
| temporal_predictor_count, t_unpred_seg_counts, hbs, bs, mi_row, |
| mi_col + hbs); |
| break; |
| case PARTITION_HORZ_A: |
| count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count, |
| t_unpred_seg_counts, hbs, hbs, mi_row, mi_col); |
| count_segs(cm, xd, tile, mi + hbs, no_pred_segcounts, |
| temporal_predictor_count, t_unpred_seg_counts, hbs, hbs, |
| mi_row, mi_col + hbs); |
| count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts, |
| temporal_predictor_count, t_unpred_seg_counts, bs, hbs, |
| mi_row + hbs, mi_col); |
| break; |
| case PARTITION_HORZ_B: |
| count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count, |
| t_unpred_seg_counts, bs, hbs, mi_row, mi_col); |
| count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts, |
| temporal_predictor_count, t_unpred_seg_counts, hbs, hbs, |
| mi_row + hbs, mi_col); |
| count_segs(cm, xd, tile, mi + hbs + hbs * mis, no_pred_segcounts, |
| temporal_predictor_count, t_unpred_seg_counts, hbs, hbs, |
| mi_row + hbs, mi_col + hbs); |
| break; |
| case PARTITION_VERT_A: |
| count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count, |
| t_unpred_seg_counts, hbs, hbs, mi_row, mi_col); |
| count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts, |
| temporal_predictor_count, t_unpred_seg_counts, hbs, hbs, |
| mi_row + hbs, mi_col); |
| count_segs(cm, xd, tile, mi + hbs, no_pred_segcounts, |
| temporal_predictor_count, t_unpred_seg_counts, hbs, bs, mi_row, |
| mi_col + hbs); |
| break; |
| case PARTITION_VERT_B: |
| count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count, |
| t_unpred_seg_counts, hbs, bs, mi_row, mi_col); |
| count_segs(cm, xd, tile, mi + hbs, no_pred_segcounts, |
| temporal_predictor_count, t_unpred_seg_counts, hbs, hbs, |
| mi_row, mi_col + hbs); |
| count_segs(cm, xd, tile, mi + hbs + hbs * mis, no_pred_segcounts, |
| temporal_predictor_count, t_unpred_seg_counts, hbs, hbs, |
| mi_row + hbs, mi_col + hbs); |
| break; |
| case PARTITION_SPLIT: { |
| const BLOCK_SIZE subsize = subsize_lookup[PARTITION_SPLIT][bsize]; |
| int n; |
| |
| assert(num_8x8_blocks_wide_lookup[mi[0]->mbmi.sb_type] < bs && |
| num_8x8_blocks_high_lookup[mi[0]->mbmi.sb_type] < bs); |
| |
| for (n = 0; n < 4; n++) { |
| const int mi_dc = hbs * (n & 1); |
| const int mi_dr = hbs * (n >> 1); |
| |
| count_segs_sb(cm, xd, tile, &mi[mi_dr * mis + mi_dc], no_pred_segcounts, |
| temporal_predictor_count, t_unpred_seg_counts, |
| mi_row + mi_dr, mi_col + mi_dc, subsize); |
| } |
| } break; |
| default: assert(0); |
| } |
| #else |
| bw = num_8x8_blocks_wide_lookup[mi[0]->mbmi.sb_type]; |
| bh = num_8x8_blocks_high_lookup[mi[0]->mbmi.sb_type]; |
| |
| if (bw == bs && bh == bs) { |
| count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count, |
| t_unpred_seg_counts, bs, bs, mi_row, mi_col); |
| } else if (bw == bs && bh < bs) { |
| count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count, |
| t_unpred_seg_counts, bs, hbs, mi_row, mi_col); |
| count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts, |
| temporal_predictor_count, t_unpred_seg_counts, bs, hbs, |
| mi_row + hbs, mi_col); |
| } else if (bw < bs && bh == bs) { |
| count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count, |
| t_unpred_seg_counts, hbs, bs, mi_row, mi_col); |
| count_segs(cm, xd, tile, mi + hbs, no_pred_segcounts, |
| temporal_predictor_count, t_unpred_seg_counts, hbs, bs, mi_row, |
| mi_col + hbs); |
| } else { |
| const BLOCK_SIZE subsize = subsize_lookup[PARTITION_SPLIT][bsize]; |
| int n; |
| |
| assert(bw < bs && bh < bs); |
| |
| for (n = 0; n < 4; n++) { |
| const int mi_dc = hbs * (n & 1); |
| const int mi_dr = hbs * (n >> 1); |
| |
| count_segs_sb(cm, xd, tile, &mi[mi_dr * mis + mi_dc], no_pred_segcounts, |
| temporal_predictor_count, t_unpred_seg_counts, |
| mi_row + mi_dr, mi_col + mi_dc, subsize); |
| } |
| } |
| #endif // CONFIG_EXT_PARTITION_TYPES |
| } |
| |
| void vp10_choose_segmap_coding_method(VP10_COMMON *cm, MACROBLOCKD *xd) { |
| struct segmentation *seg = &cm->seg; |
| struct segmentation_probs *segp = &cm->fc->seg; |
| |
| int no_pred_cost; |
| int t_pred_cost = INT_MAX; |
| |
| int i, tile_col, tile_row, mi_row, mi_col; |
| |
| unsigned(*temporal_predictor_count)[2] = cm->counts.seg.pred; |
| unsigned *no_pred_segcounts = cm->counts.seg.tree_total; |
| unsigned *t_unpred_seg_counts = cm->counts.seg.tree_mispred; |
| |
| vpx_prob no_pred_tree[SEG_TREE_PROBS]; |
| vpx_prob t_pred_tree[SEG_TREE_PROBS]; |
| vpx_prob t_nopred_prob[PREDICTION_PROBS]; |
| |
| (void)xd; |
| |
| // We are about to recompute all the segment counts, so zero the accumulators. |
| vp10_zero(cm->counts.seg); |
| |
| // First of all generate stats regarding how well the last segment map |
| // predicts this one |
| for (tile_row = 0; tile_row < cm->tile_rows; tile_row++) { |
| TileInfo tile_info; |
| vp10_tile_set_row(&tile_info, cm, tile_row); |
| for (tile_col = 0; tile_col < cm->tile_cols; tile_col++) { |
| MODE_INFO **mi_ptr; |
| vp10_tile_set_col(&tile_info, cm, tile_col); |
| mi_ptr = cm->mi_grid_visible + tile_info.mi_row_start * cm->mi_stride + |
| tile_info.mi_col_start; |
| for (mi_row = tile_info.mi_row_start; mi_row < tile_info.mi_row_end; |
| mi_row += cm->mib_size, mi_ptr += cm->mib_size * cm->mi_stride) { |
| MODE_INFO **mi = mi_ptr; |
| for (mi_col = tile_info.mi_col_start; mi_col < tile_info.mi_col_end; |
| mi_col += cm->mib_size, mi += cm->mib_size) { |
| count_segs_sb(cm, xd, &tile_info, mi, no_pred_segcounts, |
| temporal_predictor_count, t_unpred_seg_counts, mi_row, |
| mi_col, cm->sb_size); |
| } |
| } |
| } |
| } |
| |
| // Work out probability tree for coding segments without prediction |
| // and the cost. |
| calc_segtree_probs(no_pred_segcounts, no_pred_tree, segp->tree_probs); |
| no_pred_cost = cost_segmap(no_pred_segcounts, no_pred_tree); |
| |
| // Key frames cannot use temporal prediction |
| if (!frame_is_intra_only(cm) && !cm->error_resilient_mode) { |
| // Work out probability tree for coding those segments not |
| // predicted using the temporal method and the cost. |
| calc_segtree_probs(t_unpred_seg_counts, t_pred_tree, segp->tree_probs); |
| t_pred_cost = cost_segmap(t_unpred_seg_counts, t_pred_tree); |
| |
| // Add in the cost of the signaling for each prediction context. |
| for (i = 0; i < PREDICTION_PROBS; i++) { |
| const int count0 = temporal_predictor_count[i][0]; |
| const int count1 = temporal_predictor_count[i][1]; |
| |
| t_nopred_prob[i] = get_binary_prob(count0, count1); |
| vp10_prob_diff_update_savings_search(temporal_predictor_count[i], |
| segp->pred_probs[i], |
| &t_nopred_prob[i], DIFF_UPDATE_PROB); |
| |
| // Add in the predictor signaling cost |
| t_pred_cost += count0 * vp10_cost_zero(t_nopred_prob[i]) + |
| count1 * vp10_cost_one(t_nopred_prob[i]); |
| } |
| } |
| |
| // Now choose which coding method to use. |
| if (t_pred_cost < no_pred_cost) { |
| assert(!cm->error_resilient_mode); |
| seg->temporal_update = 1; |
| } else { |
| seg->temporal_update = 0; |
| } |
| } |
| |
| void vp10_reset_segment_features(VP10_COMMON *cm) { |
| struct segmentation *seg = &cm->seg; |
| |
| // Set up default state for MB feature flags |
| seg->enabled = 0; |
| seg->update_map = 0; |
| seg->update_data = 0; |
| vp10_clearall_segfeatures(seg); |
| } |
