diff options
Diffstat (limited to 'third_party/aom/av1/encoder/segmentation.c')
| -rw-r--r-- | third_party/aom/av1/encoder/segmentation.c | 394 |
1 files changed, 394 insertions, 0 deletions
diff --git a/third_party/aom/av1/encoder/segmentation.c b/third_party/aom/av1/encoder/segmentation.c new file mode 100644 index 000000000..b581a61d0 --- /dev/null +++ b/third_party/aom/av1/encoder/segmentation.c @@ -0,0 +1,394 @@ +/* + * Copyright (c) 2016, 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 <limits.h> + +#include "aom_mem/aom_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 av1_enable_segmentation(struct segmentation *seg) { + seg->enabled = 1; + seg->update_map = 1; + seg->update_data = 1; +} + +void av1_disable_segmentation(struct segmentation *seg) { + seg->enabled = 0; + seg->update_map = 0; + seg->update_data = 0; +} + +void av1_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 av1_disable_segfeature(struct segmentation *seg, int segment_id, + SEG_LVL_FEATURES feature_id) { + seg->feature_mask[segment_id] &= ~(1 << feature_id); +} + +void av1_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, + aom_prob *segment_tree_probs, + const aom_prob *cur_tree_probs, + const int probwt) { + // 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; + av1_prob_diff_update_savings_search(ct, cur_tree_probs[i], + &segment_tree_probs[i], + DIFF_UPDATE_PROB, probwt); + } +} + +// Based on set of segment counts and probabilities calculate a cost estimate +static int cost_segmap(unsigned *segcounts, aom_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 * av1_cost_zero(probs[0]) + c4567 * av1_cost_one(probs[0]); + + // Cost subsequent levels + if (c0123 > 0) { + cost += c01 * av1_cost_zero(probs[1]) + c23 * av1_cost_one(probs[1]); + + if (c01 > 0) + cost += segcounts[0] * av1_cost_zero(probs[3]) + + segcounts[1] * av1_cost_one(probs[3]); + if (c23 > 0) + cost += segcounts[2] * av1_cost_zero(probs[4]) + + segcounts[3] * av1_cost_one(probs[4]); + } + + if (c4567 > 0) { + cost += c45 * av1_cost_zero(probs[2]) + c67 * av1_cost_one(probs[2]); + + if (c45 > 0) + cost += segcounts[4] * av1_cost_zero(probs[5]) + + segcounts[5] * av1_cost_one(probs[5]); + if (c67 > 0) + cost += segcounts[6] * av1_cost_zero(probs[6]) + + segcounts[7] * av1_cost_one(probs[6]); + } + + return cost; +} + +static void count_segs(const AV1_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, +#if CONFIG_DEPENDENT_HORZTILES + cm->dependent_horz_tiles, +#endif // CONFIG_DEPENDENT_HORZTILES + 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 = av1_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 AV1_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 = mi_size_wide[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 = mi_size_wide[mi[0]->mbmi.sb_type]; + bh = mi_size_high[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 av1_choose_segmap_coding_method(AV1_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; +#if CONFIG_TILE_GROUPS + const int probwt = cm->num_tg; +#else + const int probwt = 1; +#endif + + 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; + + aom_prob no_pred_tree[SEG_TREE_PROBS]; + aom_prob t_pred_tree[SEG_TREE_PROBS]; + aom_prob t_nopred_prob[PREDICTION_PROBS]; + + (void)xd; + + // We are about to recompute all the segment counts, so zero the accumulators. + av1_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; + av1_tile_set_row(&tile_info, cm, tile_row); + for (tile_col = 0; tile_col < cm->tile_cols; tile_col++) { + MODE_INFO **mi_ptr; + av1_tile_set_col(&tile_info, cm, tile_col); +#if CONFIG_TILE_GROUPS && CONFIG_DEPENDENT_HORZTILES + av1_tile_set_tg_boundary(&tile_info, cm, tile_row, tile_col); +#endif + 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, probwt); + 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, + probwt); + 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); + av1_prob_diff_update_savings_search( + temporal_predictor_count[i], segp->pred_probs[i], &t_nopred_prob[i], + DIFF_UPDATE_PROB, probwt); + + // Add in the predictor signaling cost + t_pred_cost += count0 * av1_cost_zero(t_nopred_prob[i]) + + count1 * av1_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 av1_reset_segment_features(AV1_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; + av1_clearall_segfeatures(seg); +} |