ref: d8aa40634a522a10635c5982cfd52bbfba15591b
dir: /vp10/encoder/segmentation.c/
/* * 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 "vpx_mem/vpx_mem.h" #include "vp10/common/pred_common.h" #include "vp10/common/tile_common.h" #include "vp10/encoder/cost.h" #include "vp10/encoder/segmentation.h" #include "vp10/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] }; #if CONFIG_MISC_FIXES int i; #endif 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]); #if CONFIG_MISC_FIXES 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); } #else (void) cur_tree_probs; #endif } // 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; int bw, bh; const int bs = num_8x8_blocks_wide_lookup[bsize], hbs = bs / 2; if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) return; 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); } } } void vp10_choose_segmap_coding_method(VP10_COMMON *cm, MACROBLOCKD *xd) { struct segmentation *seg = &cm->seg; #if CONFIG_MISC_FIXES struct segmentation_probs *segp = &cm->fc->seg; #else struct segmentation_probs *segp = &cm->segp; #endif int no_pred_cost; int t_pred_cost = INT_MAX; int i, tile_col, mi_row, mi_col; #if CONFIG_MISC_FIXES 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; #else unsigned temporal_predictor_count[PREDICTION_PROBS][2] = { { 0 } }; unsigned no_pred_segcounts[MAX_SEGMENTS] = { 0 }; unsigned t_unpred_seg_counts[MAX_SEGMENTS] = { 0 }; #endif vpx_prob no_pred_tree[SEG_TREE_PROBS]; vpx_prob t_pred_tree[SEG_TREE_PROBS]; vpx_prob t_nopred_prob[PREDICTION_PROBS]; #if CONFIG_MISC_FIXES (void) xd; #else // Set default state for the segment tree probabilities and the // temporal coding probabilities memset(segp->tree_probs, 255, sizeof(segp->tree_probs)); memset(segp->pred_probs, 255, sizeof(segp->pred_probs)); #endif // First of all generate stats regarding how well the last segment map // predicts this one for (tile_col = 0; tile_col < 1 << cm->log2_tile_cols; tile_col++) { TileInfo tile; MODE_INFO **mi_ptr; vp10_tile_init(&tile, cm, 0, tile_col); mi_ptr = cm->mi_grid_visible + tile.mi_col_start; for (mi_row = 0; mi_row < cm->mi_rows; mi_row += 8, mi_ptr += 8 * cm->mi_stride) { MODE_INFO **mi = mi_ptr; for (mi_col = tile.mi_col_start; mi_col < tile.mi_col_end; mi_col += 8, mi += 8) count_segs_sb(cm, xd, &tile, mi, no_pred_segcounts, temporal_predictor_count, t_unpred_seg_counts, mi_row, mi_col, BLOCK_64X64); } } // 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)) { // 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]; #if CONFIG_MISC_FIXES vp10_prob_diff_update_savings_search(temporal_predictor_count[i], segp->pred_probs[i], &t_nopred_prob[i], DIFF_UPDATE_PROB); #else t_nopred_prob[i] = get_binary_prob(count0, count1); #endif // 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) { seg->temporal_update = 1; #if !CONFIG_MISC_FIXES memcpy(segp->tree_probs, t_pred_tree, sizeof(t_pred_tree)); memcpy(segp->pred_probs, t_nopred_prob, sizeof(t_nopred_prob)); #endif } else { seg->temporal_update = 0; #if !CONFIG_MISC_FIXES memcpy(segp->tree_probs, no_pred_tree, sizeof(no_pred_tree)); #endif } } void vp10_reset_segment_features(VP10_COMMON *cm) { struct segmentation *seg = &cm->seg; #if !CONFIG_MISC_FIXES struct segmentation_probs *segp = &cm->segp; #endif // Set up default state for MB feature flags seg->enabled = 0; seg->update_map = 0; seg->update_data = 0; #if !CONFIG_MISC_FIXES memset(segp->tree_probs, 255, sizeof(segp->tree_probs)); #endif vp10_clearall_segfeatures(seg); }