/* * 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 #include "vpx_mem/vpx_mem.h" #include "vp9/encoder/vp9_segmentation.h" #include "vp9/common/vp9_pred_common.h" #include "vp9/common/vp9_tile_common.h" void vp9_enable_segmentation(VP9_PTR ptr) { VP9_COMP *cpi = (VP9_COMP *)ptr; cpi->mb.e_mbd.seg.enabled = 1; cpi->mb.e_mbd.seg.update_map = 1; cpi->mb.e_mbd.seg.update_data = 1; } void vp9_disable_segmentation(VP9_PTR ptr) { VP9_COMP *cpi = (VP9_COMP *)ptr; cpi->mb.e_mbd.seg.enabled = 0; } void vp9_set_segmentation_map(VP9_PTR ptr, unsigned char *segmentation_map) { VP9_COMP *cpi = (VP9_COMP *)(ptr); // Copy in the new segmentation map vpx_memcpy(cpi->segmentation_map, segmentation_map, (cpi->common.mi_rows * cpi->common.mi_cols)); // Signal that the map should be updated. cpi->mb.e_mbd.seg.update_map = 1; cpi->mb.e_mbd.seg.update_data = 1; } void vp9_set_segment_data(VP9_PTR ptr, signed char *feature_data, unsigned char abs_delta) { VP9_COMP *cpi = (VP9_COMP *)(ptr); cpi->mb.e_mbd.seg.abs_delta = abs_delta; vpx_memcpy(cpi->mb.e_mbd.seg.feature_data, feature_data, sizeof(cpi->mb.e_mbd.seg.feature_data)); // TBD ?? Set the feature mask // vpx_memcpy(cpi->mb.e_mbd.segment_feature_mask, 0, // sizeof(cpi->mb.e_mbd.segment_feature_mask)); } // Based on set of segment counts calculate a probability tree static void calc_segtree_probs(int *segcounts, vp9_prob *segment_tree_probs) { // Work out probabilities of each segment 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]; segment_tree_probs[0] = get_binary_prob(c01 + c23, c45 + c67); segment_tree_probs[1] = get_binary_prob(c01, c23); segment_tree_probs[2] = get_binary_prob(c45, c67); 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]); } // Based on set of segment counts and probabilities calculate a cost estimate static int cost_segmap(int *segcounts, vp9_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 * vp9_cost_zero(probs[0]) + c4567 * vp9_cost_one(probs[0]); // Cost subsequent levels if (c0123 > 0) { cost += c01 * vp9_cost_zero(probs[1]) + c23 * vp9_cost_one(probs[1]); if (c01 > 0) cost += segcounts[0] * vp9_cost_zero(probs[3]) + segcounts[1] * vp9_cost_one(probs[3]); if (c23 > 0) cost += segcounts[2] * vp9_cost_zero(probs[4]) + segcounts[3] * vp9_cost_one(probs[4]); } if (c4567 > 0) { cost += c45 * vp9_cost_zero(probs[2]) + c67 * vp9_cost_one(probs[2]); if (c45 > 0) cost += segcounts[4] * vp9_cost_zero(probs[5]) + segcounts[5] * vp9_cost_one(probs[5]); if (c67 > 0) cost += segcounts[6] * vp9_cost_zero(probs[6]) + segcounts[7] * vp9_cost_one(probs[6]); } return cost; } static void count_segs(VP9_COMP *cpi, MODE_INFO *mi, int *no_pred_segcounts, int (*temporal_predictor_count)[2], int *t_unpred_seg_counts, int bw, int bh, int mi_row, int mi_col) { VP9_COMMON *const cm = &cpi->common; MACROBLOCKD *const xd = &cpi->mb.e_mbd; int segment_id; if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) return; segment_id = mi->mbmi.segment_id; xd->mode_info_context = mi; set_mi_row_col(cm, xd, mi_row, bh, mi_col, bw); // 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_TYPE bsize = mi->mbmi.sb_type; // Test to see if the segment id matches the predicted value. const int pred_segment_id = vp9_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 = vp9_get_pred_context_seg_id(xd); // Store the prediction status for this mb and update counts // as appropriate vp9_set_pred_flag_seg_id(cm, bsize, mi_row, mi_col, pred_flag); temporal_predictor_count[pred_context][pred_flag]++; if (!pred_flag) // Update the "unpredicted" segment count t_unpred_seg_counts[segment_id]++; } } static void count_segs_sb(VP9_COMP *cpi, MODE_INFO *mi, int *no_pred_segcounts, int (*temporal_predictor_count)[2], int *t_unpred_seg_counts, int mi_row, int mi_col, BLOCK_SIZE_TYPE bsize) { VP9_COMMON *const cm = &cpi->common; const int mis = cm->mode_info_stride; int bwl, bhl; const int bsl = mi_width_log2(bsize), bs = 1 << (bsl - 1); if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols) return; bwl = mi_width_log2(mi->mbmi.sb_type); bhl = mi_height_log2(mi->mbmi.sb_type); if (bwl == bsl && bhl == bsl) { count_segs(cpi, mi, no_pred_segcounts, temporal_predictor_count, t_unpred_seg_counts, 1 << bsl, 1 << bsl, mi_row, mi_col); } else if (bwl == bsl && bhl < bsl) { count_segs(cpi, mi, no_pred_segcounts, temporal_predictor_count, t_unpred_seg_counts, 1 << bsl, bs, mi_row, mi_col); count_segs(cpi, mi + bs * mis, no_pred_segcounts, temporal_predictor_count, t_unpred_seg_counts, 1 << bsl, bs, mi_row + bs, mi_col); } else if (bwl < bsl && bhl == bsl) { count_segs(cpi, mi, no_pred_segcounts, temporal_predictor_count, t_unpred_seg_counts, bs, 1 << bsl, mi_row, mi_col); count_segs(cpi, mi + bs, no_pred_segcounts, temporal_predictor_count, t_unpred_seg_counts, bs, 1 << bsl, mi_row, mi_col + bs); } else { BLOCK_SIZE_TYPE subsize; int n; assert(bwl < bsl && bhl < bsl); if (bsize == BLOCK_64X64) { subsize = BLOCK_32X32; } else if (bsize == BLOCK_32X32) { subsize = BLOCK_16X16; } else { assert(bsize == BLOCK_16X16); subsize = BLOCK_8X8; } for (n = 0; n < 4; n++) { const int y_idx = n >> 1, x_idx = n & 0x01; count_segs_sb(cpi, mi + y_idx * bs * mis + x_idx * bs, no_pred_segcounts, temporal_predictor_count, t_unpred_seg_counts, mi_row + y_idx * bs, mi_col + x_idx * bs, subsize); } } } void vp9_choose_segmap_coding_method(VP9_COMP *cpi) { VP9_COMMON *const cm = &cpi->common; struct segmentation *seg = &cpi->mb.e_mbd.seg; int no_pred_cost; int t_pred_cost = INT_MAX; int i, tile_col, mi_row, mi_col; int temporal_predictor_count[PREDICTION_PROBS][2]; int no_pred_segcounts[MAX_SEGMENTS]; int t_unpred_seg_counts[MAX_SEGMENTS]; vp9_prob no_pred_tree[SEG_TREE_PROBS]; vp9_prob t_pred_tree[SEG_TREE_PROBS]; vp9_prob t_nopred_prob[PREDICTION_PROBS]; const int mis = cm->mode_info_stride; MODE_INFO *mi_ptr, *mi; // Set default state for the segment tree probabilities and the // temporal coding probabilities vpx_memset(seg->tree_probs, 255, sizeof(seg->tree_probs)); vpx_memset(seg->pred_probs, 255, sizeof(seg->pred_probs)); vpx_memset(no_pred_segcounts, 0, sizeof(no_pred_segcounts)); vpx_memset(t_unpred_seg_counts, 0, sizeof(t_unpred_seg_counts)); vpx_memset(temporal_predictor_count, 0, sizeof(temporal_predictor_count)); // 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++) { vp9_get_tile_col_offsets(cm, tile_col); mi_ptr = cm->mi + cm->cur_tile_mi_col_start; for (mi_row = 0; mi_row < cm->mi_rows; mi_row += 8, mi_ptr += 8 * mis) { mi = mi_ptr; for (mi_col = cm->cur_tile_mi_col_start; mi_col < cm->cur_tile_mi_col_end; mi_col += 8, mi += 8) count_segs_sb(cpi, 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); no_pred_cost = cost_segmap(no_pred_segcounts, no_pred_tree); // Key frames cannot use temporal prediction if (cm->frame_type != KEY_FRAME) { // 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); t_pred_cost = cost_segmap(t_unpred_seg_counts, t_pred_tree); // Add in the cost of the signalling 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); // Add in the predictor signaling cost t_pred_cost += count0 * vp9_cost_zero(t_nopred_prob[i]) + count1 * vp9_cost_one(t_nopred_prob[i]); } } // Now choose which coding method to use. if (t_pred_cost < no_pred_cost) { seg->temporal_update = 1; vpx_memcpy(seg->tree_probs, t_pred_tree, sizeof(t_pred_tree)); vpx_memcpy(seg->pred_probs, t_nopred_prob, sizeof(t_nopred_prob)); } else { seg->temporal_update = 0; vpx_memcpy(seg->tree_probs, no_pred_tree, sizeof(no_pred_tree)); } }