291 строка
10 KiB
C
291 строка
10 KiB
C
/*
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* Copyright (c) 2012 The WebM project authors. All Rights Reserved.
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*
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* Use of this source code is governed by a BSD-style license
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* that can be found in the LICENSE file in the root of the source
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* tree. An additional intellectual property rights grant can be found
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* in the file PATENTS. All contributing project authors may
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* be found in the AUTHORS file in the root of the source tree.
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*/
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#include <limits.h>
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#include "vpx_mem/vpx_mem.h"
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#include "vp10/common/pred_common.h"
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#include "vp10/common/tile_common.h"
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#include "vp10/encoder/cost.h"
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#include "vp10/encoder/segmentation.h"
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#include "vp10/encoder/subexp.h"
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void vp10_enable_segmentation(struct segmentation *seg) {
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seg->enabled = 1;
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seg->update_map = 1;
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seg->update_data = 1;
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}
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void vp10_disable_segmentation(struct segmentation *seg) {
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seg->enabled = 0;
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seg->update_map = 0;
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seg->update_data = 0;
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}
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void vp10_set_segment_data(struct segmentation *seg,
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signed char *feature_data,
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unsigned char abs_delta) {
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seg->abs_delta = abs_delta;
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memcpy(seg->feature_data, feature_data, sizeof(seg->feature_data));
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}
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void vp10_disable_segfeature(struct segmentation *seg, int segment_id,
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SEG_LVL_FEATURES feature_id) {
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seg->feature_mask[segment_id] &= ~(1 << feature_id);
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}
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void vp10_clear_segdata(struct segmentation *seg, int segment_id,
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SEG_LVL_FEATURES feature_id) {
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seg->feature_data[segment_id][feature_id] = 0;
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}
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// Based on set of segment counts calculate a probability tree
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static void calc_segtree_probs(unsigned *segcounts,
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vpx_prob *segment_tree_probs, const vpx_prob *cur_tree_probs) {
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// Work out probabilities of each segment
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const unsigned cc[4] = {
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segcounts[0] + segcounts[1], segcounts[2] + segcounts[3],
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segcounts[4] + segcounts[5], segcounts[6] + segcounts[7]
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};
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const unsigned ccc[2] = { cc[0] + cc[1], cc[2] + cc[3] };
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int i;
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segment_tree_probs[0] = get_binary_prob(ccc[0], ccc[1]);
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segment_tree_probs[1] = get_binary_prob(cc[0], cc[1]);
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segment_tree_probs[2] = get_binary_prob(cc[2], cc[3]);
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segment_tree_probs[3] = get_binary_prob(segcounts[0], segcounts[1]);
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segment_tree_probs[4] = get_binary_prob(segcounts[2], segcounts[3]);
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segment_tree_probs[5] = get_binary_prob(segcounts[4], segcounts[5]);
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segment_tree_probs[6] = get_binary_prob(segcounts[6], segcounts[7]);
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for (i = 0; i < 7; i++) {
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const unsigned *ct = i == 0 ? ccc : i < 3 ? cc + (i & 2)
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: segcounts + (i - 3) * 2;
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vp10_prob_diff_update_savings_search(ct,
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cur_tree_probs[i], &segment_tree_probs[i], DIFF_UPDATE_PROB);
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}
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}
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// Based on set of segment counts and probabilities calculate a cost estimate
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static int cost_segmap(unsigned *segcounts, vpx_prob *probs) {
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const int c01 = segcounts[0] + segcounts[1];
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const int c23 = segcounts[2] + segcounts[3];
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const int c45 = segcounts[4] + segcounts[5];
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const int c67 = segcounts[6] + segcounts[7];
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const int c0123 = c01 + c23;
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const int c4567 = c45 + c67;
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// Cost the top node of the tree
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int cost = c0123 * vp10_cost_zero(probs[0]) +
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c4567 * vp10_cost_one(probs[0]);
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// Cost subsequent levels
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if (c0123 > 0) {
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cost += c01 * vp10_cost_zero(probs[1]) +
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c23 * vp10_cost_one(probs[1]);
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if (c01 > 0)
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cost += segcounts[0] * vp10_cost_zero(probs[3]) +
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segcounts[1] * vp10_cost_one(probs[3]);
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if (c23 > 0)
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cost += segcounts[2] * vp10_cost_zero(probs[4]) +
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segcounts[3] * vp10_cost_one(probs[4]);
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}
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if (c4567 > 0) {
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cost += c45 * vp10_cost_zero(probs[2]) +
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c67 * vp10_cost_one(probs[2]);
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if (c45 > 0)
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cost += segcounts[4] * vp10_cost_zero(probs[5]) +
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segcounts[5] * vp10_cost_one(probs[5]);
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if (c67 > 0)
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cost += segcounts[6] * vp10_cost_zero(probs[6]) +
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segcounts[7] * vp10_cost_one(probs[6]);
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}
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return cost;
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}
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static void count_segs(const VP10_COMMON *cm, MACROBLOCKD *xd,
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const TileInfo *tile, MODE_INFO **mi,
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unsigned *no_pred_segcounts,
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unsigned (*temporal_predictor_count)[2],
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unsigned *t_unpred_seg_counts,
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int bw, int bh, int mi_row, int mi_col) {
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int segment_id;
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if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols)
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return;
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xd->mi = mi;
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segment_id = xd->mi[0]->mbmi.segment_id;
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set_mi_row_col(xd, tile, mi_row, bh, mi_col, bw, cm->mi_rows, cm->mi_cols);
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// Count the number of hits on each segment with no prediction
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no_pred_segcounts[segment_id]++;
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// Temporal prediction not allowed on key frames
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if (cm->frame_type != KEY_FRAME) {
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const BLOCK_SIZE bsize = xd->mi[0]->mbmi.sb_type;
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// Test to see if the segment id matches the predicted value.
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const int pred_segment_id = get_segment_id(cm, cm->last_frame_seg_map,
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bsize, mi_row, mi_col);
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const int pred_flag = pred_segment_id == segment_id;
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const int pred_context = vp10_get_pred_context_seg_id(xd);
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// Store the prediction status for this mb and update counts
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// as appropriate
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xd->mi[0]->mbmi.seg_id_predicted = pred_flag;
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temporal_predictor_count[pred_context][pred_flag]++;
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// Update the "unpredicted" segment count
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if (!pred_flag)
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t_unpred_seg_counts[segment_id]++;
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}
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}
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static void count_segs_sb(const VP10_COMMON *cm, MACROBLOCKD *xd,
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const TileInfo *tile, MODE_INFO **mi,
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unsigned *no_pred_segcounts,
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unsigned (*temporal_predictor_count)[2],
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unsigned *t_unpred_seg_counts,
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int mi_row, int mi_col,
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BLOCK_SIZE bsize) {
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const int mis = cm->mi_stride;
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int bw, bh;
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const int bs = num_8x8_blocks_wide_lookup[bsize], hbs = bs / 2;
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if (mi_row >= cm->mi_rows || mi_col >= cm->mi_cols)
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return;
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bw = num_8x8_blocks_wide_lookup[mi[0]->mbmi.sb_type];
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bh = num_8x8_blocks_high_lookup[mi[0]->mbmi.sb_type];
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if (bw == bs && bh == bs) {
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count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
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t_unpred_seg_counts, bs, bs, mi_row, mi_col);
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} else if (bw == bs && bh < bs) {
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count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
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t_unpred_seg_counts, bs, hbs, mi_row, mi_col);
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count_segs(cm, xd, tile, mi + hbs * mis, no_pred_segcounts,
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temporal_predictor_count, t_unpred_seg_counts, bs, hbs,
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mi_row + hbs, mi_col);
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} else if (bw < bs && bh == bs) {
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count_segs(cm, xd, tile, mi, no_pred_segcounts, temporal_predictor_count,
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t_unpred_seg_counts, hbs, bs, mi_row, mi_col);
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count_segs(cm, xd, tile, mi + hbs,
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no_pred_segcounts, temporal_predictor_count, t_unpred_seg_counts,
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hbs, bs, mi_row, mi_col + hbs);
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} else {
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const BLOCK_SIZE subsize = subsize_lookup[PARTITION_SPLIT][bsize];
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int n;
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assert(bw < bs && bh < bs);
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for (n = 0; n < 4; n++) {
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const int mi_dc = hbs * (n & 1);
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const int mi_dr = hbs * (n >> 1);
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count_segs_sb(cm, xd, tile, &mi[mi_dr * mis + mi_dc],
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no_pred_segcounts, temporal_predictor_count,
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t_unpred_seg_counts,
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mi_row + mi_dr, mi_col + mi_dc, subsize);
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}
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}
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}
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void vp10_choose_segmap_coding_method(VP10_COMMON *cm, MACROBLOCKD *xd) {
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struct segmentation *seg = &cm->seg;
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struct segmentation_probs *segp = &cm->fc->seg;
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int no_pred_cost;
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int t_pred_cost = INT_MAX;
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int i, tile_col, mi_row, mi_col;
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unsigned (*temporal_predictor_count)[2] = cm->counts.seg.pred;
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unsigned *no_pred_segcounts = cm->counts.seg.tree_total;
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unsigned *t_unpred_seg_counts = cm->counts.seg.tree_mispred;
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vpx_prob no_pred_tree[SEG_TREE_PROBS];
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vpx_prob t_pred_tree[SEG_TREE_PROBS];
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vpx_prob t_nopred_prob[PREDICTION_PROBS];
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(void) xd;
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// First of all generate stats regarding how well the last segment map
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// predicts this one
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for (tile_col = 0; tile_col < 1 << cm->log2_tile_cols; tile_col++) {
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TileInfo tile;
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MODE_INFO **mi_ptr;
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vp10_tile_init(&tile, cm, 0, tile_col);
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mi_ptr = cm->mi_grid_visible + tile.mi_col_start;
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for (mi_row = 0; mi_row < cm->mi_rows;
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mi_row += 8, mi_ptr += 8 * cm->mi_stride) {
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MODE_INFO **mi = mi_ptr;
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for (mi_col = tile.mi_col_start; mi_col < tile.mi_col_end;
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mi_col += 8, mi += 8)
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count_segs_sb(cm, xd, &tile, mi, no_pred_segcounts,
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temporal_predictor_count, t_unpred_seg_counts,
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mi_row, mi_col, BLOCK_64X64);
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}
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}
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// Work out probability tree for coding segments without prediction
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// and the cost.
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calc_segtree_probs(no_pred_segcounts, no_pred_tree, segp->tree_probs);
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no_pred_cost = cost_segmap(no_pred_segcounts, no_pred_tree);
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// Key frames cannot use temporal prediction
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if (!frame_is_intra_only(cm)) {
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// Work out probability tree for coding those segments not
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// predicted using the temporal method and the cost.
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calc_segtree_probs(t_unpred_seg_counts, t_pred_tree, segp->tree_probs);
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t_pred_cost = cost_segmap(t_unpred_seg_counts, t_pred_tree);
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// Add in the cost of the signaling for each prediction context.
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for (i = 0; i < PREDICTION_PROBS; i++) {
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const int count0 = temporal_predictor_count[i][0];
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const int count1 = temporal_predictor_count[i][1];
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vp10_prob_diff_update_savings_search(temporal_predictor_count[i],
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segp->pred_probs[i],
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&t_nopred_prob[i], DIFF_UPDATE_PROB);
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// Add in the predictor signaling cost
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t_pred_cost += count0 * vp10_cost_zero(t_nopred_prob[i]) +
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count1 * vp10_cost_one(t_nopred_prob[i]);
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}
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}
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// Now choose which coding method to use.
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if (t_pred_cost < no_pred_cost) {
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seg->temporal_update = 1;
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} else {
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seg->temporal_update = 0;
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}
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}
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void vp10_reset_segment_features(VP10_COMMON *cm) {
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struct segmentation *seg = &cm->seg;
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// Set up default state for MB feature flags
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seg->enabled = 0;
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seg->update_map = 0;
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seg->update_data = 0;
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vp10_clearall_segfeatures(seg);
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}
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