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-rw-r--r--media/libvpx/vp9/encoder/vp9_segmentation.c281
1 files changed, 281 insertions, 0 deletions
diff --git a/media/libvpx/vp9/encoder/vp9_segmentation.c b/media/libvpx/vp9/encoder/vp9_segmentation.c
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+/*
+ * 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 "vp9/common/vp9_pred_common.h"
+#include "vp9/common/vp9_tile_common.h"
+
+#include "vp9/encoder/vp9_cost.h"
+#include "vp9/encoder/vp9_segmentation.h"
+
+void vp9_enable_segmentation(struct segmentation *seg) {
+ seg->enabled = 1;
+ seg->update_map = 1;
+ seg->update_data = 1;
+}
+
+void vp9_disable_segmentation(struct segmentation *seg) {
+ seg->enabled = 0;
+ seg->update_map = 0;
+ seg->update_data = 0;
+}
+
+void vp9_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 vp9_disable_segfeature(struct segmentation *seg, int segment_id,
+ SEG_LVL_FEATURES feature_id) {
+ seg->feature_mask[segment_id] &= ~(1 << feature_id);
+}
+
+void vp9_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(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(const VP9_COMMON *cm, MACROBLOCKD *xd,
+ const TileInfo *tile, 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) {
+ 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 = 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
+ 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 VP9_COMMON *cm, MACROBLOCKD *xd,
+ const TileInfo *tile, 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 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 vp9_choose_segmap_coding_method(VP9_COMMON *cm, MACROBLOCKD *xd) {
+ struct segmentation *seg = &cm->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] = { { 0 } };
+ int no_pred_segcounts[MAX_SEGMENTS] = { 0 };
+ int t_unpred_seg_counts[MAX_SEGMENTS] = { 0 };
+
+ vp9_prob no_pred_tree[SEG_TREE_PROBS];
+ vp9_prob t_pred_tree[SEG_TREE_PROBS];
+ vp9_prob t_nopred_prob[PREDICTION_PROBS];
+
+ // Set default state for the segment tree probabilities and the
+ // temporal coding probabilities
+ memset(seg->tree_probs, 255, sizeof(seg->tree_probs));
+ memset(seg->pred_probs, 255, sizeof(seg->pred_probs));
+
+ // 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;
+ vp9_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);
+ 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);
+ 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);
+
+ // 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;
+ memcpy(seg->tree_probs, t_pred_tree, sizeof(t_pred_tree));
+ memcpy(seg->pred_probs, t_nopred_prob, sizeof(t_nopred_prob));
+ } else {
+ seg->temporal_update = 0;
+ memcpy(seg->tree_probs, no_pred_tree, sizeof(no_pred_tree));
+ }
+}
+
+void vp9_reset_segment_features(struct segmentation *seg) {
+ // Set up default state for MB feature flags
+ seg->enabled = 0;
+ seg->update_map = 0;
+ seg->update_data = 0;
+ memset(seg->tree_probs, 255, sizeof(seg->tree_probs));
+ vp9_clearall_segfeatures(seg);
+}