ref: 69a09a70e920f6934dbd00c9994ee73d81eed809
dir: /vp9/encoder/vp9_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 "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;
vpx_memcpy(seg->feature_data, feature_data, sizeof(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));
}
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].src_mi->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].src_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
xd->mi[0].src_mi->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].src_mi->mbmi.sb_type];
bh = num_8x8_blocks_high_lookup[mi[0].src_mi->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
vpx_memset(seg->tree_probs, 255, sizeof(seg->tree_probs));
vpx_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 + 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;
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));
}
}
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;
vpx_memset(seg->tree_probs, 255, sizeof(seg->tree_probs));
vp9_clearall_segfeatures(seg);
}