vp9/encoder/vp9_segmentation.c - aom - Git at Google

 /*
  *  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/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.segmentation_enabled = 1;
   cpi->mb.e_mbd.update_mb_segmentation_map = 1;
   cpi->mb.e_mbd.update_mb_segmentation_data = 1;
 }

 void vp9_disable_segmentation(VP9_PTR ptr) {
   VP9_COMP *cpi = (VP9_COMP *)ptr;
   cpi->mb.e_mbd.segmentation_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.update_mb_segmentation_map = 1;
   cpi->mb.e_mbd.update_mb_segmentation_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.mb_segment_abs_delta = abs_delta;

   vpx_memcpy(cpi->mb.e_mbd.segment_feature_data, feature_data,
              sizeof(cpi->mb.e_mbd.segment_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(MACROBLOCKD *xd, 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(MACROBLOCKD *xd, 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) {
     // Test to see if the segment id matches the predicted value.
     const int pred_seg_id = vp9_get_pred_mi_segid(cm, mi->mbmi.sb_type,
                                                   mi_row, mi_col);
     const int seg_predicted = (segment_id == pred_seg_id);

     // Get the segment id prediction context
     const int pred_context = vp9_get_pred_context(cm, xd, PRED_SEG_ID);

     // Store the prediction status for this mb and update counts
     // as appropriate
     vp9_set_pred_flag(xd, PRED_SEG_ID, seg_predicted);
     temporal_predictor_count[pred_context][seg_predicted]++;

     if (!seg_predicted)
       // 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_SIZE_SB64X64) {
       subsize = BLOCK_SIZE_SB32X32;
     } else if (bsize == BLOCK_SIZE_SB32X32) {
       subsize = BLOCK_SIZE_MB16X16;
     } else {
       assert(bsize == BLOCK_SIZE_MB16X16);
       subsize = BLOCK_SIZE_SB8X8;
     }

     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;
   MACROBLOCKD *const xd = &cpi->mb.e_mbd;

   int no_pred_cost;
   int t_pred_cost = INT_MAX;

   int i;
   int tile_col, mi_row, mi_col;

   int temporal_predictor_count[PREDICTION_PROBS][2];
   int no_pred_segcounts[MAX_MB_SEGMENTS];
   int t_unpred_seg_counts[MAX_MB_SEGMENTS];

   vp9_prob no_pred_tree[MB_SEG_TREE_PROBS];
   vp9_prob t_pred_tree[MB_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(xd->mb_segment_tree_probs, 255, sizeof(xd->mb_segment_tree_probs));
   vpx_memset(cm->segment_pred_probs, 255, sizeof(cm->segment_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 < cm->tile_columns; 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_SIZE_SB64X64);
       }
     }
   }

   // Work out probability tree for coding segments without prediction
   // and the cost.
   calc_segtree_probs(xd, no_pred_segcounts, no_pred_tree);
   no_pred_cost = cost_segmap(xd, 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(xd, t_unpred_seg_counts, t_pred_tree);
     t_pred_cost = cost_segmap(xd, 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) {
     cm->temporal_update = 1;
     vpx_memcpy(xd->mb_segment_tree_probs, t_pred_tree, sizeof(t_pred_tree));
     vpx_memcpy(cm->segment_pred_probs, t_nopred_prob, sizeof(t_nopred_prob));
   } else {
     cm->temporal_update = 0;
     vpx_memcpy(xd->mb_segment_tree_probs, no_pred_tree, sizeof(no_pred_tree));
   }
 }
	/*
	* 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/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.segmentation_enabled = 1;
	cpi->mb.e_mbd.update_mb_segmentation_map = 1;
	cpi->mb.e_mbd.update_mb_segmentation_data = 1;
	}

	void vp9_disable_segmentation(VP9_PTR ptr) {
	VP9_COMP cpi = (VP9_COMP )ptr;
	cpi->mb.e_mbd.segmentation_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.update_mb_segmentation_map = 1;
	cpi->mb.e_mbd.update_mb_segmentation_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.mb_segment_abs_delta = abs_delta;

	vpx_memcpy(cpi->mb.e_mbd.segment_feature_data, feature_data,
	sizeof(cpi->mb.e_mbd.segment_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(MACROBLOCKD xd, 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(MACROBLOCKD xd, 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) {
	// Test to see if the segment id matches the predicted value.
	const int pred_seg_id = vp9_get_pred_mi_segid(cm, mi->mbmi.sb_type,
	mi_row, mi_col);
	const int seg_predicted = (segment_id == pred_seg_id);

	// Get the segment id prediction context
	const int pred_context = vp9_get_pred_context(cm, xd, PRED_SEG_ID);

	// Store the prediction status for this mb and update counts
	// as appropriate
	vp9_set_pred_flag(xd, PRED_SEG_ID, seg_predicted);
	temporal_predictor_count[pred_context][seg_predicted]++;

	if (!seg_predicted)
	// 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_SIZE_SB64X64) {
	subsize = BLOCK_SIZE_SB32X32;
	} else if (bsize == BLOCK_SIZE_SB32X32) {
	subsize = BLOCK_SIZE_MB16X16;
	} else {
	assert(bsize == BLOCK_SIZE_MB16X16);
	subsize = BLOCK_SIZE_SB8X8;
	}

	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;
	MACROBLOCKD *const xd = &cpi->mb.e_mbd;

	int no_pred_cost;
	int t_pred_cost = INT_MAX;

	int i;
	int tile_col, mi_row, mi_col;

	int temporal_predictor_count[PREDICTION_PROBS][2];
	int no_pred_segcounts[MAX_MB_SEGMENTS];
	int t_unpred_seg_counts[MAX_MB_SEGMENTS];

	vp9_prob no_pred_tree[MB_SEG_TREE_PROBS];
	vp9_prob t_pred_tree[MB_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(xd->mb_segment_tree_probs, 255, sizeof(xd->mb_segment_tree_probs));
	vpx_memset(cm->segment_pred_probs, 255, sizeof(cm->segment_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 < cm->tile_columns; 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_SIZE_SB64X64);
	}
	}
	}

	// Work out probability tree for coding segments without prediction
	// and the cost.
	calc_segtree_probs(xd, no_pred_segcounts, no_pred_tree);
	no_pred_cost = cost_segmap(xd, 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(xd, t_unpred_seg_counts, t_pred_tree);
	t_pred_cost = cost_segmap(xd, 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) {
	cm->temporal_update = 1;
	vpx_memcpy(xd->mb_segment_tree_probs, t_pred_tree, sizeof(t_pred_tree));
	vpx_memcpy(cm->segment_pred_probs, t_nopred_prob, sizeof(t_nopred_prob));
	} else {
	cm->temporal_update = 0;
	vpx_memcpy(xd->mb_segment_tree_probs, no_pred_tree, sizeof(no_pred_tree));
	}
	}