Esempio n. 1
0
static void adapt_coef_probs(VP9_COMMON *cm, TX_SIZE tx_size,
                             unsigned int count_sat,
                             unsigned int update_factor) {
  FRAME_CONTEXT *pre_fc = &cm->frame_contexts[cm->frame_context_idx];

  vp9_coeff_probs_model *dst_coef_probs = cm->fc.coef_probs[tx_size];
  vp9_coeff_probs_model *pre_coef_probs = pre_fc->coef_probs[tx_size];
  vp9_coeff_count_model *coef_counts = cm->counts.coef[tx_size];
  unsigned int (*eob_branch_count)[REF_TYPES][COEF_BANDS][PREV_COEF_CONTEXTS] =
      cm->counts.eob_branch[tx_size];
  int t, i, j, k, l;
  unsigned int branch_ct[UNCONSTRAINED_NODES][2];
  vp9_prob coef_probs[UNCONSTRAINED_NODES];

  for (i = 0; i < BLOCK_TYPES; ++i)
    for (j = 0; j < REF_TYPES; ++j)
      for (k = 0; k < COEF_BANDS; ++k)
        for (l = 0; l < PREV_COEF_CONTEXTS; ++l) {
          if (l >= 3 && k == 0)
            continue;
          vp9_tree_probs_from_distribution(vp9_coefmodel_tree, coef_probs,
                                           branch_ct, coef_counts[i][j][k][l],
                                           0);
          branch_ct[0][1] = eob_branch_count[i][j][k][l] - branch_ct[0][0];
          coef_probs[0] = get_binary_prob(branch_ct[0][0], branch_ct[0][1]);
          for (t = 0; t < UNCONSTRAINED_NODES; ++t)
            dst_coef_probs[i][j][k][l][t] = merge_probs(
                pre_coef_probs[i][j][k][l][t], coef_probs[t],
                branch_ct[t], count_sat, update_factor);
        }
}
Esempio n. 2
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static void build_tree_distribution(VP9_COMP *cpi, TX_SIZE tx_size,
                                    vp9_coeff_stats *coef_branch_ct,
                                    vp9_coeff_probs_model *coef_probs) {
  vp9_coeff_count *coef_counts = cpi->td.rd_counts.coef_counts[tx_size];
  unsigned int (*eob_branch_ct)[REF_TYPES][COEF_BANDS][COEFF_CONTEXTS] =
      cpi->common.counts.eob_branch[tx_size];
  int i, j, k, l, m;

  for (i = 0; i < PLANE_TYPES; ++i) {
    for (j = 0; j < REF_TYPES; ++j) {
      for (k = 0; k < COEF_BANDS; ++k) {
        for (l = 0; l < BAND_COEFF_CONTEXTS(k); ++l) {
          vp9_tree_probs_from_distribution(vp9_coef_tree,
                                           coef_branch_ct[i][j][k][l],
                                           coef_counts[i][j][k][l]);
          coef_branch_ct[i][j][k][l][0][1] = eob_branch_ct[i][j][k][l] -
                                             coef_branch_ct[i][j][k][l][0][0];
          for (m = 0; m < UNCONSTRAINED_NODES; ++m)
            coef_probs[i][j][k][l][m] = get_binary_prob(
                                            coef_branch_ct[i][j][k][l][m][0],
                                            coef_branch_ct[i][j][k][l][m][1]);
        }
      }
    }
  }
}
Esempio n. 3
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// Based on set of segment counts calculate a probability tree
static void calc_segtree_probs(unsigned *segcounts,
                               aom_prob *segment_tree_probs,
                               const aom_prob *cur_tree_probs,
                               const int probwt) {
  // Work out probabilities of each segment
  const unsigned cc[4] = { segcounts[0] + segcounts[1],
                           segcounts[2] + segcounts[3],
                           segcounts[4] + segcounts[5],
                           segcounts[6] + segcounts[7] };
  const unsigned ccc[2] = { cc[0] + cc[1], cc[2] + cc[3] };
  int i;

  segment_tree_probs[0] = get_binary_prob(ccc[0], ccc[1]);
  segment_tree_probs[1] = get_binary_prob(cc[0], cc[1]);
  segment_tree_probs[2] = get_binary_prob(cc[2], cc[3]);
  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]);

  for (i = 0; i < 7; i++) {
    const unsigned *ct =
        i == 0 ? ccc : i < 3 ? cc + (i & 2) : segcounts + (i - 3) * 2;
    av1_prob_diff_update_savings_search(ct, cur_tree_probs[i],
                                        &segment_tree_probs[i],
                                        DIFF_UPDATE_PROB, probwt);
  }
}
Esempio n. 4
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// Based on set of segment counts calculate a probability tree
static void calc_segtree_probs(unsigned *segcounts,
                               vpx_prob *segment_tree_probs,
                               const vpx_prob *cur_tree_probs) {
  // Work out probabilities of each segment
  const unsigned cc[4] = { segcounts[0] + segcounts[1],
                           segcounts[2] + segcounts[3],
                           segcounts[4] + segcounts[5],
                           segcounts[6] + segcounts[7] };
  const unsigned ccc[2] = { cc[0] + cc[1], cc[2] + cc[3] };
#if CONFIG_MISC_FIXES
  int i;
#endif

  segment_tree_probs[0] = get_binary_prob(ccc[0], ccc[1]);
  segment_tree_probs[1] = get_binary_prob(cc[0], cc[1]);
  segment_tree_probs[2] = get_binary_prob(cc[2], cc[3]);
  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]);

#if CONFIG_MISC_FIXES
  for (i = 0; i < 7; i++) {
    const unsigned *ct =
        i == 0 ? ccc : i < 3 ? cc + (i & 2) : segcounts + (i - 3) * 2;
    vp10_prob_diff_update_savings_search(
        ct, cur_tree_probs[i], &segment_tree_probs[i], DIFF_UPDATE_PROB);
  }
#else
  (void)cur_tree_probs;
#endif
}
Esempio n. 5
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static void print_probs(FILE *f, vp9_coeff_accum *context_counters,
                        int block_types, const char *header) {
  int type, ref, band, pt, t;

  fprintf(f, "static const vp9_coeff_probs %s = {", header);

  type = 0;
#define Newline(x, spaces) (x ? " " : "\n" spaces)
  do {
    fprintf(f, "%s%s{ /* block Type %d */",
            Comma(type), Newline(type, "  "), type);
    ref = 0;
    do {
      fprintf(f, "%s%s{ /* %s */",
              Comma(band), Newline(band, "    "), ref ? "Inter" : "Intra");
      band = 0;
      do {
        fprintf(f, "%s%s{ /* Coeff Band %d */",
                Comma(band), Newline(band, "      "), band);
        pt = 0;
        do {
          unsigned int branch_ct[ENTROPY_NODES][2];
          unsigned int coef_counts[MAX_ENTROPY_TOKENS + 1];
          vp9_prob coef_probs[ENTROPY_NODES];

          if (pt >= 3 && band == 0)
            break;
          for (t = 0; t < MAX_ENTROPY_TOKENS + 1; ++t)
            coef_counts[t] = context_counters[type][ref][band][pt][t];
          vp9_tree_probs_from_distribution(vp9_coef_tree, coef_probs,
                                           branch_ct, coef_counts, 0);
          branch_ct[0][1] = coef_counts[MAX_ENTROPY_TOKENS] - branch_ct[0][0];
          coef_probs[0] = get_binary_prob(branch_ct[0][0], branch_ct[0][1]);
          fprintf(f, "%s\n      {", Comma(pt));

          t = 0;
          do {
            fprintf(f, "%s %3d", Comma(t), coef_probs[t]);
          } while (++t < ENTROPY_NODES);

          fprintf(f, " }");
        } while (++pt < PREV_COEF_CONTEXTS);
        fprintf(f, "\n      }");
      } while (++band < COEF_BANDS);
      fprintf(f, "\n    }");
    } while (++ref < REF_TYPES);
    fprintf(f, "\n  }");
  } while (++type < block_types);
  fprintf(f, "\n};\n");
}
Esempio n. 6
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void vp9_tree_probs_from_distribution(
  int n,                      /* n = size of alphabet */
  vp9_token tok               [ /* n */ ],
  vp9_tree tree,
  vp9_prob probs          [ /* n-1 */ ],
  unsigned int branch_ct       [ /* n-1 */ ] [2],
  const unsigned int num_events[ /* n */ ]
) {
  const int tree_len = n - 1;
  int t = 0;

  branch_counts(n, tok, tree, branch_ct, num_events);

  do {
    probs[t] = get_binary_prob(branch_ct[t][0], branch_ct[t][1]);
  } while (++t < tree_len);
}
Esempio n. 7
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// 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]);
}
Esempio n. 8
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void av1_choose_segmap_coding_method(AV1_COMMON *cm, MACROBLOCKD *xd) {
  struct segmentation *seg = &cm->seg;
  struct segmentation_probs *segp = &cm->fc->seg;

  int no_pred_cost;
  int t_pred_cost = INT_MAX;

  int i, tile_col, tile_row, mi_row, mi_col;
#if CONFIG_TILE_GROUPS
  const int probwt = cm->num_tg;
#else
  const int probwt = 1;
#endif

  unsigned(*temporal_predictor_count)[2] = cm->counts.seg.pred;
  unsigned *no_pred_segcounts = cm->counts.seg.tree_total;
  unsigned *t_unpred_seg_counts = cm->counts.seg.tree_mispred;

  aom_prob no_pred_tree[SEG_TREE_PROBS];
  aom_prob t_pred_tree[SEG_TREE_PROBS];
  aom_prob t_nopred_prob[PREDICTION_PROBS];

  (void)xd;

  // We are about to recompute all the segment counts, so zero the accumulators.
  av1_zero(cm->counts.seg);

  // First of all generate stats regarding how well the last segment map
  // predicts this one
  for (tile_row = 0; tile_row < cm->tile_rows; tile_row++) {
    TileInfo tile_info;
    av1_tile_set_row(&tile_info, cm, tile_row);
    for (tile_col = 0; tile_col < cm->tile_cols; tile_col++) {
      MODE_INFO **mi_ptr;
      av1_tile_set_col(&tile_info, cm, tile_col);
      mi_ptr = cm->mi_grid_visible + tile_info.mi_row_start * cm->mi_stride +
               tile_info.mi_col_start;
      for (mi_row = tile_info.mi_row_start; mi_row < tile_info.mi_row_end;
           mi_row += cm->mib_size, mi_ptr += cm->mib_size * cm->mi_stride) {
        MODE_INFO **mi = mi_ptr;
        for (mi_col = tile_info.mi_col_start; mi_col < tile_info.mi_col_end;
             mi_col += cm->mib_size, mi += cm->mib_size) {
          count_segs_sb(cm, xd, &tile_info, mi, no_pred_segcounts,
                        temporal_predictor_count, t_unpred_seg_counts, mi_row,
                        mi_col, cm->sb_size);
        }
      }
    }
  }

  // Work out probability tree for coding segments without prediction
  // and the cost.
  calc_segtree_probs(no_pred_segcounts, no_pred_tree, segp->tree_probs, probwt);
  no_pred_cost = cost_segmap(no_pred_segcounts, no_pred_tree);

  // Key frames cannot use temporal prediction
  if (!frame_is_intra_only(cm) && !cm->error_resilient_mode) {
    // 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, segp->tree_probs,
                       probwt);
    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);
      av1_prob_diff_update_savings_search(
          temporal_predictor_count[i], segp->pred_probs[i], &t_nopred_prob[i],
          DIFF_UPDATE_PROB, probwt);

      // Add in the predictor signaling cost
      t_pred_cost += count0 * av1_cost_zero(t_nopred_prob[i]) +
                     count1 * av1_cost_one(t_nopred_prob[i]);
    }
  }

  // Now choose which coding method to use.
  if (t_pred_cost < no_pred_cost) {
    assert(!cm->error_resilient_mode);
    seg->temporal_update = 1;
  } else {
    seg->temporal_update = 0;
  }
}
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));
  }
}
Esempio n. 10
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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));
  }
}
Esempio n. 11
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void vp10_choose_segmap_coding_method(VP10_COMMON *cm, MACROBLOCKD *xd) {
  struct segmentation *seg = &cm->seg;
#if CONFIG_MISC_FIXES
  struct segmentation_probs *segp = &cm->fc->seg;
#else
  struct segmentation_probs *segp = &cm->segp;
#endif

  int no_pred_cost;
  int t_pred_cost = INT_MAX;

  int i, tile_col, mi_row, mi_col;

#if CONFIG_MISC_FIXES
  unsigned(*temporal_predictor_count)[2] = cm->counts.seg.pred;
  unsigned *no_pred_segcounts = cm->counts.seg.tree_total;
  unsigned *t_unpred_seg_counts = cm->counts.seg.tree_mispred;
#else
  unsigned temporal_predictor_count[PREDICTION_PROBS][2] = { { 0 } };
  unsigned no_pred_segcounts[MAX_SEGMENTS] = { 0 };
  unsigned t_unpred_seg_counts[MAX_SEGMENTS] = { 0 };
#endif

  vpx_prob no_pred_tree[SEG_TREE_PROBS];
  vpx_prob t_pred_tree[SEG_TREE_PROBS];
  vpx_prob t_nopred_prob[PREDICTION_PROBS];

#if CONFIG_MISC_FIXES
  (void)xd;
#else
  // Set default state for the segment tree probabilities and the
  // temporal coding probabilities
  memset(segp->tree_probs, 255, sizeof(segp->tree_probs));
  memset(segp->pred_probs, 255, sizeof(segp->pred_probs));
#endif

  // 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;
    vp10_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, segp->tree_probs);
  no_pred_cost = cost_segmap(no_pred_segcounts, no_pred_tree);

  // Key frames cannot use temporal prediction
  if (!frame_is_intra_only(cm) && !cm->error_resilient_mode) {
    // 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, segp->tree_probs);
    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];

#if CONFIG_MISC_FIXES
      vp10_prob_diff_update_savings_search(temporal_predictor_count[i],
                                           segp->pred_probs[i],
                                           &t_nopred_prob[i], DIFF_UPDATE_PROB);
#else
      t_nopred_prob[i] = get_binary_prob(count0, count1);
#endif

      // Add in the predictor signaling cost
      t_pred_cost += count0 * vp10_cost_zero(t_nopred_prob[i]) +
                     count1 * vp10_cost_one(t_nopred_prob[i]);
    }
  }

  // Now choose which coding method to use.
  if (t_pred_cost < no_pred_cost) {
    assert(!cm->error_resilient_mode);
    seg->temporal_update = 1;
#if !CONFIG_MISC_FIXES
    memcpy(segp->tree_probs, t_pred_tree, sizeof(t_pred_tree));
    memcpy(segp->pred_probs, t_nopred_prob, sizeof(t_nopred_prob));
#endif
  } else {
    seg->temporal_update = 0;
#if !CONFIG_MISC_FIXES
    memcpy(segp->tree_probs, no_pred_tree, sizeof(no_pred_tree));
#endif
  }
}