Perform greedy histogram merge in a unified way.
Previously, the stochastic method for histogram combination could finish in a greedy way if the number of iterations to perform so was smaller. Except that another greedy combination was performed afterwards ... hence wasted CPU in some cases. Change-Id: Ic0f26873e6dc746679486b91cb35d73efee91931
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@ -777,10 +777,13 @@ static int HistogramCombineGreedy(VP8LHistogramSet* const image_histo) {
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return ok;
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}
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// Perform histogram aggregation using a stochastic approach.
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// 'do_greedy' is set to 1 if a greedy approach needs to be performed
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// afterwards, 0 otherwise.
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static void HistogramCombineStochastic(VP8LHistogramSet* const image_histo,
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VP8LHistogram* tmp_histo,
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VP8LHistogram* best_combo,
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int quality, int min_cluster_size) {
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VP8LHistogram* best_combo, int quality,
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int min_cluster_size, int* do_greedy) {
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int iter;
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uint32_t seed = 0;
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int tries_with_no_success = 0;
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@ -789,48 +792,44 @@ static void HistogramCombineStochastic(VP8LHistogramSet* const image_histo,
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const int outer_iters = image_histo_size * iter_mult;
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const int num_pairs = image_histo_size / 2;
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const int num_tries_no_success = outer_iters / 2;
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int idx2_max = image_histo_size - 1;
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int do_brute_dorce = 0;
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VP8LHistogram** const histograms = image_histo->histograms;
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// Collapse similar histograms in 'image_histo'.
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*do_greedy = (image_histo->size <= min_cluster_size);
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++min_cluster_size;
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for (iter = 0;
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iter < outer_iters && image_histo_size >= min_cluster_size;
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for (iter = 0; iter < outer_iters && image_histo_size >= min_cluster_size &&
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++tries_with_no_success < num_tries_no_success;
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++iter) {
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double best_cost_diff = 0.;
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int best_idx1 = -1, best_idx2 = 1;
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int j;
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int num_tries =
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(num_pairs < image_histo_size) ? num_pairs : image_histo_size;
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// Use a brute force approach if:
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// - stochastic has not worked for a while and
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// - if the number of iterations for brute force is less than the number of
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// iterations if we never find a match ever again stochastically (hence
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// num_tries times the number of remaining outer iterations).
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do_brute_dorce =
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(tries_with_no_success > 10) &&
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(idx2_max * (idx2_max + 1) < 2 * num_tries * (outer_iters - iter));
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if (do_brute_dorce) num_tries = idx2_max;
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// If the stochastic method has not worked for a while (10 iterations) and
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// if it requires less iterations to finish off with a greedy approach, go
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// for it.
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// With the greedy approach, each histogram is compared to the other ones,
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// hence (image_histo_size-1)*image_histo_size/2 overall comparisons.
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// Then, at each iteration, the best pair is merged and compared to all
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// the other ones, adding (image_histo_size-2)*(image_histo_size-1)/2 more
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// comparisons. Overall: (image_histo_size-1)^2 comparisons.
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*do_greedy |= (tries_with_no_success > 10) &&
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((image_histo_size - 1) * (image_histo_size - 1) <
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num_tries * (outer_iters - iter));
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if (*do_greedy) break;
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seed += iter;
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for (j = 0; j < num_tries; ++j) {
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double curr_cost_diff;
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// Choose two histograms at random and try to combine them.
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uint32_t idx1, idx2;
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if (do_brute_dorce) {
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// Use a brute force approach.
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idx1 = (uint32_t)j;
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idx2 = (uint32_t)idx2_max;
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} else {
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const uint32_t tmp = (j & 7) + 1;
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const uint32_t diff =
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(tmp < 3) ? tmp : MyRand(&seed) % (image_histo_size - 1);
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idx1 = MyRand(&seed) % image_histo_size;
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idx2 = (idx1 + diff + 1) % image_histo_size;
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if (idx1 == idx2) {
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continue;
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}
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const uint32_t tmp = (j & 7) + 1;
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const uint32_t diff =
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(tmp < 3) ? tmp : MyRand(&seed) % (image_histo_size - 1);
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idx1 = MyRand(&seed) % image_histo_size;
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idx2 = (idx1 + diff + 1) % image_histo_size;
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if (idx1 == idx2) {
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continue;
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}
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// Calculate cost reduction on combining.
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@ -843,24 +842,20 @@ static void HistogramCombineStochastic(VP8LHistogramSet* const image_histo,
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best_idx2 = idx2;
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}
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}
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if (do_brute_dorce) --idx2_max;
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if (best_idx1 >= 0) {
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HistogramSwap(&best_combo, &histograms[best_idx1]);
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// swap best_idx2 slot with last one (which is now unused)
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--image_histo_size;
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if (idx2_max >= image_histo_size) idx2_max = image_histo_size - 1;
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if (best_idx2 != image_histo_size) {
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HistogramSwap(&histograms[image_histo_size], &histograms[best_idx2]);
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histograms[image_histo_size] = NULL;
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}
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tries_with_no_success = 0;
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}
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if (++tries_with_no_success >= num_tries_no_success || idx2_max == 0) {
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break;
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}
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}
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image_histo->size = image_histo_size;
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*do_greedy |= (image_histo->size <= min_cluster_size);
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}
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// -----------------------------------------------------------------------------
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@ -970,10 +965,10 @@ int VP8LGetHistoImageSymbols(int xsize, int ysize,
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const float x = quality / 100.f;
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// cubic ramp between 1 and MAX_HISTO_GREEDY:
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const int threshold_size = (int)(1 + (x * x * x) * (MAX_HISTO_GREEDY - 1));
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int do_greedy;
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HistogramCombineStochastic(image_histo, tmp_histos->histograms[0],
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cur_combo, quality, threshold_size);
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if ((image_histo->size <= threshold_size) &&
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!HistogramCombineGreedy(image_histo)) {
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cur_combo, quality, threshold_size, &do_greedy);
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if (do_greedy && !HistogramCombineGreedy(image_histo)) {
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goto Error;
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}
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}
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