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author | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-04-19 01:13:27 +0000 |
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committer | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-04-19 01:13:27 +0000 |
commit | 40a355a42d4a9444dc753c04c6608dade2f06a23 (patch) | |
tree | 871fc667d2de662f171103ce5ec067014ef85e61 /third_party/jpeg-xl/lib/jxl/modular/encoding/enc_encoding.cc | |
parent | Adding upstream version 124.0.1. (diff) | |
download | firefox-40a355a42d4a9444dc753c04c6608dade2f06a23.tar.xz firefox-40a355a42d4a9444dc753c04c6608dade2f06a23.zip |
Adding upstream version 125.0.1.upstream/125.0.1
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
Diffstat (limited to 'third_party/jpeg-xl/lib/jxl/modular/encoding/enc_encoding.cc')
-rw-r--r-- | third_party/jpeg-xl/lib/jxl/modular/encoding/enc_encoding.cc | 82 |
1 files changed, 47 insertions, 35 deletions
diff --git a/third_party/jpeg-xl/lib/jxl/modular/encoding/enc_encoding.cc b/third_party/jpeg-xl/lib/jxl/modular/encoding/enc_encoding.cc index fc2e69e4a6..84d8137d21 100644 --- a/third_party/jpeg-xl/lib/jxl/modular/encoding/enc_encoding.cc +++ b/third_party/jpeg-xl/lib/jxl/modular/encoding/enc_encoding.cc @@ -6,35 +6,24 @@ #include <stdint.h> #include <stdlib.h> -#include <cinttypes> #include <limits> -#include <numeric> #include <queue> -#include <set> -#include <unordered_map> -#include <unordered_set> #include "lib/jxl/base/common.h" #include "lib/jxl/base/printf_macros.h" #include "lib/jxl/base/status.h" -#include "lib/jxl/dec_ans.h" -#include "lib/jxl/dec_bit_reader.h" #include "lib/jxl/enc_ans.h" #include "lib/jxl/enc_aux_out.h" #include "lib/jxl/enc_bit_writer.h" #include "lib/jxl/enc_fields.h" -#include "lib/jxl/entropy_coder.h" #include "lib/jxl/fields.h" #include "lib/jxl/image_ops.h" #include "lib/jxl/modular/encoding/context_predict.h" -#include "lib/jxl/modular/encoding/enc_debug_tree.h" #include "lib/jxl/modular/encoding/enc_ma.h" #include "lib/jxl/modular/encoding/encoding.h" #include "lib/jxl/modular/encoding/ma_common.h" #include "lib/jxl/modular/options.h" -#include "lib/jxl/modular/transform/transform.h" #include "lib/jxl/pack_signed.h" -#include "lib/jxl/toc.h" namespace jxl { @@ -61,7 +50,7 @@ inline std::array<uint8_t, 3> PredictorColor(Predictor p) { return {{255, 0, 255}}; case Predictor::Weighted: return {{0, 255, 255}}; - // TODO + // TODO(jon) default: return {{255, 255, 255}}; }; @@ -101,17 +90,17 @@ Tree MakeFixedTree(int property, const std::vector<int32_t> &cutoffs, } // namespace -void GatherTreeData(const Image &image, pixel_type chan, size_t group_id, - const weighted::Header &wp_header, - const ModularOptions &options, TreeSamples &tree_samples, - size_t *total_pixels) { +Status GatherTreeData(const Image &image, pixel_type chan, size_t group_id, + const weighted::Header &wp_header, + const ModularOptions &options, TreeSamples &tree_samples, + size_t *total_pixels) { const Channel &channel = image.channel[chan]; JXL_DEBUG_V(7, "Learning %" PRIuS "x%" PRIuS " channel %d", channel.w, channel.h, chan); std::array<pixel_type, kNumStaticProperties> static_props = { - {chan, (int)group_id}}; + {chan, static_cast<int>(group_id)}}; Properties properties(kNumNonrefProperties + kExtraPropsPerChannel * options.max_properties); double pixel_fraction = std::min(1.0f, options.nb_repeats); @@ -137,7 +126,9 @@ void GatherTreeData(const Image &image, pixel_type chan, size_t group_id, }; const intptr_t onerow = channel.plane.PixelsPerRow(); - Channel references(properties.size() - kNumNonrefProperties, channel.w); + JXL_ASSIGN_OR_RETURN( + Channel references, + Channel::Create(properties.size() - kNumNonrefProperties, channel.w)); weighted::State wp_state(wp_header, channel.w, channel.h); tree_samples.PrepareForSamples(pixel_fraction * channel.h * channel.w + 64); const bool multiple_predictors = tree_samples.NumPredictors() != 1; @@ -198,6 +189,7 @@ void GatherTreeData(const Image &image, pixel_type chan, size_t group_id, } } } + return true; } Tree PredefinedTree(ModularOptions::TreeKind tree_kind, size_t total_pixels) { @@ -316,7 +308,9 @@ Status EncodeModularChannelMAANS(const Image &image, pixel_type chan, JXL_ASSERT(channel.w != 0 && channel.h != 0); Image3F predictor_img; - if (kWantDebug) predictor_img = Image3F(channel.w, channel.h); + if (kWantDebug) { + JXL_ASSIGN_OR_RETURN(predictor_img, Image3F::Create(channel.w, channel.h)); + } JXL_DEBUG_V(6, "Encoding %" PRIuS "x%" PRIuS @@ -325,8 +319,9 @@ Status EncodeModularChannelMAANS(const Image &image, pixel_type chan, channel.w, channel.h, chan, channel.hshift, channel.vshift); std::array<pixel_type, kNumStaticProperties> static_props = { - {chan, (int)group_id}}; - bool use_wp, is_wp_only; + {chan, static_cast<int>(group_id)}}; + bool use_wp; + bool is_wp_only; bool is_gradient_only; size_t num_props; FlatTree tree = FilterTree(global_tree, static_props, &num_props, &use_wp, @@ -439,7 +434,8 @@ Status EncodeModularChannelMAANS(const Image &image, pixel_type chan, FillImage(static_cast<float>(PredictorColor(tree[0].predictor)[c]), &predictor_img.Plane(c)); } - uint32_t mul_shift = FloorLog2Nonzero((uint32_t)tree[0].multiplier); + uint32_t mul_shift = + FloorLog2Nonzero(static_cast<uint32_t>(tree[0].multiplier)); const intptr_t onerow = channel.plane.PixelsPerRow(); for (size_t y = 0; y < channel.h; y++) { const pixel_type *JXL_RESTRICT r = channel.Row(y); @@ -454,7 +450,9 @@ Status EncodeModularChannelMAANS(const Image &image, pixel_type chan, } else if (!use_wp && !skip_encoder_fast_path) { const intptr_t onerow = channel.plane.PixelsPerRow(); - Channel references(properties.size() - kNumNonrefProperties, channel.w); + JXL_ASSIGN_OR_RETURN( + Channel references, + Channel::Create(properties.size() - kNumNonrefProperties, channel.w)); for (size_t y = 0; y < channel.h; y++) { const pixel_type *JXL_RESTRICT p = channel.Row(y); PrecomputeReferences(channel, y, image, chan, &references); @@ -481,7 +479,9 @@ Status EncodeModularChannelMAANS(const Image &image, pixel_type chan, } } else { const intptr_t onerow = channel.plane.PixelsPerRow(); - Channel references(properties.size() - kNumNonrefProperties, channel.w); + JXL_ASSIGN_OR_RETURN( + Channel references, + Channel::Create(properties.size() - kNumNonrefProperties, channel.w)); weighted::State wp_state(wp_header, channel.w, channel.h); for (size_t y = 0; y < channel.h; y++) { const pixel_type *JXL_RESTRICT p = channel.Row(y); @@ -556,8 +556,20 @@ Status ModularEncode(const Image &image, const ModularOptions &options, TreeSamples tree_samples_storage; size_t total_pixels_storage = 0; if (!total_pixels) total_pixels = &total_pixels_storage; + if (*total_pixels == 0) { + for (size_t i = 0; i < nb_channels; i++) { + if (i >= image.nb_meta_channels && + (image.channel[i].w > options.max_chan_size || + image.channel[i].h > options.max_chan_size)) { + break; + } + *total_pixels += image.channel[i].w * image.channel[i].h; + } + *total_pixels = std::max<size_t>(*total_pixels, 1); + } // If there's no tree, compute one (or gather data to). - if (tree == nullptr) { + if (tree == nullptr && + options.tree_kind == ModularOptions::TreeKind::kLearn) { bool gather_data = tree_samples != nullptr; if (tree_samples == nullptr) { JXL_RETURN_IF_ERROR(tree_samples_storage.SetPredictor( @@ -586,9 +598,9 @@ Status ModularEncode(const Image &image, const ModularOptions &options, image.channel[i].h > options.max_chan_size)) { break; } - GatherTreeData(image, i, group_id, header->wp_header, options, - gather_data ? *tree_samples : tree_samples_storage, - total_pixels); + JXL_RETURN_IF_ERROR(GatherTreeData( + image, i, group_id, header->wp_header, options, + gather_data ? *tree_samples : tree_samples_storage, total_pixels)); } if (gather_data) return true; } @@ -609,10 +621,10 @@ Status ModularEncode(const Image &image, const ModularOptions &options, ? LearnTree(std::move(tree_samples_storage), *total_pixels, options) : PredefinedTree(options.tree_kind, *total_pixels); tree = &tree_storage; - tokens = &tokens_storage[0]; + tokens = tokens_storage.data(); Tree decoded_tree; - TokenizeTree(*tree, &tree_tokens[0], &decoded_tree); + TokenizeTree(*tree, tree_tokens.data(), &decoded_tree); JXL_ASSERT(tree->size() == decoded_tree.size()); tree_storage = std::move(decoded_tree); @@ -622,9 +634,9 @@ Status ModularEncode(const Image &image, const ModularOptions &options, } */ // Write tree - BuildAndEncodeHistograms(HistogramParams(), kNumTreeContexts, tree_tokens, - &code, &context_map, writer, kLayerModularTree, - aux_out); + BuildAndEncodeHistograms(options.histogram_params, kNumTreeContexts, + tree_tokens, &code, &context_map, writer, + kLayerModularTree, aux_out); WriteTokens(tree_tokens[0], code, context_map, 0, writer, kLayerModularTree, aux_out); } @@ -669,7 +681,7 @@ Status ModularEncode(const Image &image, const ModularOptions &options, if (!header->use_global_tree) { EntropyEncodingData code; std::vector<uint8_t> context_map; - HistogramParams histo_params; + HistogramParams histo_params = options.histogram_params; histo_params.image_widths.push_back(image_width); BuildAndEncodeHistograms(histo_params, (tree->size() + 1) / 2, tokens_storage, &code, &context_map, writer, layer, @@ -691,7 +703,7 @@ Status ModularGenericCompress(Image &image, const ModularOptions &opts, if (image.w == 0 || image.h == 0) return true; ModularOptions options = opts; // Make a copy to modify it. - if (options.predictor == static_cast<Predictor>(-1)) { + if (options.predictor == kUndefinedPredictor) { options.predictor = Predictor::Gradient; } |