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authorDaniel Baumann <daniel.baumann@progress-linux.org>2024-04-19 00:47:55 +0000
committerDaniel Baumann <daniel.baumann@progress-linux.org>2024-04-19 00:47:55 +0000
commit26a029d407be480d791972afb5975cf62c9360a6 (patch)
treef435a8308119effd964b339f76abb83a57c29483 /third_party/jpeg-xl/lib/jxl/modular/options.h
parentInitial commit. (diff)
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Adding upstream version 124.0.1.upstream/124.0.1
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
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+// Copyright (c) the JPEG XL 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.
+
+#ifndef LIB_JXL_MODULAR_OPTIONS_H_
+#define LIB_JXL_MODULAR_OPTIONS_H_
+
+#include <stdint.h>
+
+#include <array>
+#include <vector>
+
+namespace jxl {
+
+using PropertyVal = int32_t;
+using Properties = std::vector<PropertyVal>;
+
+enum class Predictor : uint32_t {
+ Zero = 0,
+ Left = 1,
+ Top = 2,
+ Average0 = 3,
+ Select = 4,
+ Gradient = 5,
+ Weighted = 6,
+ TopRight = 7,
+ TopLeft = 8,
+ LeftLeft = 9,
+ Average1 = 10,
+ Average2 = 11,
+ Average3 = 12,
+ Average4 = 13,
+ // The following predictors are encoder-only.
+ Best = 14, // Best of Gradient and Weighted
+ Variable =
+ 15, // Find the best decision tree for predictors/predictor per row
+};
+
+constexpr size_t kNumModularPredictors =
+ static_cast<size_t>(Predictor::Average4) + 1;
+constexpr size_t kNumModularEncoderPredictors =
+ static_cast<size_t>(Predictor::Variable) + 1;
+
+static constexpr ssize_t kNumStaticProperties = 2; // channel, group_id.
+
+using StaticPropRange =
+ std::array<std::array<uint32_t, 2>, kNumStaticProperties>;
+
+struct ModularMultiplierInfo {
+ StaticPropRange range;
+ uint32_t multiplier;
+};
+
+struct ModularOptions {
+ /// Used in both encode and decode:
+
+ // Stop encoding/decoding when reaching a (non-meta) channel that has a
+ // dimension bigger than max_chan_size.
+ size_t max_chan_size = 0xFFFFFF;
+
+ // Used during decoding for validation of transforms (sqeeezing) scheme.
+ size_t group_dim = 0x1FFFFFFF;
+
+ /// Encode options:
+ // Fraction of pixels to look at to learn a MA tree
+ // Number of iterations to do to learn a MA tree
+ // (if zero there is no MA context model)
+ float nb_repeats = .5f;
+
+ // Maximum number of (previous channel) properties to use in the MA trees
+ int max_properties = 0; // no previous channels
+
+ // Alternative heuristic tweaks.
+ // Properties default to channel, group, weighted, gradient residual, W-NW,
+ // NW-N, N-NE, N-NN
+ std::vector<uint32_t> splitting_heuristics_properties = {0, 1, 15, 9,
+ 10, 11, 12, 13};
+ float splitting_heuristics_node_threshold = 96;
+ size_t max_property_values = 32;
+
+ // Predictor to use for each channel.
+ Predictor predictor = static_cast<Predictor>(-1);
+
+ int wp_mode = 0;
+
+ float fast_decode_multiplier = 1.01f;
+
+ // Forces the encoder to produce a tree that is compatible with the WP-only
+ // decode path (or with the no-wp path, or the gradient-only path).
+ enum class TreeMode { kGradientOnly, kWPOnly, kNoWP, kDefault };
+ TreeMode wp_tree_mode = TreeMode::kDefault;
+
+ // Skip fast paths in the encoder.
+ bool skip_encoder_fast_path = false;
+
+ // Kind of tree to use.
+ // TODO(veluca): add tree kinds for JPEG recompression with CfL enabled,
+ // general AC metadata, different DC qualities, and others.
+ enum class TreeKind {
+ kTrivialTreeNoPredictor,
+ kLearn,
+ kJpegTranscodeACMeta,
+ kFalconACMeta,
+ kACMeta,
+ kWPFixedDC,
+ kGradientFixedDC,
+ };
+ TreeKind tree_kind = TreeKind::kLearn;
+
+ // Ignore the image and just pretend all tokens are zeroes
+ bool zero_tokens = false;
+};
+
+} // namespace jxl
+
+#endif // LIB_JXL_MODULAR_OPTIONS_H_