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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_
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