Adding upstream version 124.0.1.upstream/124.0.1

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 117 insertions, 0 deletions

diff --git a/third_party/jpeg-xl/lib/jxl/modular/options.h b/third_party/jpeg-xl/lib/jxl/modular/options.h
new file mode 100644
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+++ b/third_party/jpeg-xl/lib/jxl/modular/options.h
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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_