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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/enc_modular.cc
parentInitial commit. (diff)
downloadfirefox-26a029d407be480d791972afb5975cf62c9360a6.tar.xz
firefox-26a029d407be480d791972afb5975cf62c9360a6.zip
Adding upstream version 124.0.1.upstream/124.0.1
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
Diffstat (limited to 'third_party/jpeg-xl/lib/jxl/enc_modular.cc')
-rw-r--r--third_party/jpeg-xl/lib/jxl/enc_modular.cc1646
1 files changed, 1646 insertions, 0 deletions
diff --git a/third_party/jpeg-xl/lib/jxl/enc_modular.cc b/third_party/jpeg-xl/lib/jxl/enc_modular.cc
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+++ b/third_party/jpeg-xl/lib/jxl/enc_modular.cc
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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.
+
+#include "lib/jxl/enc_modular.h"
+
+#include <stddef.h>
+#include <stdint.h>
+
+#include <array>
+#include <atomic>
+#include <limits>
+#include <queue>
+#include <utility>
+#include <vector>
+
+#include "lib/jxl/base/compiler_specific.h"
+#include "lib/jxl/base/printf_macros.h"
+#include "lib/jxl/base/status.h"
+#include "lib/jxl/compressed_dc.h"
+#include "lib/jxl/dec_ans.h"
+#include "lib/jxl/enc_aux_out.h"
+#include "lib/jxl/enc_bit_writer.h"
+#include "lib/jxl/enc_cluster.h"
+#include "lib/jxl/enc_fields.h"
+#include "lib/jxl/enc_gaborish.h"
+#include "lib/jxl/enc_params.h"
+#include "lib/jxl/enc_patch_dictionary.h"
+#include "lib/jxl/enc_quant_weights.h"
+#include "lib/jxl/frame_header.h"
+#include "lib/jxl/modular/encoding/context_predict.h"
+#include "lib/jxl/modular/encoding/enc_debug_tree.h"
+#include "lib/jxl/modular/encoding/enc_encoding.h"
+#include "lib/jxl/modular/encoding/encoding.h"
+#include "lib/jxl/modular/encoding/ma_common.h"
+#include "lib/jxl/modular/modular_image.h"
+#include "lib/jxl/modular/options.h"
+#include "lib/jxl/modular/transform/enc_transform.h"
+#include "lib/jxl/pack_signed.h"
+#include "lib/jxl/toc.h"
+
+namespace jxl {
+
+namespace {
+// constexpr bool kPrintTree = false;
+
+// Squeeze default quantization factors
+// these quantization factors are for -Q 50 (other qualities simply scale the
+// factors; things are rounded down and obviously cannot get below 1)
+static const float squeeze_quality_factor =
+ 0.35; // for easy tweaking of the quality range (decrease this number for
+ // higher quality)
+static const float squeeze_luma_factor =
+ 1.1; // for easy tweaking of the balance between luma (or anything
+ // non-chroma) and chroma (decrease this number for higher quality
+ // luma)
+static const float squeeze_quality_factor_xyb = 2.4f;
+static const float squeeze_xyb_qtable[3][16] = {
+ {163.84, 81.92, 40.96, 20.48, 10.24, 5.12, 2.56, 1.28, 0.64, 0.32, 0.16,
+ 0.08, 0.04, 0.02, 0.01, 0.005}, // Y
+ {1024, 512, 256, 128, 64, 32, 16, 8, 4, 2, 1, 0.5, 0.5, 0.5, 0.5,
+ 0.5}, // X
+ {2048, 1024, 512, 256, 128, 64, 32, 16, 8, 4, 2, 1, 0.5, 0.5, 0.5,
+ 0.5}, // B-Y
+};
+
+static const float squeeze_luma_qtable[16] = {
+ 163.84, 81.92, 40.96, 20.48, 10.24, 5.12, 2.56, 1.28,
+ 0.64, 0.32, 0.16, 0.08, 0.04, 0.02, 0.01, 0.005};
+// for 8-bit input, the range of YCoCg chroma is -255..255 so basically this
+// does 4:2:0 subsampling (two most fine grained layers get quantized away)
+static const float squeeze_chroma_qtable[16] = {
+ 1024, 512, 256, 128, 64, 32, 16, 8, 4, 2, 1, 0.5, 0.5, 0.5, 0.5, 0.5};
+
+// Merges the trees in `trees` using nodes that decide on stream_id, as defined
+// by `tree_splits`.
+void MergeTrees(const std::vector<Tree>& trees,
+ const std::vector<size_t>& tree_splits, size_t begin,
+ size_t end, Tree* tree) {
+ JXL_ASSERT(trees.size() + 1 == tree_splits.size());
+ JXL_ASSERT(end > begin);
+ JXL_ASSERT(end <= trees.size());
+ if (end == begin + 1) {
+ // Insert the tree, adding the opportune offset to all child nodes.
+ // This will make the leaf IDs wrong, but subsequent roundtripping will fix
+ // them.
+ size_t sz = tree->size();
+ tree->insert(tree->end(), trees[begin].begin(), trees[begin].end());
+ for (size_t i = sz; i < tree->size(); i++) {
+ (*tree)[i].lchild += sz;
+ (*tree)[i].rchild += sz;
+ }
+ return;
+ }
+ size_t mid = (begin + end) / 2;
+ size_t splitval = tree_splits[mid] - 1;
+ size_t cur = tree->size();
+ tree->emplace_back(1 /*stream_id*/, splitval, 0, 0, Predictor::Zero, 0, 1);
+ (*tree)[cur].lchild = tree->size();
+ MergeTrees(trees, tree_splits, mid, end, tree);
+ (*tree)[cur].rchild = tree->size();
+ MergeTrees(trees, tree_splits, begin, mid, tree);
+}
+
+void QuantizeChannel(Channel& ch, const int q) {
+ if (q == 1) return;
+ for (size_t y = 0; y < ch.plane.ysize(); y++) {
+ pixel_type* row = ch.plane.Row(y);
+ for (size_t x = 0; x < ch.plane.xsize(); x++) {
+ if (row[x] < 0) {
+ row[x] = -((-row[x] + q / 2) / q) * q;
+ } else {
+ row[x] = ((row[x] + q / 2) / q) * q;
+ }
+ }
+ }
+}
+
+// convert binary32 float that corresponds to custom [bits]-bit float (with
+// [exp_bits] exponent bits) to a [bits]-bit integer representation that should
+// fit in pixel_type
+Status float_to_int(const float* const row_in, pixel_type* const row_out,
+ size_t xsize, unsigned int bits, unsigned int exp_bits,
+ bool fp, double dfactor) {
+ JXL_ASSERT(sizeof(pixel_type) * 8 >= bits);
+ if (!fp) {
+ if (bits > 22) {
+ for (size_t x = 0; x < xsize; ++x) {
+ row_out[x] = row_in[x] * dfactor + (row_in[x] < 0 ? -0.5 : 0.5);
+ }
+ } else {
+ float factor = dfactor;
+ for (size_t x = 0; x < xsize; ++x) {
+ row_out[x] = row_in[x] * factor + (row_in[x] < 0 ? -0.5f : 0.5f);
+ }
+ }
+ return true;
+ }
+ if (bits == 32 && fp) {
+ JXL_ASSERT(exp_bits == 8);
+ memcpy((void*)row_out, (const void*)row_in, 4 * xsize);
+ return true;
+ }
+
+ int exp_bias = (1 << (exp_bits - 1)) - 1;
+ int max_exp = (1 << exp_bits) - 1;
+ uint32_t sign = (1u << (bits - 1));
+ int mant_bits = bits - exp_bits - 1;
+ int mant_shift = 23 - mant_bits;
+ for (size_t x = 0; x < xsize; ++x) {
+ uint32_t f;
+ memcpy(&f, &row_in[x], 4);
+ int signbit = (f >> 31);
+ f &= 0x7fffffff;
+ if (f == 0) {
+ row_out[x] = (signbit ? sign : 0);
+ continue;
+ }
+ int exp = (f >> 23) - 127;
+ if (exp == 128) return JXL_FAILURE("Inf/NaN not allowed");
+ int mantissa = (f & 0x007fffff);
+ // broke up the binary32 into its parts, now reassemble into
+ // arbitrary float
+ exp += exp_bias;
+ if (exp < 0) { // will become a subnormal number
+ // add implicit leading 1 to mantissa
+ mantissa |= 0x00800000;
+ if (exp < -mant_bits) {
+ return JXL_FAILURE(
+ "Invalid float number: %g cannot be represented with %i "
+ "exp_bits and %i mant_bits (exp %i)",
+ row_in[x], exp_bits, mant_bits, exp);
+ }
+ mantissa >>= 1 - exp;
+ exp = 0;
+ }
+ // exp should be representable in exp_bits, otherwise input was
+ // invalid
+ if (exp > max_exp) return JXL_FAILURE("Invalid float exponent");
+ if (mantissa & ((1 << mant_shift) - 1)) {
+ return JXL_FAILURE("%g is losing precision (mant: %x)", row_in[x],
+ mantissa);
+ }
+ mantissa >>= mant_shift;
+ f = (signbit ? sign : 0);
+ f |= (exp << mant_bits);
+ f |= mantissa;
+ row_out[x] = (pixel_type)f;
+ }
+ return true;
+}
+} // namespace
+
+ModularFrameEncoder::ModularFrameEncoder(const FrameHeader& frame_header,
+ const CompressParams& cparams_orig)
+ : frame_dim_(frame_header.ToFrameDimensions()), cparams_(cparams_orig) {
+ size_t num_streams =
+ ModularStreamId::Num(frame_dim_, frame_header.passes.num_passes);
+ if (cparams_.ModularPartIsLossless()) {
+ switch (cparams_.decoding_speed_tier) {
+ case 0:
+ break;
+ case 1:
+ cparams_.options.wp_tree_mode = ModularOptions::TreeMode::kWPOnly;
+ break;
+ case 2: {
+ cparams_.options.wp_tree_mode = ModularOptions::TreeMode::kGradientOnly;
+ cparams_.options.predictor = Predictor::Gradient;
+ break;
+ }
+ case 3: { // LZ77, no Gradient.
+ cparams_.options.nb_repeats = 0;
+ cparams_.options.predictor = Predictor::Gradient;
+ break;
+ }
+ default: { // LZ77, no predictor.
+ cparams_.options.nb_repeats = 0;
+ cparams_.options.predictor = Predictor::Zero;
+ break;
+ }
+ }
+ }
+ if (cparams_.decoding_speed_tier >= 1 && cparams_.responsive &&
+ cparams_.ModularPartIsLossless()) {
+ cparams_.options.tree_kind =
+ ModularOptions::TreeKind::kTrivialTreeNoPredictor;
+ cparams_.options.nb_repeats = 0;
+ }
+ stream_images_.resize(num_streams);
+
+ // use a sensible default if nothing explicit is specified:
+ // Squeeze for lossy, no squeeze for lossless
+ if (cparams_.responsive < 0) {
+ if (cparams_.ModularPartIsLossless()) {
+ cparams_.responsive = 0;
+ } else {
+ cparams_.responsive = 1;
+ }
+ }
+
+ cparams_.options.splitting_heuristics_node_threshold =
+ 82 + 14 * static_cast<int>(cparams_.speed_tier);
+
+ {
+ // Set properties.
+ std::vector<uint32_t> prop_order;
+ if (cparams_.responsive) {
+ // Properties in order of their likelihood of being useful for Squeeze
+ // residuals.
+ prop_order = {0, 1, 4, 5, 6, 7, 8, 15, 9, 10, 11, 12, 13, 14, 2, 3};
+ } else {
+ // Same, but for the non-Squeeze case.
+ prop_order = {0, 1, 15, 9, 10, 11, 12, 13, 14, 2, 3, 4, 5, 6, 7, 8};
+ // if few groups, don't use group as a property
+ if (num_streams < 30 && cparams_.speed_tier > SpeedTier::kTortoise) {
+ prop_order.erase(prop_order.begin() + 1);
+ }
+ }
+ switch (cparams_.speed_tier) {
+ case SpeedTier::kHare:
+ cparams_.options.splitting_heuristics_properties.assign(
+ prop_order.begin(), prop_order.begin() + 4);
+ cparams_.options.max_property_values = 24;
+ break;
+ case SpeedTier::kWombat:
+ cparams_.options.splitting_heuristics_properties.assign(
+ prop_order.begin(), prop_order.begin() + 5);
+ cparams_.options.max_property_values = 32;
+ break;
+ case SpeedTier::kSquirrel:
+ cparams_.options.splitting_heuristics_properties.assign(
+ prop_order.begin(), prop_order.begin() + 7);
+ cparams_.options.max_property_values = 48;
+ break;
+ case SpeedTier::kKitten:
+ cparams_.options.splitting_heuristics_properties.assign(
+ prop_order.begin(), prop_order.begin() + 10);
+ cparams_.options.max_property_values = 96;
+ break;
+ case SpeedTier::kTortoise:
+ cparams_.options.splitting_heuristics_properties = prop_order;
+ cparams_.options.max_property_values = 256;
+ break;
+ default:
+ cparams_.options.splitting_heuristics_properties.assign(
+ prop_order.begin(), prop_order.begin() + 3);
+ cparams_.options.max_property_values = 16;
+ break;
+ }
+ if (cparams_.speed_tier > SpeedTier::kTortoise) {
+ // Gradient in previous channels.
+ for (int i = 0; i < cparams_.options.max_properties; i++) {
+ cparams_.options.splitting_heuristics_properties.push_back(
+ kNumNonrefProperties + i * 4 + 3);
+ }
+ } else {
+ // All the extra properties in Tortoise mode.
+ for (int i = 0; i < cparams_.options.max_properties * 4; i++) {
+ cparams_.options.splitting_heuristics_properties.push_back(
+ kNumNonrefProperties + i);
+ }
+ }
+ }
+
+ if (cparams_.options.predictor == static_cast<Predictor>(-1)) {
+ // no explicit predictor(s) given, set a good default
+ if ((cparams_.speed_tier <= SpeedTier::kTortoise ||
+ cparams_.modular_mode == false) &&
+ cparams_.IsLossless() && cparams_.responsive == false) {
+ // TODO(veluca): allow all predictors that don't break residual
+ // multipliers in lossy mode.
+ cparams_.options.predictor = Predictor::Variable;
+ } else if (cparams_.responsive || cparams_.lossy_palette) {
+ // zero predictor for Squeeze residues and lossy palette
+ cparams_.options.predictor = Predictor::Zero;
+ } else if (!cparams_.IsLossless()) {
+ // If not responsive and lossy. TODO(veluca): use near_lossless instead?
+ cparams_.options.predictor = Predictor::Gradient;
+ } else if (cparams_.speed_tier < SpeedTier::kFalcon) {
+ // try median and weighted predictor for anything else
+ cparams_.options.predictor = Predictor::Best;
+ } else if (cparams_.speed_tier == SpeedTier::kFalcon) {
+ // just weighted predictor in falcon mode
+ cparams_.options.predictor = Predictor::Weighted;
+ } else if (cparams_.speed_tier > SpeedTier::kFalcon) {
+ // just gradient predictor in thunder mode
+ cparams_.options.predictor = Predictor::Gradient;
+ }
+ } else {
+ delta_pred_ = cparams_.options.predictor;
+ if (cparams_.lossy_palette) cparams_.options.predictor = Predictor::Zero;
+ }
+ if (!cparams_.ModularPartIsLossless()) {
+ if (cparams_.options.predictor == Predictor::Weighted ||
+ cparams_.options.predictor == Predictor::Variable ||
+ cparams_.options.predictor == Predictor::Best)
+ cparams_.options.predictor = Predictor::Zero;
+ }
+ tree_splits_.push_back(0);
+ if (cparams_.modular_mode == false) {
+ cparams_.options.fast_decode_multiplier = 1.0f;
+ tree_splits_.push_back(ModularStreamId::VarDCTDC(0).ID(frame_dim_));
+ tree_splits_.push_back(ModularStreamId::ModularDC(0).ID(frame_dim_));
+ tree_splits_.push_back(ModularStreamId::ACMetadata(0).ID(frame_dim_));
+ tree_splits_.push_back(ModularStreamId::QuantTable(0).ID(frame_dim_));
+ tree_splits_.push_back(ModularStreamId::ModularAC(0, 0).ID(frame_dim_));
+ ac_metadata_size.resize(frame_dim_.num_dc_groups);
+ extra_dc_precision.resize(frame_dim_.num_dc_groups);
+ }
+ tree_splits_.push_back(num_streams);
+ cparams_.options.max_chan_size = frame_dim_.group_dim;
+ cparams_.options.group_dim = frame_dim_.group_dim;
+
+ // TODO(veluca): figure out how to use different predictor sets per channel.
+ stream_options_.resize(num_streams, cparams_.options);
+}
+
+bool do_transform(Image& image, const Transform& tr,
+ const weighted::Header& wp_header,
+ jxl::ThreadPool* pool = nullptr, bool force_jxlart = false) {
+ Transform t = tr;
+ bool did_it = true;
+ if (force_jxlart) {
+ if (!t.MetaApply(image)) return false;
+ } else {
+ did_it = TransformForward(t, image, wp_header, pool);
+ }
+ if (did_it) image.transform.push_back(t);
+ return did_it;
+}
+
+Status ModularFrameEncoder::ComputeEncodingData(
+ const FrameHeader& frame_header, const ImageMetadata& metadata,
+ Image3F* JXL_RESTRICT color, const std::vector<ImageF>& extra_channels,
+ PassesEncoderState* JXL_RESTRICT enc_state, const JxlCmsInterface& cms,
+ ThreadPool* pool, AuxOut* aux_out, bool do_color) {
+ JXL_DEBUG_V(6, "Computing modular encoding data for frame %s",
+ frame_header.DebugString().c_str());
+
+ if (do_color && frame_header.loop_filter.gab) {
+ float w = 0.9908511000000001f;
+ float weights[3] = {w, w, w};
+ GaborishInverse(color, Rect(*color), weights, pool);
+ }
+
+ if (do_color && metadata.bit_depth.bits_per_sample <= 16 &&
+ cparams_.speed_tier < SpeedTier::kCheetah &&
+ cparams_.decoding_speed_tier < 2) {
+ FindBestPatchDictionary(*color, enc_state, cms, nullptr, aux_out,
+ cparams_.color_transform == ColorTransform::kXYB);
+ PatchDictionaryEncoder::SubtractFrom(
+ enc_state->shared.image_features.patches, color);
+ }
+
+ // Convert ImageBundle to modular Image object
+ const size_t xsize = frame_dim_.xsize;
+ const size_t ysize = frame_dim_.ysize;
+
+ int nb_chans = 3;
+ if (metadata.color_encoding.IsGray() &&
+ cparams_.color_transform == ColorTransform::kNone) {
+ nb_chans = 1;
+ }
+ if (!do_color) nb_chans = 0;
+
+ nb_chans += extra_channels.size();
+
+ bool fp = metadata.bit_depth.floating_point_sample &&
+ cparams_.color_transform != ColorTransform::kXYB;
+
+ // bits_per_sample is just metadata for XYB images.
+ if (metadata.bit_depth.bits_per_sample >= 32 && do_color &&
+ cparams_.color_transform != ColorTransform::kXYB) {
+ if (metadata.bit_depth.bits_per_sample == 32 && fp == false) {
+ return JXL_FAILURE("uint32_t not supported in enc_modular");
+ } else if (metadata.bit_depth.bits_per_sample > 32) {
+ return JXL_FAILURE("bits_per_sample > 32 not supported");
+ }
+ }
+
+ // in the non-float case, there is an implicit 0 sign bit
+ int max_bitdepth =
+ do_color ? metadata.bit_depth.bits_per_sample + (fp ? 0 : 1) : 0;
+ Image& gi = stream_images_[0];
+ gi = Image(xsize, ysize, metadata.bit_depth.bits_per_sample, nb_chans);
+ int c = 0;
+ if (cparams_.color_transform == ColorTransform::kXYB &&
+ cparams_.modular_mode == true) {
+ float enc_factors[3] = {32768.0f, 2048.0f, 2048.0f};
+ if (cparams_.butteraugli_distance > 0 && !cparams_.responsive) {
+ // quantize XYB here and then treat it as a lossless image
+ enc_factors[0] *= 1.f / (1.f + 23.f * cparams_.butteraugli_distance);
+ enc_factors[1] *= 1.f / (1.f + 14.f * cparams_.butteraugli_distance);
+ enc_factors[2] *= 1.f / (1.f + 14.f * cparams_.butteraugli_distance);
+ cparams_.butteraugli_distance = 0;
+ }
+ if (cparams_.manual_xyb_factors.size() == 3) {
+ DequantMatricesSetCustomDC(&enc_state->shared.matrices,
+ cparams_.manual_xyb_factors.data());
+ // TODO(jon): update max_bitdepth in this case
+ } else {
+ DequantMatricesSetCustomDC(&enc_state->shared.matrices, enc_factors);
+ max_bitdepth = 12;
+ }
+ }
+ pixel_type maxval = gi.bitdepth < 32 ? (1u << gi.bitdepth) - 1 : 0;
+ if (do_color) {
+ for (; c < 3; c++) {
+ if (metadata.color_encoding.IsGray() &&
+ cparams_.color_transform == ColorTransform::kNone &&
+ c != (cparams_.color_transform == ColorTransform::kXYB ? 1 : 0))
+ continue;
+ int c_out = c;
+ // XYB is encoded as YX(B-Y)
+ if (cparams_.color_transform == ColorTransform::kXYB && c < 2)
+ c_out = 1 - c_out;
+ double factor = maxval;
+ if (cparams_.color_transform == ColorTransform::kXYB)
+ factor = enc_state->shared.matrices.InvDCQuant(c);
+ if (c == 2 && cparams_.color_transform == ColorTransform::kXYB) {
+ JXL_ASSERT(!fp);
+ for (size_t y = 0; y < ysize; ++y) {
+ const float* const JXL_RESTRICT row_in = color->PlaneRow(c, y);
+ pixel_type* const JXL_RESTRICT row_out = gi.channel[c_out].Row(y);
+ pixel_type* const JXL_RESTRICT row_Y = gi.channel[0].Row(y);
+ for (size_t x = 0; x < xsize; ++x) {
+ row_out[x] = row_in[x] * factor + 0.5f;
+ row_out[x] -= row_Y[x];
+ // zero the lsb of B
+ row_out[x] = row_out[x] / 2 * 2;
+ }
+ }
+ } else {
+ int bits = metadata.bit_depth.bits_per_sample;
+ int exp_bits = metadata.bit_depth.exponent_bits_per_sample;
+ gi.channel[c_out].hshift = frame_header.chroma_subsampling.HShift(c);
+ gi.channel[c_out].vshift = frame_header.chroma_subsampling.VShift(c);
+ size_t xsize_shifted = DivCeil(xsize, 1 << gi.channel[c_out].hshift);
+ size_t ysize_shifted = DivCeil(ysize, 1 << gi.channel[c_out].vshift);
+ gi.channel[c_out].shrink(xsize_shifted, ysize_shifted);
+ std::atomic<bool> has_error{false};
+ JXL_RETURN_IF_ERROR(RunOnPool(
+ pool, 0, ysize_shifted, ThreadPool::NoInit,
+ [&](const int task, const int thread) {
+ const size_t y = task;
+ const float* const JXL_RESTRICT row_in = color->PlaneRow(c, y);
+ pixel_type* const JXL_RESTRICT row_out = gi.channel[c_out].Row(y);
+ if (!float_to_int(row_in, row_out, xsize_shifted, bits, exp_bits,
+ fp, factor)) {
+ has_error = true;
+ };
+ },
+ "float2int"));
+ if (has_error) {
+ return JXL_FAILURE("Error in float to integer conversion");
+ }
+ }
+ }
+ if (metadata.color_encoding.IsGray() &&
+ cparams_.color_transform == ColorTransform::kNone)
+ c = 1;
+ }
+
+ for (size_t ec = 0; ec < extra_channels.size(); ec++, c++) {
+ const ExtraChannelInfo& eci = metadata.extra_channel_info[ec];
+ size_t ecups = frame_header.extra_channel_upsampling[ec];
+ gi.channel[c].shrink(DivCeil(frame_dim_.xsize_upsampled, ecups),
+ DivCeil(frame_dim_.ysize_upsampled, ecups));
+ gi.channel[c].hshift = gi.channel[c].vshift =
+ CeilLog2Nonzero(ecups) - CeilLog2Nonzero(frame_header.upsampling);
+
+ int bits = eci.bit_depth.bits_per_sample;
+ int exp_bits = eci.bit_depth.exponent_bits_per_sample;
+ bool fp = eci.bit_depth.floating_point_sample;
+ double factor = (fp ? 1 : ((1u << eci.bit_depth.bits_per_sample) - 1));
+ if (bits + (fp ? 0 : 1) > max_bitdepth) max_bitdepth = bits + (fp ? 0 : 1);
+ std::atomic<bool> has_error{false};
+ JXL_RETURN_IF_ERROR(RunOnPool(
+ pool, 0, gi.channel[c].plane.ysize(), ThreadPool::NoInit,
+ [&](const int task, const int thread) {
+ const size_t y = task;
+ const float* const JXL_RESTRICT row_in = extra_channels[ec].Row(y);
+ pixel_type* const JXL_RESTRICT row_out = gi.channel[c].Row(y);
+ if (!float_to_int(row_in, row_out, gi.channel[c].plane.xsize(), bits,
+ exp_bits, fp, factor)) {
+ has_error = true;
+ };
+ },
+ "float2int"));
+ if (has_error) return JXL_FAILURE("Error in float to integer conversion");
+ }
+ JXL_ASSERT(c == nb_chans);
+
+ int level_max_bitdepth = (cparams_.level == 5 ? 16 : 32);
+ if (max_bitdepth > level_max_bitdepth)
+ return JXL_FAILURE(
+ "Bitdepth too high for level %i (need %i bits, have only %i in this "
+ "level)",
+ cparams_.level, max_bitdepth, level_max_bitdepth);
+
+ // Set options and apply transformations
+ if (!cparams_.ModularPartIsLossless()) {
+ if (cparams_.palette_colors != 0) {
+ JXL_DEBUG_V(3, "Lossy encode, not doing palette transforms");
+ }
+ if (cparams_.color_transform == ColorTransform::kXYB) {
+ cparams_.channel_colors_pre_transform_percent = 0;
+ }
+ cparams_.channel_colors_percent = 0;
+ cparams_.palette_colors = 0;
+ cparams_.lossy_palette = false;
+ }
+
+ // Global palette
+ if (cparams_.palette_colors != 0 || cparams_.lossy_palette) {
+ // all-channel palette (e.g. RGBA)
+ if (gi.channel.size() - gi.nb_meta_channels > 1) {
+ Transform maybe_palette(TransformId::kPalette);
+ maybe_palette.begin_c = gi.nb_meta_channels;
+ maybe_palette.num_c = gi.channel.size() - gi.nb_meta_channels;
+ maybe_palette.nb_colors =
+ std::min((int)(xsize * ysize / 2), std::abs(cparams_.palette_colors));
+ maybe_palette.ordered_palette = cparams_.palette_colors >= 0;
+ maybe_palette.lossy_palette =
+ (cparams_.lossy_palette && maybe_palette.num_c == 3);
+ if (maybe_palette.lossy_palette) {
+ maybe_palette.predictor = delta_pred_;
+ }
+ // TODO(veluca): use a custom weighted header if using the weighted
+ // predictor.
+ do_transform(gi, maybe_palette, weighted::Header(), pool,
+ cparams_.options.zero_tokens);
+ }
+ // all-minus-one-channel palette (RGB with separate alpha, or CMY with
+ // separate K)
+ if (gi.channel.size() - gi.nb_meta_channels > 3) {
+ Transform maybe_palette_3(TransformId::kPalette);
+ maybe_palette_3.begin_c = gi.nb_meta_channels;
+ maybe_palette_3.num_c = gi.channel.size() - gi.nb_meta_channels - 1;
+ maybe_palette_3.nb_colors =
+ std::min((int)(xsize * ysize / 3), std::abs(cparams_.palette_colors));
+ maybe_palette_3.ordered_palette = cparams_.palette_colors >= 0;
+ maybe_palette_3.lossy_palette = cparams_.lossy_palette;
+ if (maybe_palette_3.lossy_palette) {
+ maybe_palette_3.predictor = delta_pred_;
+ }
+ do_transform(gi, maybe_palette_3, weighted::Header(), pool,
+ cparams_.options.zero_tokens);
+ }
+ }
+
+ // Global channel palette
+ if (cparams_.channel_colors_pre_transform_percent > 0 &&
+ !cparams_.lossy_palette &&
+ (cparams_.speed_tier <= SpeedTier::kThunder ||
+ (do_color && metadata.bit_depth.bits_per_sample > 8))) {
+ // single channel palette (like FLIF's ChannelCompact)
+ size_t nb_channels = gi.channel.size() - gi.nb_meta_channels;
+ int orig_bitdepth = max_bitdepth;
+ max_bitdepth = 0;
+ for (size_t i = 0; i < nb_channels; i++) {
+ int32_t min, max;
+ compute_minmax(gi.channel[gi.nb_meta_channels + i], &min, &max);
+ int64_t colors = (int64_t)max - min + 1;
+ JXL_DEBUG_V(10, "Channel %" PRIuS ": range=%i..%i", i, min, max);
+ Transform maybe_palette_1(TransformId::kPalette);
+ maybe_palette_1.begin_c = i + gi.nb_meta_channels;
+ maybe_palette_1.num_c = 1;
+ // simple heuristic: if less than X percent of the values in the range
+ // actually occur, it is probably worth it to do a compaction
+ // (but only if the channel palette is less than 6% the size of the
+ // image itself)
+ maybe_palette_1.nb_colors = std::min(
+ (int)(xsize * ysize / 16),
+ (int)(cparams_.channel_colors_pre_transform_percent / 100. * colors));
+ if (do_transform(gi, maybe_palette_1, weighted::Header(), pool)) {
+ // effective bit depth is lower, adjust quantization accordingly
+ compute_minmax(gi.channel[gi.nb_meta_channels + i], &min, &max);
+ if (max < maxval) maxval = max;
+ int ch_bitdepth =
+ (max > 0 ? CeilLog2Nonzero(static_cast<uint32_t>(max)) : 0);
+ if (ch_bitdepth > max_bitdepth) max_bitdepth = ch_bitdepth;
+ } else
+ max_bitdepth = orig_bitdepth;
+ }
+ }
+
+ // don't do an RCT if we're short on bits
+ if (cparams_.color_transform == ColorTransform::kNone && do_color &&
+ gi.channel.size() - gi.nb_meta_channels >= 3 &&
+ max_bitdepth + 1 < level_max_bitdepth) {
+ if (cparams_.colorspace < 0 && (!cparams_.ModularPartIsLossless() ||
+ cparams_.speed_tier > SpeedTier::kHare)) {
+ Transform ycocg{TransformId::kRCT};
+ ycocg.rct_type = 6;
+ ycocg.begin_c = gi.nb_meta_channels;
+ do_transform(gi, ycocg, weighted::Header(), pool);
+ max_bitdepth++;
+ } else if (cparams_.colorspace > 0) {
+ Transform sg(TransformId::kRCT);
+ sg.begin_c = gi.nb_meta_channels;
+ sg.rct_type = cparams_.colorspace;
+ do_transform(gi, sg, weighted::Header(), pool);
+ max_bitdepth++;
+ }
+ }
+
+ // don't do squeeze if we don't have some spare bits
+ if (cparams_.responsive && !gi.channel.empty() &&
+ max_bitdepth + 2 < level_max_bitdepth) {
+ Transform t(TransformId::kSqueeze);
+ do_transform(gi, t, weighted::Header(), pool);
+ max_bitdepth += 2;
+ }
+
+ if (max_bitdepth + 1 > level_max_bitdepth) {
+ // force no group RCTs if we don't have a spare bit
+ cparams_.colorspace = 0;
+ }
+ JXL_ASSERT(max_bitdepth <= level_max_bitdepth);
+
+ if (!cparams_.ModularPartIsLossless()) {
+ quants_.resize(gi.channel.size(), 1);
+ float quantizer = 0.25f;
+ if (!cparams_.responsive) {
+ JXL_DEBUG_V(1,
+ "Warning: lossy compression without Squeeze "
+ "transform is just color quantization.");
+ quantizer *= 0.1f;
+ }
+ float bitdepth_correction = 1.f;
+ if (cparams_.color_transform != ColorTransform::kXYB) {
+ bitdepth_correction = maxval / 255.f;
+ }
+ std::vector<float> quantizers;
+ float dist = cparams_.butteraugli_distance;
+ for (size_t i = 0; i < 3; i++) {
+ quantizers.push_back(quantizer * dist * bitdepth_correction);
+ }
+ for (size_t i = 0; i < extra_channels.size(); i++) {
+ int ec_bitdepth =
+ metadata.extra_channel_info[i].bit_depth.bits_per_sample;
+ pixel_type ec_maxval = ec_bitdepth < 32 ? (1u << ec_bitdepth) - 1 : 0;
+ bitdepth_correction = ec_maxval / 255.f;
+ if (i < cparams_.ec_distance.size()) dist = cparams_.ec_distance[i];
+ if (dist < 0) dist = cparams_.butteraugli_distance;
+ quantizers.push_back(quantizer * dist * bitdepth_correction);
+ }
+ if (cparams_.options.nb_repeats == 0) {
+ return JXL_FAILURE("nb_repeats = 0 not supported with modular lossy!");
+ }
+ for (uint32_t i = gi.nb_meta_channels; i < gi.channel.size(); i++) {
+ Channel& ch = gi.channel[i];
+ int shift = ch.hshift + ch.vshift; // number of pixel halvings
+ if (shift > 16) shift = 16;
+ if (shift > 0) shift--;
+ int q;
+ // assuming default Squeeze here
+ int component =
+ (do_color ? 0 : 3) + ((i - gi.nb_meta_channels) % nb_chans);
+ // last 4 channels are final chroma residuals
+ if (nb_chans > 2 && i >= gi.channel.size() - 4 && cparams_.responsive) {
+ component = 1;
+ }
+ if (cparams_.color_transform == ColorTransform::kXYB && component < 3) {
+ q = quantizers[component] * squeeze_quality_factor_xyb *
+ squeeze_xyb_qtable[component][shift];
+ } else {
+ if (cparams_.colorspace != 0 && component > 0 && component < 3) {
+ q = quantizers[component] * squeeze_quality_factor *
+ squeeze_chroma_qtable[shift];
+ } else {
+ q = quantizers[component] * squeeze_quality_factor *
+ squeeze_luma_factor * squeeze_luma_qtable[shift];
+ }
+ }
+ if (q < 1) q = 1;
+ QuantizeChannel(gi.channel[i], q);
+ quants_[i] = q;
+ }
+ }
+
+ // Fill other groups.
+ struct GroupParams {
+ Rect rect;
+ int minShift;
+ int maxShift;
+ ModularStreamId id;
+ };
+ std::vector<GroupParams> stream_params;
+
+ stream_options_[0] = cparams_.options;
+
+ // DC
+ for (size_t group_id = 0; group_id < frame_dim_.num_dc_groups; group_id++) {
+ const size_t gx = group_id % frame_dim_.xsize_dc_groups;
+ const size_t gy = group_id / frame_dim_.xsize_dc_groups;
+ const Rect rect(gx * frame_dim_.dc_group_dim, gy * frame_dim_.dc_group_dim,
+ frame_dim_.dc_group_dim, frame_dim_.dc_group_dim);
+ // minShift==3 because (frame_dim.dc_group_dim >> 3) == frame_dim.group_dim
+ // maxShift==1000 is infinity
+ stream_params.push_back(
+ GroupParams{rect, 3, 1000, ModularStreamId::ModularDC(group_id)});
+ }
+ // AC global -> nothing.
+ // AC
+ for (size_t group_id = 0; group_id < frame_dim_.num_groups; group_id++) {
+ const size_t gx = group_id % frame_dim_.xsize_groups;
+ const size_t gy = group_id / frame_dim_.xsize_groups;
+ const Rect mrect(gx * frame_dim_.group_dim, gy * frame_dim_.group_dim,
+ frame_dim_.group_dim, frame_dim_.group_dim);
+ for (size_t i = 0; i < enc_state->progressive_splitter.GetNumPasses();
+ i++) {
+ int maxShift, minShift;
+ frame_header.passes.GetDownsamplingBracket(i, minShift, maxShift);
+ stream_params.push_back(GroupParams{
+ mrect, minShift, maxShift, ModularStreamId::ModularAC(group_id, i)});
+ }
+ }
+ // if there's only one group, everything ends up in GlobalModular
+ // in that case, also try RCTs/WP params for the one group
+ if (stream_params.size() == 2) {
+ stream_params.push_back(GroupParams{Rect(0, 0, xsize, ysize), 0, 1000,
+ ModularStreamId::Global()});
+ }
+ gi_channel_.resize(stream_images_.size());
+
+ JXL_RETURN_IF_ERROR(RunOnPool(
+ pool, 0, stream_params.size(), ThreadPool::NoInit,
+ [&](const uint32_t i, size_t /* thread */) {
+ stream_options_[stream_params[i].id.ID(frame_dim_)] = cparams_.options;
+ JXL_CHECK(PrepareStreamParams(
+ stream_params[i].rect, cparams_, stream_params[i].minShift,
+ stream_params[i].maxShift, stream_params[i].id, do_color));
+ },
+ "ChooseParams"));
+ {
+ // Clear out channels that have been copied to groups.
+ Image& full_image = stream_images_[0];
+ size_t c = full_image.nb_meta_channels;
+ for (; c < full_image.channel.size(); c++) {
+ Channel& fc = full_image.channel[c];
+ if (fc.w > frame_dim_.group_dim || fc.h > frame_dim_.group_dim) break;
+ }
+ for (; c < full_image.channel.size(); c++) {
+ full_image.channel[c].plane = ImageI();
+ }
+ }
+
+ JXL_RETURN_IF_ERROR(ValidateChannelDimensions(gi, stream_options_[0]));
+ return true;
+}
+
+Status ModularFrameEncoder::ComputeTree(ThreadPool* pool) {
+ std::vector<ModularMultiplierInfo> multiplier_info;
+ if (!quants_.empty()) {
+ for (uint32_t stream_id = 0; stream_id < stream_images_.size();
+ stream_id++) {
+ // skip non-modular stream_ids
+ if (stream_id > 0 && gi_channel_[stream_id].empty()) continue;
+ const Image& image = stream_images_[stream_id];
+ const ModularOptions& options = stream_options_[stream_id];
+ for (uint32_t i = image.nb_meta_channels; i < image.channel.size(); i++) {
+ if (i >= image.nb_meta_channels &&
+ (image.channel[i].w > options.max_chan_size ||
+ image.channel[i].h > options.max_chan_size)) {
+ continue;
+ }
+ if (stream_id > 0 && gi_channel_[stream_id].empty()) continue;
+ size_t ch_id = stream_id == 0
+ ? i
+ : gi_channel_[stream_id][i - image.nb_meta_channels];
+ uint32_t q = quants_[ch_id];
+ // Inform the tree splitting heuristics that each channel in each group
+ // used this quantization factor. This will produce a tree with the
+ // given multipliers.
+ if (multiplier_info.empty() ||
+ multiplier_info.back().range[1][0] != stream_id ||
+ multiplier_info.back().multiplier != q) {
+ StaticPropRange range;
+ range[0] = {{i, i + 1}};
+ range[1] = {{stream_id, stream_id + 1}};
+ multiplier_info.push_back({range, (uint32_t)q});
+ } else {
+ // Previous channel in the same group had the same quantization
+ // factor. Don't provide two different ranges, as that creates
+ // unnecessary nodes.
+ multiplier_info.back().range[0][1] = i + 1;
+ }
+ }
+ }
+ // Merge group+channel settings that have the same channels and quantization
+ // factors, to avoid unnecessary nodes.
+ std::sort(multiplier_info.begin(), multiplier_info.end(),
+ [](ModularMultiplierInfo a, ModularMultiplierInfo b) {
+ return std::make_tuple(a.range, a.multiplier) <
+ std::make_tuple(b.range, b.multiplier);
+ });
+ size_t new_num = 1;
+ for (size_t i = 1; i < multiplier_info.size(); i++) {
+ ModularMultiplierInfo& prev = multiplier_info[new_num - 1];
+ ModularMultiplierInfo& cur = multiplier_info[i];
+ if (prev.range[0] == cur.range[0] && prev.multiplier == cur.multiplier &&
+ prev.range[1][1] == cur.range[1][0]) {
+ prev.range[1][1] = cur.range[1][1];
+ } else {
+ multiplier_info[new_num++] = multiplier_info[i];
+ }
+ }
+ multiplier_info.resize(new_num);
+ }
+
+ if (!cparams_.custom_fixed_tree.empty()) {
+ tree_ = cparams_.custom_fixed_tree;
+ } else if (cparams_.speed_tier < SpeedTier::kFalcon ||
+ !cparams_.modular_mode) {
+ // Avoid creating a tree with leaves that don't correspond to any pixels.
+ std::vector<size_t> useful_splits;
+ useful_splits.reserve(tree_splits_.size());
+ for (size_t chunk = 0; chunk < tree_splits_.size() - 1; chunk++) {
+ bool has_pixels = false;
+ size_t start = tree_splits_[chunk];
+ size_t stop = tree_splits_[chunk + 1];
+ for (size_t i = start; i < stop; i++) {
+ if (!stream_images_[i].empty()) has_pixels = true;
+ }
+ if (has_pixels) {
+ useful_splits.push_back(tree_splits_[chunk]);
+ }
+ }
+ // Don't do anything if modular mode does not have any pixels in this image
+ if (useful_splits.empty()) return true;
+ useful_splits.push_back(tree_splits_.back());
+
+ std::atomic_flag invalid_force_wp = ATOMIC_FLAG_INIT;
+
+ std::vector<Tree> trees(useful_splits.size() - 1);
+ JXL_RETURN_IF_ERROR(RunOnPool(
+ pool, 0, useful_splits.size() - 1, ThreadPool::NoInit,
+ [&](const uint32_t chunk, size_t /* thread */) {
+ // TODO(veluca): parallelize more.
+ size_t total_pixels = 0;
+ uint32_t start = useful_splits[chunk];
+ uint32_t stop = useful_splits[chunk + 1];
+ while (start < stop && stream_images_[start].empty()) ++start;
+ while (start < stop && stream_images_[stop - 1].empty()) --stop;
+ if (stream_options_[start].tree_kind !=
+ ModularOptions::TreeKind::kLearn) {
+ for (size_t i = start; i < stop; i++) {
+ for (const Channel& ch : stream_images_[i].channel) {
+ total_pixels += ch.w * ch.h;
+ }
+ }
+ trees[chunk] =
+ PredefinedTree(stream_options_[start].tree_kind, total_pixels);
+ return;
+ }
+ TreeSamples tree_samples;
+ if (!tree_samples.SetPredictor(stream_options_[start].predictor,
+ stream_options_[start].wp_tree_mode)) {
+ invalid_force_wp.test_and_set(std::memory_order_acq_rel);
+ return;
+ }
+ if (!tree_samples.SetProperties(
+ stream_options_[start].splitting_heuristics_properties,
+ stream_options_[start].wp_tree_mode)) {
+ invalid_force_wp.test_and_set(std::memory_order_acq_rel);
+ return;
+ }
+ uint32_t max_c = 0;
+ std::vector<pixel_type> pixel_samples;
+ std::vector<pixel_type> diff_samples;
+ std::vector<uint32_t> group_pixel_count;
+ std::vector<uint32_t> channel_pixel_count;
+ for (size_t i = start; i < stop; i++) {
+ max_c = std::max<uint32_t>(stream_images_[i].channel.size(), max_c);
+ CollectPixelSamples(stream_images_[i], stream_options_[i], i,
+ group_pixel_count, channel_pixel_count,
+ pixel_samples, diff_samples);
+ }
+ StaticPropRange range;
+ range[0] = {{0, max_c}};
+ range[1] = {{start, stop}};
+ auto local_multiplier_info = multiplier_info;
+
+ tree_samples.PreQuantizeProperties(
+ range, local_multiplier_info, group_pixel_count,
+ channel_pixel_count, pixel_samples, diff_samples,
+ stream_options_[start].max_property_values);
+ for (size_t i = start; i < stop; i++) {
+ JXL_CHECK(ModularGenericCompress(
+ stream_images_[i], stream_options_[i], /*writer=*/nullptr,
+ /*aux_out=*/nullptr, 0, i, &tree_samples, &total_pixels));
+ }
+
+ // TODO(veluca): parallelize more.
+ trees[chunk] =
+ LearnTree(std::move(tree_samples), total_pixels,
+ stream_options_[start], local_multiplier_info, range);
+ },
+ "LearnTrees"));
+ if (invalid_force_wp.test_and_set(std::memory_order_acq_rel)) {
+ return JXL_FAILURE("PrepareEncoding: force_no_wp with {Weighted}");
+ }
+ tree_.clear();
+ MergeTrees(trees, useful_splits, 0, useful_splits.size() - 1, &tree_);
+ } else {
+ // Fixed tree.
+ size_t total_pixels = 0;
+ for (const Image& img : stream_images_) {
+ for (const Channel& ch : img.channel) {
+ total_pixels += ch.w * ch.h;
+ }
+ }
+ if (cparams_.speed_tier <= SpeedTier::kFalcon) {
+ tree_ =
+ PredefinedTree(ModularOptions::TreeKind::kWPFixedDC, total_pixels);
+ } else if (cparams_.speed_tier <= SpeedTier::kThunder) {
+ tree_ = PredefinedTree(ModularOptions::TreeKind::kGradientFixedDC,
+ total_pixels);
+ } else {
+ tree_ = {PropertyDecisionNode::Leaf(Predictor::Gradient)};
+ }
+ }
+ tree_tokens_.resize(1);
+ tree_tokens_[0].clear();
+ Tree decoded_tree;
+ TokenizeTree(tree_, &tree_tokens_[0], &decoded_tree);
+ JXL_ASSERT(tree_.size() == decoded_tree.size());
+ tree_ = std::move(decoded_tree);
+
+ /* TODO(szabadka) Add text output callback to cparams
+ if (kPrintTree && WantDebugOutput(aux_out)) {
+ if (frame_header.dc_level > 0) {
+ PrintTree(tree_, aux_out->debug_prefix + "/dc_frame_level" +
+ std::to_string(frame_header.dc_level) + "_tree");
+ } else {
+ PrintTree(tree_, aux_out->debug_prefix + "/global_tree");
+ }
+ } */
+ return true;
+}
+
+Status ModularFrameEncoder::ComputeTokens(ThreadPool* pool) {
+ size_t num_streams = stream_images_.size();
+ stream_headers_.resize(num_streams);
+ tokens_.resize(num_streams);
+ image_widths_.resize(num_streams);
+ JXL_RETURN_IF_ERROR(RunOnPool(
+ pool, 0, num_streams, ThreadPool::NoInit,
+ [&](const uint32_t stream_id, size_t /* thread */) {
+ AuxOut my_aux_out;
+ tokens_[stream_id].clear();
+ JXL_CHECK(ModularGenericCompress(
+ stream_images_[stream_id], stream_options_[stream_id],
+ /*writer=*/nullptr, &my_aux_out, 0, stream_id,
+ /*tree_samples=*/nullptr,
+ /*total_pixels=*/nullptr,
+ /*tree=*/&tree_, /*header=*/&stream_headers_[stream_id],
+ /*tokens=*/&tokens_[stream_id],
+ /*widths=*/&image_widths_[stream_id]));
+ },
+ "ComputeTokens"));
+ return true;
+}
+
+Status ModularFrameEncoder::EncodeGlobalInfo(bool streaming_mode,
+ BitWriter* writer,
+ AuxOut* aux_out) {
+ BitWriter::Allotment allotment(writer, 1);
+ // If we are using brotli, or not using modular mode.
+ if (tree_tokens_.empty() || tree_tokens_[0].empty()) {
+ writer->Write(1, 0);
+ allotment.ReclaimAndCharge(writer, kLayerModularTree, aux_out);
+ return true;
+ }
+ writer->Write(1, 1);
+ allotment.ReclaimAndCharge(writer, kLayerModularTree, aux_out);
+
+ // Write tree
+ HistogramParams params;
+ if (cparams_.speed_tier > SpeedTier::kKitten) {
+ params.clustering = HistogramParams::ClusteringType::kFast;
+ params.ans_histogram_strategy =
+ cparams_.speed_tier > SpeedTier::kThunder
+ ? HistogramParams::ANSHistogramStrategy::kFast
+ : HistogramParams::ANSHistogramStrategy::kApproximate;
+ params.lz77_method =
+ cparams_.decoding_speed_tier >= 3 && cparams_.modular_mode
+ ? (cparams_.speed_tier >= SpeedTier::kFalcon
+ ? HistogramParams::LZ77Method::kRLE
+ : HistogramParams::LZ77Method::kLZ77)
+ : HistogramParams::LZ77Method::kNone;
+ // Near-lossless DC, as well as modular mode, require choosing hybrid uint
+ // more carefully.
+ if ((!extra_dc_precision.empty() && extra_dc_precision[0] != 0) ||
+ (cparams_.modular_mode && cparams_.speed_tier < SpeedTier::kCheetah)) {
+ params.uint_method = HistogramParams::HybridUintMethod::kFast;
+ } else {
+ params.uint_method = HistogramParams::HybridUintMethod::kNone;
+ }
+ } else if (cparams_.speed_tier <= SpeedTier::kTortoise) {
+ params.lz77_method = HistogramParams::LZ77Method::kOptimal;
+ } else {
+ params.lz77_method = HistogramParams::LZ77Method::kLZ77;
+ }
+ if (cparams_.decoding_speed_tier >= 1) {
+ params.max_histograms = 12;
+ }
+ if (cparams_.decoding_speed_tier >= 1 && cparams_.responsive) {
+ params.lz77_method = cparams_.speed_tier >= SpeedTier::kCheetah
+ ? HistogramParams::LZ77Method::kRLE
+ : cparams_.speed_tier >= SpeedTier::kKitten
+ ? HistogramParams::LZ77Method::kLZ77
+ : HistogramParams::LZ77Method::kOptimal;
+ }
+ if (cparams_.decoding_speed_tier >= 2 && cparams_.responsive) {
+ params.uint_method = HistogramParams::HybridUintMethod::k000;
+ params.force_huffman = true;
+ }
+ {
+ EntropyEncodingData tree_code;
+ std::vector<uint8_t> tree_context_map;
+ BuildAndEncodeHistograms(params, kNumTreeContexts, tree_tokens_, &tree_code,
+ &tree_context_map, writer, kLayerModularTree,
+ aux_out);
+ WriteTokens(tree_tokens_[0], tree_code, tree_context_map, 0, writer,
+ kLayerModularTree, aux_out);
+ }
+ params.streaming_mode = streaming_mode;
+ params.add_missing_symbols = streaming_mode;
+ params.image_widths = image_widths_;
+ // Write histograms.
+ BuildAndEncodeHistograms(params, (tree_.size() + 1) / 2, tokens_, &code_,
+ &context_map_, writer, kLayerModularGlobal, aux_out);
+ return true;
+}
+
+Status ModularFrameEncoder::EncodeStream(BitWriter* writer, AuxOut* aux_out,
+ size_t layer,
+ const ModularStreamId& stream) {
+ size_t stream_id = stream.ID(frame_dim_);
+ if (stream_images_[stream_id].channel.empty()) {
+ return true; // Image with no channels, header never gets decoded.
+ }
+ if (tokens_.empty()) {
+ JXL_RETURN_IF_ERROR(ModularGenericCompress(
+ stream_images_[stream_id], stream_options_[stream_id], writer, aux_out,
+ layer, stream_id));
+ } else {
+ JXL_RETURN_IF_ERROR(
+ Bundle::Write(stream_headers_[stream_id], writer, layer, aux_out));
+ WriteTokens(tokens_[stream_id], code_, context_map_, 0, writer, layer,
+ aux_out);
+ }
+ return true;
+}
+
+void ModularFrameEncoder::ClearStreamData(const ModularStreamId& stream) {
+ size_t stream_id = stream.ID(frame_dim_);
+ Image empty_image;
+ std::swap(stream_images_[stream_id], empty_image);
+}
+
+namespace {
+float EstimateWPCost(const Image& img, size_t i) {
+ size_t extra_bits = 0;
+ float histo_cost = 0;
+ HybridUintConfig config;
+ int32_t cutoffs[] = {-500, -392, -255, -191, -127, -95, -63, -47, -31,
+ -23, -15, -11, -7, -4, -3, -1, 0, 1,
+ 3, 5, 7, 11, 15, 23, 31, 47, 63,
+ 95, 127, 191, 255, 392, 500};
+ constexpr size_t nc = sizeof(cutoffs) / sizeof(*cutoffs) + 1;
+ Histogram histo[nc] = {};
+ weighted::Header wp_header;
+ PredictorMode(i, &wp_header);
+ for (const Channel& ch : img.channel) {
+ const intptr_t onerow = ch.plane.PixelsPerRow();
+ weighted::State wp_state(wp_header, ch.w, ch.h);
+ Properties properties(1);
+ for (size_t y = 0; y < ch.h; y++) {
+ const pixel_type* JXL_RESTRICT r = ch.Row(y);
+ for (size_t x = 0; x < ch.w; x++) {
+ size_t offset = 0;
+ pixel_type_w left = (x ? r[x - 1] : y ? *(r + x - onerow) : 0);
+ pixel_type_w top = (y ? *(r + x - onerow) : left);
+ pixel_type_w topleft = (x && y ? *(r + x - 1 - onerow) : left);
+ pixel_type_w topright =
+ (x + 1 < ch.w && y ? *(r + x + 1 - onerow) : top);
+ pixel_type_w toptop = (y > 1 ? *(r + x - onerow - onerow) : top);
+ pixel_type guess = wp_state.Predict</*compute_properties=*/true>(
+ x, y, ch.w, top, left, topright, topleft, toptop, &properties,
+ offset);
+ size_t ctx = 0;
+ for (int c : cutoffs) {
+ ctx += c >= properties[0];
+ }
+ pixel_type res = r[x] - guess;
+ uint32_t token, nbits, bits;
+ config.Encode(PackSigned(res), &token, &nbits, &bits);
+ histo[ctx].Add(token);
+ extra_bits += nbits;
+ wp_state.UpdateErrors(r[x], x, y, ch.w);
+ }
+ }
+ for (size_t h = 0; h < nc; h++) {
+ histo_cost += histo[h].ShannonEntropy();
+ histo[h].Clear();
+ }
+ }
+ return histo_cost + extra_bits;
+}
+
+float EstimateCost(const Image& img) {
+ // TODO(veluca): consider SIMDfication of this code.
+ size_t extra_bits = 0;
+ float histo_cost = 0;
+ HybridUintConfig config;
+ uint32_t cutoffs[] = {0, 1, 3, 5, 7, 11, 15, 23, 31,
+ 47, 63, 95, 127, 191, 255, 392, 500};
+ constexpr size_t nc = sizeof(cutoffs) / sizeof(*cutoffs) + 1;
+ Histogram histo[nc] = {};
+ for (const Channel& ch : img.channel) {
+ const intptr_t onerow = ch.plane.PixelsPerRow();
+ for (size_t y = 0; y < ch.h; y++) {
+ const pixel_type* JXL_RESTRICT r = ch.Row(y);
+ for (size_t x = 0; x < ch.w; x++) {
+ pixel_type_w left = (x ? r[x - 1] : y ? *(r + x - onerow) : 0);
+ pixel_type_w top = (y ? *(r + x - onerow) : left);
+ pixel_type_w topleft = (x && y ? *(r + x - 1 - onerow) : left);
+ size_t maxdiff = std::max(std::max(left, top), topleft) -
+ std::min(std::min(left, top), topleft);
+ size_t ctx = 0;
+ for (uint32_t c : cutoffs) {
+ ctx += c > maxdiff;
+ }
+ pixel_type res = r[x] - ClampedGradient(top, left, topleft);
+ uint32_t token, nbits, bits;
+ config.Encode(PackSigned(res), &token, &nbits, &bits);
+ histo[ctx].Add(token);
+ extra_bits += nbits;
+ }
+ }
+ for (size_t h = 0; h < nc; h++) {
+ histo_cost += histo[h].ShannonEntropy();
+ histo[h].Clear();
+ }
+ }
+ return histo_cost + extra_bits;
+}
+
+} // namespace
+
+Status ModularFrameEncoder::PrepareStreamParams(const Rect& rect,
+ const CompressParams& cparams_,
+ int minShift, int maxShift,
+ const ModularStreamId& stream,
+ bool do_color) {
+ size_t stream_id = stream.ID(frame_dim_);
+ Image& full_image = stream_images_[0];
+ const size_t xsize = rect.xsize();
+ const size_t ysize = rect.ysize();
+ Image& gi = stream_images_[stream_id];
+ if (stream_id > 0) {
+ gi = Image(xsize, ysize, full_image.bitdepth, 0);
+ // start at the first bigger-than-frame_dim.group_dim non-metachannel
+ size_t c = full_image.nb_meta_channels;
+ for (; c < full_image.channel.size(); c++) {
+ Channel& fc = full_image.channel[c];
+ if (fc.w > frame_dim_.group_dim || fc.h > frame_dim_.group_dim) break;
+ }
+ for (; c < full_image.channel.size(); c++) {
+ Channel& fc = full_image.channel[c];
+ int shift = std::min(fc.hshift, fc.vshift);
+ if (shift > maxShift) continue;
+ if (shift < minShift) continue;
+ Rect r(rect.x0() >> fc.hshift, rect.y0() >> fc.vshift,
+ rect.xsize() >> fc.hshift, rect.ysize() >> fc.vshift, fc.w, fc.h);
+ if (r.xsize() == 0 || r.ysize() == 0) continue;
+ gi_channel_[stream_id].push_back(c);
+ Channel gc(r.xsize(), r.ysize());
+ gc.hshift = fc.hshift;
+ gc.vshift = fc.vshift;
+ for (size_t y = 0; y < r.ysize(); ++y) {
+ memcpy(gc.Row(y), r.ConstRow(fc.plane, y),
+ r.xsize() * sizeof(pixel_type));
+ }
+ gi.channel.emplace_back(std::move(gc));
+ }
+
+ if (gi.channel.empty()) return true;
+ // Do some per-group transforms
+
+ // Local palette
+ // TODO(veluca): make this work with quantize-after-prediction in lossy
+ // mode.
+ if (cparams_.butteraugli_distance == 0.f && cparams_.palette_colors != 0 &&
+ cparams_.speed_tier < SpeedTier::kCheetah) {
+ // all-channel palette (e.g. RGBA)
+ if (gi.channel.size() - gi.nb_meta_channels > 1) {
+ Transform maybe_palette(TransformId::kPalette);
+ maybe_palette.begin_c = gi.nb_meta_channels;
+ maybe_palette.num_c = gi.channel.size() - gi.nb_meta_channels;
+ maybe_palette.nb_colors = std::abs(cparams_.palette_colors);
+ maybe_palette.ordered_palette = cparams_.palette_colors >= 0;
+ do_transform(gi, maybe_palette, weighted::Header());
+ }
+ // all-minus-one-channel palette (RGB with separate alpha, or CMY with
+ // separate K)
+ if (gi.channel.size() - gi.nb_meta_channels > 3) {
+ Transform maybe_palette_3(TransformId::kPalette);
+ maybe_palette_3.begin_c = gi.nb_meta_channels;
+ maybe_palette_3.num_c = gi.channel.size() - gi.nb_meta_channels - 1;
+ maybe_palette_3.nb_colors = std::abs(cparams_.palette_colors);
+ maybe_palette_3.ordered_palette = cparams_.palette_colors >= 0;
+ maybe_palette_3.lossy_palette = cparams_.lossy_palette;
+ if (maybe_palette_3.lossy_palette) {
+ maybe_palette_3.predictor = Predictor::Weighted;
+ }
+ do_transform(gi, maybe_palette_3, weighted::Header());
+ }
+ }
+
+ // Local channel palette
+ if (cparams_.channel_colors_percent > 0 &&
+ cparams_.butteraugli_distance == 0.f && !cparams_.lossy_palette &&
+ cparams_.speed_tier < SpeedTier::kCheetah &&
+ !(cparams_.responsive && cparams_.decoding_speed_tier >= 1)) {
+ // single channel palette (like FLIF's ChannelCompact)
+ size_t nb_channels = gi.channel.size() - gi.nb_meta_channels;
+ for (size_t i = 0; i < nb_channels; i++) {
+ int32_t min, max;
+ compute_minmax(gi.channel[gi.nb_meta_channels + i], &min, &max);
+ int64_t colors = (int64_t)max - min + 1;
+ JXL_DEBUG_V(10, "Channel %" PRIuS ": range=%i..%i", i, min, max);
+ Transform maybe_palette_1(TransformId::kPalette);
+ maybe_palette_1.begin_c = i + gi.nb_meta_channels;
+ maybe_palette_1.num_c = 1;
+ // simple heuristic: if less than X percent of the values in the range
+ // actually occur, it is probably worth it to do a compaction
+ // (but only if the channel palette is less than 80% the size of the
+ // image itself)
+ maybe_palette_1.nb_colors =
+ std::min((int)(xsize * ysize * 0.8),
+ (int)(cparams_.channel_colors_percent / 100. * colors));
+ do_transform(gi, maybe_palette_1, weighted::Header());
+ }
+ }
+ }
+
+ // lossless and no specific color transform specified: try Nothing, YCoCg,
+ // and 17 RCTs
+ if (cparams_.color_transform == ColorTransform::kNone &&
+ cparams_.IsLossless() && cparams_.colorspace < 0 &&
+ gi.channel.size() - gi.nb_meta_channels >= 3 &&
+ cparams_.responsive == false && do_color &&
+ cparams_.speed_tier <= SpeedTier::kHare) {
+ Transform sg(TransformId::kRCT);
+ sg.begin_c = gi.nb_meta_channels;
+ size_t nb_rcts_to_try = 0;
+ switch (cparams_.speed_tier) {
+ case SpeedTier::kLightning:
+ case SpeedTier::kThunder:
+ case SpeedTier::kFalcon:
+ case SpeedTier::kCheetah:
+ nb_rcts_to_try = 0; // Just do global YCoCg
+ break;
+ case SpeedTier::kHare:
+ nb_rcts_to_try = 4;
+ break;
+ case SpeedTier::kWombat:
+ nb_rcts_to_try = 5;
+ break;
+ case SpeedTier::kSquirrel:
+ nb_rcts_to_try = 7;
+ break;
+ case SpeedTier::kKitten:
+ nb_rcts_to_try = 9;
+ break;
+ case SpeedTier::kGlacier:
+ case SpeedTier::kTortoise:
+ nb_rcts_to_try = 19;
+ break;
+ }
+ float best_cost = std::numeric_limits<float>::max();
+ size_t best_rct = 0;
+ // These should be 19 actually different transforms; the remaining ones
+ // are equivalent to one of these (note that the first two are do-nothing
+ // and YCoCg) modulo channel reordering (which only matters in the case of
+ // MA-with-prev-channels-properties) and/or sign (e.g. RmG vs GmR)
+ for (int i : {0 * 7 + 0, 0 * 7 + 6, 0 * 7 + 5, 1 * 7 + 3, 3 * 7 + 5,
+ 5 * 7 + 5, 1 * 7 + 5, 2 * 7 + 5, 1 * 7 + 1, 0 * 7 + 4,
+ 1 * 7 + 2, 2 * 7 + 1, 2 * 7 + 2, 2 * 7 + 3, 4 * 7 + 4,
+ 4 * 7 + 5, 0 * 7 + 2, 0 * 7 + 1, 0 * 7 + 3}) {
+ if (nb_rcts_to_try == 0) break;
+ sg.rct_type = i;
+ nb_rcts_to_try--;
+ if (do_transform(gi, sg, weighted::Header())) {
+ float cost = EstimateCost(gi);
+ if (cost < best_cost) {
+ best_rct = i;
+ best_cost = cost;
+ }
+ Transform t = gi.transform.back();
+ JXL_RETURN_IF_ERROR(t.Inverse(gi, weighted::Header(), nullptr));
+ gi.transform.pop_back();
+ }
+ }
+ // Apply the best RCT to the image for future encoding.
+ sg.rct_type = best_rct;
+ do_transform(gi, sg, weighted::Header());
+ } else {
+ // No need to try anything, just use the default options.
+ }
+ size_t nb_wp_modes = 1;
+ if (cparams_.speed_tier <= SpeedTier::kTortoise) {
+ nb_wp_modes = 5;
+ } else if (cparams_.speed_tier <= SpeedTier::kKitten) {
+ nb_wp_modes = 2;
+ }
+ if (nb_wp_modes > 1 &&
+ (stream_options_[stream_id].predictor == Predictor::Weighted ||
+ stream_options_[stream_id].predictor == Predictor::Best ||
+ stream_options_[stream_id].predictor == Predictor::Variable)) {
+ float best_cost = std::numeric_limits<float>::max();
+ stream_options_[stream_id].wp_mode = 0;
+ for (size_t i = 0; i < nb_wp_modes; i++) {
+ float cost = EstimateWPCost(gi, i);
+ if (cost < best_cost) {
+ best_cost = cost;
+ stream_options_[stream_id].wp_mode = i;
+ }
+ }
+ }
+ return true;
+}
+
+constexpr float q_deadzone = 0.62f;
+int QuantizeWP(const int32_t* qrow, size_t onerow, size_t c, size_t x, size_t y,
+ size_t w, weighted::State* wp_state, float value,
+ float inv_factor) {
+ float svalue = value * inv_factor;
+ PredictionResult pred =
+ PredictNoTreeWP(w, qrow + x, onerow, x, y, Predictor::Weighted, wp_state);
+ svalue -= pred.guess;
+ if (svalue > -q_deadzone && svalue < q_deadzone) svalue = 0;
+ int residual = roundf(svalue);
+ if (residual > 2 || residual < -2) residual = roundf(svalue * 0.5) * 2;
+ return residual + pred.guess;
+}
+
+int QuantizeGradient(const int32_t* qrow, size_t onerow, size_t c, size_t x,
+ size_t y, size_t w, float value, float inv_factor) {
+ float svalue = value * inv_factor;
+ PredictionResult pred =
+ PredictNoTreeNoWP(w, qrow + x, onerow, x, y, Predictor::Gradient);
+ svalue -= pred.guess;
+ if (svalue > -q_deadzone && svalue < q_deadzone) svalue = 0;
+ int residual = roundf(svalue);
+ if (residual > 2 || residual < -2) residual = roundf(svalue * 0.5) * 2;
+ return residual + pred.guess;
+}
+
+void ModularFrameEncoder::AddVarDCTDC(const FrameHeader& frame_header,
+ const Image3F& dc, const Rect& r,
+ size_t group_index, bool nl_dc,
+ PassesEncoderState* enc_state,
+ bool jpeg_transcode) {
+ extra_dc_precision[group_index] = nl_dc ? 1 : 0;
+ float mul = 1 << extra_dc_precision[group_index];
+
+ size_t stream_id = ModularStreamId::VarDCTDC(group_index).ID(frame_dim_);
+ stream_options_[stream_id].max_chan_size = 0xFFFFFF;
+ stream_options_[stream_id].predictor = Predictor::Weighted;
+ stream_options_[stream_id].wp_tree_mode = ModularOptions::TreeMode::kWPOnly;
+ if (cparams_.speed_tier >= SpeedTier::kSquirrel) {
+ stream_options_[stream_id].tree_kind = ModularOptions::TreeKind::kWPFixedDC;
+ }
+ if (cparams_.speed_tier < SpeedTier::kSquirrel && !nl_dc) {
+ stream_options_[stream_id].predictor =
+ (cparams_.speed_tier < SpeedTier::kKitten ? Predictor::Variable
+ : Predictor::Best);
+ stream_options_[stream_id].wp_tree_mode =
+ ModularOptions::TreeMode::kDefault;
+ stream_options_[stream_id].tree_kind = ModularOptions::TreeKind::kLearn;
+ }
+ if (cparams_.decoding_speed_tier >= 1) {
+ stream_options_[stream_id].tree_kind =
+ ModularOptions::TreeKind::kGradientFixedDC;
+ }
+
+ stream_images_[stream_id] = Image(r.xsize(), r.ysize(), 8, 3);
+ if (nl_dc && stream_options_[stream_id].tree_kind ==
+ ModularOptions::TreeKind::kGradientFixedDC) {
+ JXL_ASSERT(frame_header.chroma_subsampling.Is444());
+ for (size_t c : {1, 0, 2}) {
+ float inv_factor = enc_state->shared.quantizer.GetInvDcStep(c) * mul;
+ float y_factor = enc_state->shared.quantizer.GetDcStep(1) / mul;
+ float cfl_factor = enc_state->shared.cmap.DCFactors()[c];
+ for (size_t y = 0; y < r.ysize(); y++) {
+ int32_t* quant_row =
+ stream_images_[stream_id].channel[c < 2 ? c ^ 1 : c].plane.Row(y);
+ size_t stride = stream_images_[stream_id]
+ .channel[c < 2 ? c ^ 1 : c]
+ .plane.PixelsPerRow();
+ const float* row = r.ConstPlaneRow(dc, c, y);
+ if (c == 1) {
+ for (size_t x = 0; x < r.xsize(); x++) {
+ quant_row[x] = QuantizeGradient(quant_row, stride, c, x, y,
+ r.xsize(), row[x], inv_factor);
+ }
+ } else {
+ int32_t* quant_row_y =
+ stream_images_[stream_id].channel[0].plane.Row(y);
+ for (size_t x = 0; x < r.xsize(); x++) {
+ quant_row[x] = QuantizeGradient(
+ quant_row, stride, c, x, y, r.xsize(),
+ row[x] - quant_row_y[x] * (y_factor * cfl_factor), inv_factor);
+ }
+ }
+ }
+ }
+ } else if (nl_dc) {
+ JXL_ASSERT(frame_header.chroma_subsampling.Is444());
+ for (size_t c : {1, 0, 2}) {
+ float inv_factor = enc_state->shared.quantizer.GetInvDcStep(c) * mul;
+ float y_factor = enc_state->shared.quantizer.GetDcStep(1) / mul;
+ float cfl_factor = enc_state->shared.cmap.DCFactors()[c];
+ weighted::Header header;
+ weighted::State wp_state(header, r.xsize(), r.ysize());
+ for (size_t y = 0; y < r.ysize(); y++) {
+ int32_t* quant_row =
+ stream_images_[stream_id].channel[c < 2 ? c ^ 1 : c].plane.Row(y);
+ size_t stride = stream_images_[stream_id]
+ .channel[c < 2 ? c ^ 1 : c]
+ .plane.PixelsPerRow();
+ const float* row = r.ConstPlaneRow(dc, c, y);
+ if (c == 1) {
+ for (size_t x = 0; x < r.xsize(); x++) {
+ quant_row[x] = QuantizeWP(quant_row, stride, c, x, y, r.xsize(),
+ &wp_state, row[x], inv_factor);
+ wp_state.UpdateErrors(quant_row[x], x, y, r.xsize());
+ }
+ } else {
+ int32_t* quant_row_y =
+ stream_images_[stream_id].channel[0].plane.Row(y);
+ for (size_t x = 0; x < r.xsize(); x++) {
+ quant_row[x] = QuantizeWP(
+ quant_row, stride, c, x, y, r.xsize(), &wp_state,
+ row[x] - quant_row_y[x] * (y_factor * cfl_factor), inv_factor);
+ wp_state.UpdateErrors(quant_row[x], x, y, r.xsize());
+ }
+ }
+ }
+ }
+ } else if (frame_header.chroma_subsampling.Is444()) {
+ for (size_t c : {1, 0, 2}) {
+ float inv_factor = enc_state->shared.quantizer.GetInvDcStep(c) * mul;
+ float y_factor = enc_state->shared.quantizer.GetDcStep(1) / mul;
+ float cfl_factor = enc_state->shared.cmap.DCFactors()[c];
+ for (size_t y = 0; y < r.ysize(); y++) {
+ int32_t* quant_row =
+ stream_images_[stream_id].channel[c < 2 ? c ^ 1 : c].plane.Row(y);
+ const float* row = r.ConstPlaneRow(dc, c, y);
+ if (c == 1) {
+ for (size_t x = 0; x < r.xsize(); x++) {
+ quant_row[x] = roundf(row[x] * inv_factor);
+ }
+ } else {
+ int32_t* quant_row_y =
+ stream_images_[stream_id].channel[0].plane.Row(y);
+ for (size_t x = 0; x < r.xsize(); x++) {
+ quant_row[x] =
+ roundf((row[x] - quant_row_y[x] * (y_factor * cfl_factor)) *
+ inv_factor);
+ }
+ }
+ }
+ }
+ } else {
+ for (size_t c : {1, 0, 2}) {
+ Rect rect(r.x0() >> frame_header.chroma_subsampling.HShift(c),
+ r.y0() >> frame_header.chroma_subsampling.VShift(c),
+ r.xsize() >> frame_header.chroma_subsampling.HShift(c),
+ r.ysize() >> frame_header.chroma_subsampling.VShift(c));
+ float inv_factor = enc_state->shared.quantizer.GetInvDcStep(c) * mul;
+ size_t ys = rect.ysize();
+ size_t xs = rect.xsize();
+ Channel& ch = stream_images_[stream_id].channel[c < 2 ? c ^ 1 : c];
+ ch.w = xs;
+ ch.h = ys;
+ ch.shrink();
+ for (size_t y = 0; y < ys; y++) {
+ int32_t* quant_row = ch.plane.Row(y);
+ const float* row = rect.ConstPlaneRow(dc, c, y);
+ for (size_t x = 0; x < xs; x++) {
+ quant_row[x] = roundf(row[x] * inv_factor);
+ }
+ }
+ }
+ }
+
+ DequantDC(r, &enc_state->shared.dc_storage, &enc_state->shared.quant_dc,
+ stream_images_[stream_id], enc_state->shared.quantizer.MulDC(),
+ 1.0 / mul, enc_state->shared.cmap.DCFactors(),
+ frame_header.chroma_subsampling, enc_state->shared.block_ctx_map);
+}
+
+void ModularFrameEncoder::AddACMetadata(const Rect& r, size_t group_index,
+ bool jpeg_transcode,
+ PassesEncoderState* enc_state) {
+ size_t stream_id = ModularStreamId::ACMetadata(group_index).ID(frame_dim_);
+ stream_options_[stream_id].max_chan_size = 0xFFFFFF;
+ stream_options_[stream_id].wp_tree_mode = ModularOptions::TreeMode::kNoWP;
+ if (jpeg_transcode) {
+ stream_options_[stream_id].tree_kind =
+ ModularOptions::TreeKind::kJpegTranscodeACMeta;
+ } else if (cparams_.speed_tier >= SpeedTier::kFalcon) {
+ stream_options_[stream_id].tree_kind =
+ ModularOptions::TreeKind::kFalconACMeta;
+ } else if (cparams_.speed_tier > SpeedTier::kKitten) {
+ stream_options_[stream_id].tree_kind = ModularOptions::TreeKind::kACMeta;
+ }
+ // If we are using a non-constant CfL field, and are in a slow enough mode,
+ // re-enable tree computation for it.
+ if (cparams_.speed_tier < SpeedTier::kSquirrel &&
+ cparams_.force_cfl_jpeg_recompression) {
+ stream_options_[stream_id].tree_kind = ModularOptions::TreeKind::kLearn;
+ }
+ // YToX, YToB, ACS + QF, EPF
+ Image& image = stream_images_[stream_id];
+ image = Image(r.xsize(), r.ysize(), 8, 4);
+ static_assert(kColorTileDimInBlocks == 8, "Color tile size changed");
+ Rect cr(r.x0() >> 3, r.y0() >> 3, (r.xsize() + 7) >> 3, (r.ysize() + 7) >> 3);
+ image.channel[0] = Channel(cr.xsize(), cr.ysize(), 3, 3);
+ image.channel[1] = Channel(cr.xsize(), cr.ysize(), 3, 3);
+ image.channel[2] = Channel(r.xsize() * r.ysize(), 2, 0, 0);
+ ConvertPlaneAndClamp(cr, enc_state->shared.cmap.ytox_map,
+ Rect(image.channel[0].plane), &image.channel[0].plane);
+ ConvertPlaneAndClamp(cr, enc_state->shared.cmap.ytob_map,
+ Rect(image.channel[1].plane), &image.channel[1].plane);
+ size_t num = 0;
+ for (size_t y = 0; y < r.ysize(); y++) {
+ AcStrategyRow row_acs = enc_state->shared.ac_strategy.ConstRow(r, y);
+ const int32_t* row_qf = r.ConstRow(enc_state->shared.raw_quant_field, y);
+ const uint8_t* row_epf = r.ConstRow(enc_state->shared.epf_sharpness, y);
+ int32_t* out_acs = image.channel[2].plane.Row(0);
+ int32_t* out_qf = image.channel[2].plane.Row(1);
+ int32_t* row_out_epf = image.channel[3].plane.Row(y);
+ for (size_t x = 0; x < r.xsize(); x++) {
+ row_out_epf[x] = row_epf[x];
+ if (!row_acs[x].IsFirstBlock()) continue;
+ out_acs[num] = row_acs[x].RawStrategy();
+ out_qf[num] = row_qf[x] - 1;
+ num++;
+ }
+ }
+ image.channel[2].w = num;
+ ac_metadata_size[group_index] = num;
+}
+
+void ModularFrameEncoder::EncodeQuantTable(
+ size_t size_x, size_t size_y, BitWriter* writer,
+ const QuantEncoding& encoding, size_t idx,
+ ModularFrameEncoder* modular_frame_encoder) {
+ JXL_ASSERT(encoding.qraw.qtable != nullptr);
+ JXL_ASSERT(size_x * size_y * 3 == encoding.qraw.qtable->size());
+ JXL_CHECK(F16Coder::Write(encoding.qraw.qtable_den, writer));
+ if (modular_frame_encoder) {
+ JXL_CHECK(modular_frame_encoder->EncodeStream(
+ writer, nullptr, 0, ModularStreamId::QuantTable(idx)));
+ return;
+ }
+ Image image(size_x, size_y, 8, 3);
+ for (size_t c = 0; c < 3; c++) {
+ for (size_t y = 0; y < size_y; y++) {
+ int32_t* JXL_RESTRICT row = image.channel[c].Row(y);
+ for (size_t x = 0; x < size_x; x++) {
+ row[x] = (*encoding.qraw.qtable)[c * size_x * size_y + y * size_x + x];
+ }
+ }
+ }
+ ModularOptions cfopts;
+ JXL_CHECK(ModularGenericCompress(image, cfopts, writer));
+}
+
+void ModularFrameEncoder::AddQuantTable(size_t size_x, size_t size_y,
+ const QuantEncoding& encoding,
+ size_t idx) {
+ size_t stream_id = ModularStreamId::QuantTable(idx).ID(frame_dim_);
+ JXL_ASSERT(encoding.qraw.qtable != nullptr);
+ JXL_ASSERT(size_x * size_y * 3 == encoding.qraw.qtable->size());
+ Image& image = stream_images_[stream_id];
+ image = Image(size_x, size_y, 8, 3);
+ for (size_t c = 0; c < 3; c++) {
+ for (size_t y = 0; y < size_y; y++) {
+ int32_t* JXL_RESTRICT row = image.channel[c].Row(y);
+ for (size_t x = 0; x < size_x; x++) {
+ row[x] = (*encoding.qraw.qtable)[c * size_x * size_y + y * size_x + x];
+ }
+ }
+ }
+}
+} // namespace jxl