diff options
Diffstat (limited to 'third_party/jpeg-xl/lib/jxl/enc_modular.cc')
| -rw-r--r-- | third_party/jpeg-xl/lib/jxl/enc_modular.cc | 631 |
1 files changed, 366 insertions, 265 deletions
diff --git a/third_party/jpeg-xl/lib/jxl/enc_modular.cc b/third_party/jpeg-xl/lib/jxl/enc_modular.cc index b8366953b7..dbd62d4a01 100644 --- a/third_party/jpeg-xl/lib/jxl/enc_modular.cc +++ b/third_party/jpeg-xl/lib/jxl/enc_modular.cc @@ -10,8 +10,8 @@ #include <array> #include <atomic> +#include <cstdint> #include <limits> -#include <queue> #include <utility> #include <vector> @@ -28,9 +28,9 @@ #include "lib/jxl/enc_params.h" #include "lib/jxl/enc_patch_dictionary.h" #include "lib/jxl/enc_quant_weights.h" +#include "lib/jxl/frame_dimensions.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" @@ -38,7 +38,7 @@ #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" +#include "modular/options.h" namespace jxl { @@ -48,15 +48,15 @@ namespace { // 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 = +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 = +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] = { +const float squeeze_quality_factor_xyb = 2.4f; +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, @@ -65,12 +65,12 @@ static const float squeeze_xyb_qtable[3][16] = { 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}; +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] = { +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 @@ -139,10 +139,12 @@ Status float_to_int(const float* const row_in, pixel_type* const row_out, } if (bits == 32 && fp) { JXL_ASSERT(exp_bits == 8); - memcpy((void*)row_out, (const void*)row_in, 4 * xsize); + memcpy(static_cast<void*>(row_out), static_cast<const void*>(row_in), + 4 * xsize); return true; } + JXL_ASSERT(bits > 0); int exp_bias = (1 << (exp_bits - 1)) - 1; int max_exp = (1 << exp_bits) - 1; uint32_t sign = (1u << (bits - 1)); @@ -186,14 +188,144 @@ Status float_to_int(const float* const row_in, pixel_type* const row_out, f = (signbit ? sign : 0); f |= (exp << mant_bits); f |= mantissa; - row_out[x] = (pixel_type)f; + row_out[x] = static_cast<pixel_type>(f); } return true; } + +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]) ? 1 : 0; + } + pixel_type res = r[x] - guess; + uint32_t token; + uint32_t nbits; + uint32_t 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 (auto& h : histo) { + histo_cost += h.ShannonEntropy(); + 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) ? 1 : 0; + } + pixel_type res = r[x] - ClampedGradient(top, left, topleft); + uint32_t token; + uint32_t nbits; + uint32_t bits; + config.Encode(PackSigned(res), &token, &nbits, &bits); + histo[ctx].Add(token); + extra_bits += nbits; + } + } + for (auto& h : histo) { + histo_cost += h.ShannonEntropy(); + h.Clear(); + } + } + return histo_cost + extra_bits; +} + +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; +} + +bool maybe_do_transform(Image& image, const Transform& tr, + const CompressParams& cparams, + const weighted::Header& wp_header, + jxl::ThreadPool* pool = nullptr, + bool force_jxlart = false) { + if (force_jxlart || cparams.speed_tier >= SpeedTier::kSquirrel) { + return do_transform(image, tr, wp_header, pool, force_jxlart); + } + float cost_before = EstimateCost(image); + bool did_it = do_transform(image, tr, wp_header, pool); + if (did_it) { + float cost_after = EstimateCost(image); + JXL_DEBUG_V(7, "Cost before: %f cost after: %f", cost_before, cost_after); + if (cost_after > cost_before) { + Transform t = image.transform.back(); + JXL_RETURN_IF_ERROR(t.Inverse(image, wp_header, pool)); + image.transform.pop_back(); + did_it = false; + } + } + return did_it; +} + } // namespace ModularFrameEncoder::ModularFrameEncoder(const FrameHeader& frame_header, - const CompressParams& cparams_orig) + const CompressParams& cparams_orig, + bool streaming_mode) : frame_dim_(frame_header.ToFrameDimensions()), cparams_(cparams_orig) { size_t num_streams = ModularStreamId::Num(frame_dim_, frame_header.passes.num_passes); @@ -253,10 +385,16 @@ ModularFrameEncoder::ModularFrameEncoder(const FrameHeader& frame_header, // 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) { + if (num_streams < 30 && cparams_.speed_tier > SpeedTier::kTortoise && + cparams_orig.ModularPartIsLossless()) { prop_order.erase(prop_order.begin() + 1); } } + int max_properties = std::min<int>( + cparams_.options.max_properties, + static_cast<int>( + frame_header.nonserialized_metadata->m.num_extra_channels) + + (frame_header.encoding == FrameEncoding::kModular ? 2 : -1)); switch (cparams_.speed_tier) { case SpeedTier::kHare: cparams_.options.splitting_heuristics_properties.assign( @@ -278,6 +416,7 @@ ModularFrameEncoder::ModularFrameEncoder(const FrameHeader& frame_header, prop_order.begin(), prop_order.begin() + 10); cparams_.options.max_property_values = 96; break; + case SpeedTier::kGlacier: case SpeedTier::kTortoise: cparams_.options.splitting_heuristics_properties = prop_order; cparams_.options.max_property_values = 256; @@ -290,24 +429,36 @@ ModularFrameEncoder::ModularFrameEncoder(const FrameHeader& frame_header, } if (cparams_.speed_tier > SpeedTier::kTortoise) { // Gradient in previous channels. - for (int i = 0; i < cparams_.options.max_properties; i++) { + for (int i = 0; i < 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++) { + for (int i = 0; i < max_properties * 4; i++) { cparams_.options.splitting_heuristics_properties.push_back( kNumNonrefProperties + i); } } } - if (cparams_.options.predictor == static_cast<Predictor>(-1)) { + if ((cparams_.options.predictor == Predictor::Average0 || + cparams_.options.predictor == Predictor::Average1 || + cparams_.options.predictor == Predictor::Average2 || + cparams_.options.predictor == Predictor::Average3 || + cparams_.options.predictor == Predictor::Average4 || + cparams_.options.predictor == Predictor::Weighted) && + !cparams_.ModularPartIsLossless()) { + // Lossy + Average/Weighted predictors does not work, so switch to default + // predictors. + cparams_.options.predictor = kUndefinedPredictor; + } + + if (cparams_.options.predictor == kUndefinedPredictor) { // no explicit predictor(s) given, set a good default - if ((cparams_.speed_tier <= SpeedTier::kTortoise || + if ((cparams_.speed_tier <= SpeedTier::kGlacier || cparams_.modular_mode == false) && - cparams_.IsLossless() && cparams_.responsive == false) { + cparams_.IsLossless() && cparams_.responsive == JXL_FALSE) { // TODO(veluca): allow all predictors that don't break residual // multipliers in lossy mode. cparams_.options.predictor = Predictor::Variable; @@ -354,48 +505,54 @@ ModularFrameEncoder::ModularFrameEncoder(const FrameHeader& frame_header, // 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); + stream_options_[0] = cparams_.options; + if (cparams_.speed_tier == SpeedTier::kFalcon) { + stream_options_[0].tree_kind = ModularOptions::TreeKind::kWPFixedDC; + } else if (cparams_.speed_tier == SpeedTier::kThunder) { + stream_options_[0].tree_kind = ModularOptions::TreeKind::kGradientFixedDC; } - if (did_it) image.transform.push_back(t); - return did_it; + stream_options_[0].histogram_params = + HistogramParams::ForModular(cparams_, {}, streaming_mode); } 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) { + const Rect& group_rect, const FrameDimensions& patch_dim, + const Rect& frame_area_rect, 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) { + bool groupwise = enc_state->streaming_mode; + + if (do_color && frame_header.loop_filter.gab && !groupwise) { float w = 0.9908511000000001f; float weights[3] = {w, w, w}; - GaborishInverse(color, Rect(*color), weights, pool); + JXL_RETURN_IF_ERROR(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); + cparams_.decoding_speed_tier < 2 && !groupwise) { + JXL_RETURN_IF_ERROR(FindBestPatchDictionary( + *color, enc_state, cms, nullptr, aux_out, + cparams_.color_transform == ColorTransform::kXYB)); PatchDictionaryEncoder::SubtractFrom( enc_state->shared.image_features.patches, color); } + if (cparams_.custom_splines.HasAny()) { + PassesSharedState& shared = enc_state->shared; + ImageFeatures& image_features = shared.image_features; + image_features.splines = cparams_.custom_splines; + } + // Convert ImageBundle to modular Image object - const size_t xsize = frame_dim_.xsize; - const size_t ysize = frame_dim_.ysize; + const size_t xsize = patch_dim.xsize; + const size_t ysize = patch_dim.ysize; int nb_chans = 3; if (metadata.color_encoding.IsGray() && @@ -423,7 +580,9 @@ Status ModularFrameEncoder::ComputeEncodingData( 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); + JXL_ASSIGN_OR_RETURN( + gi, Image::Create(xsize, ysize, metadata.bit_depth.bits_per_sample, + nb_chans)); int c = 0; if (cparams_.color_transform == ColorTransform::kXYB && cparams_.modular_mode == true) { @@ -478,17 +637,21 @@ Status ModularFrameEncoder::ComputeEncodingData( 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); + JXL_RETURN_IF_ERROR( + 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) { + if (has_error) return; const size_t y = task; - const float* const JXL_RESTRICT row_in = color->PlaneRow(c, y); + const float* const JXL_RESTRICT row_in = + color->PlaneRow(c, y + group_rect.y0()) + group_rect.x0(); 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; + return; }; }, "float2int")); @@ -505,8 +668,9 @@ Status ModularFrameEncoder::ComputeEncodingData( 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)); + JXL_RETURN_IF_ERROR( + gi.channel[c].shrink(DivCeil(patch_dim.xsize_upsampled, ecups), + DivCeil(patch_dim.ysize_upsampled, ecups))); gi.channel[c].hshift = gi.channel[c].vshift = CeilLog2Nonzero(ecups) - CeilLog2Nonzero(frame_header.upsampling); @@ -519,12 +683,15 @@ Status ModularFrameEncoder::ComputeEncodingData( JXL_RETURN_IF_ERROR(RunOnPool( pool, 0, gi.channel[c].plane.ysize(), ThreadPool::NoInit, [&](const int task, const int thread) { + if (has_error) return; const size_t y = task; - const float* const JXL_RESTRICT row_in = extra_channels[ec].Row(y); + const float* const JXL_RESTRICT row_in = + extra_channels[ec].Row(y + group_rect.y0()) + group_rect.x0(); 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; + return; }; }, "float2int")); @@ -533,11 +700,12 @@ Status ModularFrameEncoder::ComputeEncodingData( JXL_ASSERT(c == nb_chans); int level_max_bitdepth = (cparams_.level == 5 ? 16 : 32); - if (max_bitdepth > level_max_bitdepth) + 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()) { @@ -553,14 +721,14 @@ Status ModularFrameEncoder::ComputeEncodingData( } // Global palette - if (cparams_.palette_colors != 0 || cparams_.lossy_palette) { + if ((cparams_.palette_colors != 0 || cparams_.lossy_palette) && !groupwise) { // 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.nb_colors = std::min(static_cast<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); @@ -569,8 +737,8 @@ Status ModularFrameEncoder::ComputeEncodingData( } // TODO(veluca): use a custom weighted header if using the weighted // predictor. - do_transform(gi, maybe_palette, weighted::Header(), pool, - cparams_.options.zero_tokens); + maybe_do_transform(gi, maybe_palette, cparams_, weighted::Header(), pool, + cparams_.options.zero_tokens); } // all-minus-one-channel palette (RGB with separate alpha, or CMY with // separate K) @@ -578,20 +746,20 @@ Status ModularFrameEncoder::ComputeEncodingData( 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.nb_colors = std::min(static_cast<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); + maybe_do_transform(gi, maybe_palette_3, cparams_, weighted::Header(), + pool, cparams_.options.zero_tokens); } } // Global channel palette - if (cparams_.channel_colors_pre_transform_percent > 0 && + if (!groupwise && cparams_.channel_colors_pre_transform_percent > 0 && !cparams_.lossy_palette && (cparams_.speed_tier <= SpeedTier::kThunder || (do_color && metadata.bit_depth.bits_per_sample > 8))) { @@ -600,9 +768,10 @@ Status ModularFrameEncoder::ComputeEncodingData( int orig_bitdepth = max_bitdepth; max_bitdepth = 0; for (size_t i = 0; i < nb_channels; i++) { - int32_t min, max; + int32_t min; + int32_t max; compute_minmax(gi.channel[gi.nb_meta_channels + i], &min, &max); - int64_t colors = (int64_t)max - min + 1; + int64_t colors = static_cast<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; @@ -612,9 +781,11 @@ Status ModularFrameEncoder::ComputeEncodingData( // (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)) { + static_cast<int>(xsize * ysize / 16), + static_cast<int>(cparams_.channel_colors_pre_transform_percent / + 100. * colors)); + if (maybe_do_transform(gi, maybe_palette_1, cparams_, 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; @@ -646,8 +817,28 @@ Status ModularFrameEncoder::ComputeEncodingData( } } + if (cparams_.move_to_front_from_channel > 0) { + for (size_t tgt = 0; + tgt + cparams_.move_to_front_from_channel < gi.channel.size(); tgt++) { + size_t pos = cparams_.move_to_front_from_channel; + while (pos > 0) { + Transform move(TransformId::kRCT); + if (pos == 1) { + move.begin_c = tgt; + move.rct_type = 28; // RGB -> GRB + pos -= 1; + } else { + move.begin_c = tgt + pos - 2; + move.rct_type = 14; // RGB -> BRG + pos -= 2; + } + do_transform(gi, move, weighted::Header(), pool); + } + } + } + // don't do squeeze if we don't have some spare bits - if (cparams_.responsive && !gi.channel.empty() && + if (!groupwise && cparams_.responsive && !gi.channel.empty() && max_bitdepth + 2 < level_max_bitdepth) { Transform t(TransformId::kSqueeze); do_transform(gi, t, weighted::Header(), pool); @@ -674,8 +865,8 @@ Status ModularFrameEncoder::ComputeEncodingData( bitdepth_correction = maxval / 255.f; } std::vector<float> quantizers; - float dist = cparams_.butteraugli_distance; for (size_t i = 0; i < 3; i++) { + float dist = cparams_.butteraugli_distance; quantizers.push_back(quantizer * dist * bitdepth_correction); } for (size_t i = 0; i < extra_channels.size(); i++) { @@ -683,6 +874,7 @@ Status ModularFrameEncoder::ComputeEncodingData( 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; + float dist = 0; 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); @@ -722,57 +914,57 @@ Status ModularFrameEncoder::ComputeEncodingData( } // 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); + for (size_t group_id = 0; group_id < patch_dim.num_dc_groups; group_id++) { + const size_t rgx = group_id % patch_dim.xsize_dc_groups; + const size_t rgy = group_id / patch_dim.xsize_dc_groups; + const Rect rect(rgx * patch_dim.dc_group_dim, rgy * patch_dim.dc_group_dim, + patch_dim.dc_group_dim, patch_dim.dc_group_dim); + size_t gx = rgx + frame_area_rect.x0() / 2048; + size_t gy = rgy + frame_area_rect.y0() / 2048; + size_t real_group_id = gy * frame_dim_.xsize_dc_groups + gx; // 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)}); + stream_params_.push_back( + GroupParams{rect, 3, 1000, ModularStreamId::ModularDC(real_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 group_id = 0; group_id < patch_dim.num_groups; group_id++) { + const size_t rgx = group_id % patch_dim.xsize_groups; + const size_t rgy = group_id / patch_dim.xsize_groups; + const Rect mrect(rgx * patch_dim.group_dim, rgy * patch_dim.group_dim, + patch_dim.group_dim, patch_dim.group_dim); + size_t gx = rgx + frame_area_rect.x0() / (frame_dim_.group_dim); + size_t gy = rgy + frame_area_rect.y0() / (frame_dim_.group_dim); + size_t real_group_id = gy * frame_dim_.xsize_groups + gx; for (size_t i = 0; i < enc_state->progressive_splitter.GetNumPasses(); i++) { - int maxShift, minShift; + int maxShift; + int minShift; frame_header.passes.GetDownsamplingBracket(i, minShift, maxShift); - stream_params.push_back(GroupParams{ - mrect, minShift, maxShift, ModularStreamId::ModularAC(group_id, i)}); + stream_params_.push_back( + GroupParams{mrect, minShift, maxShift, + ModularStreamId::ModularAC(real_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()}); + 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, + 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; + size_t stream = stream_params_[i].id.ID(frame_dim_); + stream_options_[stream] = stream_options_[0]; JXL_CHECK(PrepareStreamParams( - stream_params[i].rect, cparams_, stream_params[i].minShift, - stream_params[i].maxShift, stream_params[i].id, do_color)); + stream_params_[i].rect, cparams_, stream_params_[i].minShift, + stream_params_[i].maxShift, stream_params_[i].id, do_color, + groupwise)); }, "ChooseParams")); { @@ -821,7 +1013,7 @@ Status ModularFrameEncoder::ComputeTree(ThreadPool* pool) { StaticPropRange range; range[0] = {{i, i + 1}}; range[1] = {{stream_id, stream_id + 1}}; - multiplier_info.push_back({range, (uint32_t)q}); + multiplier_info.push_back({range, static_cast<uint32_t>(q)}); } else { // Previous channel in the same group had the same quantization // factor. Don't provide two different ranges, as that creates @@ -922,11 +1114,10 @@ Status ModularFrameEncoder::ComputeTree(ThreadPool* pool) { 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, + range, 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( @@ -937,7 +1128,7 @@ Status ModularFrameEncoder::ComputeTree(ThreadPool* pool) { // TODO(veluca): parallelize more. trees[chunk] = LearnTree(std::move(tree_samples), total_pixels, - stream_options_[start], local_multiplier_info, range); + stream_options_[start], multiplier_info, range); }, "LearnTrees")); if (invalid_force_wp.test_and_set(std::memory_order_acq_rel)) { @@ -966,7 +1157,7 @@ Status ModularFrameEncoder::ComputeTree(ThreadPool* pool) { tree_tokens_.resize(1); tree_tokens_[0].clear(); 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_ = std::move(decoded_tree); @@ -1019,46 +1210,8 @@ Status ModularFrameEncoder::EncodeGlobalInfo(bool streaming_mode, 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; - } + HistogramParams params = + HistogramParams::ForModular(cparams_, extra_dc_precision, streaming_mode); { EntropyEncodingData tree_code; std::vector<uint8_t> tree_context_map; @@ -1082,6 +1235,7 @@ Status ModularFrameEncoder::EncodeStream(BitWriter* writer, AuxOut* aux_out, const ModularStreamId& stream) { size_t stream_id = stream.ID(frame_dim_); if (stream_images_[stream_id].channel.empty()) { + JXL_DEBUG_V(10, "Modular stream %" PRIuS " is empty.", stream_id); return true; // Image with no channels, header never gets decoded. } if (tokens_.empty()) { @@ -1103,113 +1257,44 @@ void ModularFrameEncoder::ClearStreamData(const ModularStreamId& stream) { 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(); - } +void ModularFrameEncoder::ClearModularStreamData() { + for (const auto& group : stream_params_) { + ClearStreamData(group.id); } - return histo_cost + extra_bits; + stream_params_.clear(); } -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; +size_t ModularFrameEncoder::ComputeStreamingAbsoluteAcGroupId( + size_t dc_group_id, size_t ac_group_id, + const FrameDimensions& patch_dim) const { + size_t dc_group_x = dc_group_id % frame_dim_.xsize_dc_groups; + size_t dc_group_y = dc_group_id / frame_dim_.xsize_dc_groups; + size_t ac_group_x = ac_group_id % patch_dim.xsize_groups; + size_t ac_group_y = ac_group_id / patch_dim.xsize_groups; + return (dc_group_x * 8 + ac_group_x) + + (dc_group_y * 8 + ac_group_y) * frame_dim_.xsize_groups; } -} // namespace - Status ModularFrameEncoder::PrepareStreamParams(const Rect& rect, const CompressParams& cparams_, int minShift, int maxShift, const ModularStreamId& stream, - bool do_color) { + bool do_color, bool groupwise) { 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); + JXL_ASSIGN_OR_RETURN(gi, + Image::Create(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; + if (!groupwise) { + 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]; @@ -1220,7 +1305,7 @@ Status ModularFrameEncoder::PrepareStreamParams(const Rect& rect, 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()); + JXL_ASSIGN_OR_RETURN(Channel gc, Channel::Create(r.xsize(), r.ysize())); gc.hshift = fc.hshift; gc.vshift = fc.vshift; for (size_t y = 0; y < r.ysize(); ++y) { @@ -1245,7 +1330,7 @@ Status ModularFrameEncoder::PrepareStreamParams(const Rect& rect, 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()); + maybe_do_transform(gi, maybe_palette, cparams_, weighted::Header()); } // all-minus-one-channel palette (RGB with separate alpha, or CMY with // separate K) @@ -1259,7 +1344,7 @@ Status ModularFrameEncoder::PrepareStreamParams(const Rect& rect, if (maybe_palette_3.lossy_palette) { maybe_palette_3.predictor = Predictor::Weighted; } - do_transform(gi, maybe_palette_3, weighted::Header()); + maybe_do_transform(gi, maybe_palette_3, cparams_, weighted::Header()); } } @@ -1271,9 +1356,10 @@ Status ModularFrameEncoder::PrepareStreamParams(const Rect& rect, // 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; + int32_t min; + int32_t max; compute_minmax(gi.channel[gi.nb_meta_channels + i], &min, &max); - int64_t colors = (int64_t)max - min + 1; + int64_t colors = static_cast<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; @@ -1282,10 +1368,10 @@ Status ModularFrameEncoder::PrepareStreamParams(const Rect& rect, // 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()); + maybe_palette_1.nb_colors = std::min( + static_cast<int>(xsize * ysize * 0.8), + static_cast<int>(cparams_.channel_colors_percent / 100. * colors)); + maybe_do_transform(gi, maybe_palette_1, cparams_, weighted::Header()); } } } @@ -1295,7 +1381,7 @@ Status ModularFrameEncoder::PrepareStreamParams(const Rect& rect, 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_.responsive == JXL_FALSE && do_color && cparams_.speed_tier <= SpeedTier::kHare) { Transform sg(TransformId::kRCT); sg.begin_c = gi.nb_meta_channels; @@ -1319,6 +1405,7 @@ Status ModularFrameEncoder::PrepareStreamParams(const Rect& rect, case SpeedTier::kKitten: nb_rcts_to_try = 9; break; + case SpeedTier::kTectonicPlate: case SpeedTier::kGlacier: case SpeedTier::kTortoise: nb_rcts_to_try = 19; @@ -1403,11 +1490,11 @@ int QuantizeGradient(const int32_t* qrow, size_t onerow, size_t c, size_t x, 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) { +Status 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]; @@ -1430,8 +1517,11 @@ void ModularFrameEncoder::AddVarDCTDC(const FrameHeader& frame_header, stream_options_[stream_id].tree_kind = ModularOptions::TreeKind::kGradientFixedDC; } + stream_options_[stream_id].histogram_params = + stream_options_[0].histogram_params; - stream_images_[stream_id] = Image(r.xsize(), r.ysize(), 8, 3); + JXL_ASSIGN_OR_RETURN(stream_images_[stream_id], + Image::Create(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()); @@ -1531,7 +1621,7 @@ void ModularFrameEncoder::AddVarDCTDC(const FrameHeader& frame_header, Channel& ch = stream_images_[stream_id].channel[c < 2 ? c ^ 1 : c]; ch.w = xs; ch.h = ys; - ch.shrink(); + JXL_RETURN_IF_ERROR(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); @@ -1546,14 +1636,17 @@ void ModularFrameEncoder::AddVarDCTDC(const FrameHeader& frame_header, 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); + return true; } -void ModularFrameEncoder::AddACMetadata(const Rect& r, size_t group_index, - bool jpeg_transcode, - PassesEncoderState* enc_state) { +Status 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 (stream_options_[stream_id].predictor != Predictor::Weighted) { + stream_options_[stream_id].wp_tree_mode = ModularOptions::TreeMode::kNoWP; + } if (jpeg_transcode) { stream_options_[stream_id].tree_kind = ModularOptions::TreeKind::kJpegTranscodeACMeta; @@ -1569,14 +1662,19 @@ void ModularFrameEncoder::AddACMetadata(const Rect& r, size_t group_index, cparams_.force_cfl_jpeg_recompression) { stream_options_[stream_id].tree_kind = ModularOptions::TreeKind::kLearn; } + stream_options_[stream_id].histogram_params = + stream_options_[0].histogram_params; // YToX, YToB, ACS + QF, EPF Image& image = stream_images_[stream_id]; - image = Image(r.xsize(), r.ysize(), 8, 4); + JXL_ASSIGN_OR_RETURN(image, Image::Create(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); + JXL_ASSIGN_OR_RETURN(image.channel[0], + Channel::Create(cr.xsize(), cr.ysize(), 3, 3)); + JXL_ASSIGN_OR_RETURN(image.channel[1], + Channel::Create(cr.xsize(), cr.ysize(), 3, 3)); + JXL_ASSIGN_OR_RETURN(image.channel[2], + Channel::Create(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, @@ -1599,9 +1697,10 @@ void ModularFrameEncoder::AddACMetadata(const Rect& r, size_t group_index, } image.channel[2].w = num; ac_metadata_size[group_index] = num; + return true; } -void ModularFrameEncoder::EncodeQuantTable( +Status ModularFrameEncoder::EncodeQuantTable( size_t size_x, size_t size_y, BitWriter* writer, const QuantEncoding& encoding, size_t idx, ModularFrameEncoder* modular_frame_encoder) { @@ -1611,9 +1710,9 @@ void ModularFrameEncoder::EncodeQuantTable( if (modular_frame_encoder) { JXL_CHECK(modular_frame_encoder->EncodeStream( writer, nullptr, 0, ModularStreamId::QuantTable(idx))); - return; + return true; } - Image image(size_x, size_y, 8, 3); + JXL_ASSIGN_OR_RETURN(Image image, Image::Create(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); @@ -1624,16 +1723,17 @@ void ModularFrameEncoder::EncodeQuantTable( } ModularOptions cfopts; JXL_CHECK(ModularGenericCompress(image, cfopts, writer)); + return true; } -void ModularFrameEncoder::AddQuantTable(size_t size_x, size_t size_y, - const QuantEncoding& encoding, - size_t idx) { +Status 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); + JXL_ASSIGN_OR_RETURN(image, Image::Create(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); @@ -1642,5 +1742,6 @@ void ModularFrameEncoder::AddQuantTable(size_t size_x, size_t size_y, } } } + return true; } } // namespace jxl |