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author | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-04-19 01:13:27 +0000 |
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committer | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-04-19 01:13:27 +0000 |
commit | 40a355a42d4a9444dc753c04c6608dade2f06a23 (patch) | |
tree | 871fc667d2de662f171103ce5ec067014ef85e61 /third_party/jpeg-xl/lib/jxl/enc_heuristics.cc | |
parent | Adding upstream version 124.0.1. (diff) | |
download | firefox-40a355a42d4a9444dc753c04c6608dade2f06a23.tar.xz firefox-40a355a42d4a9444dc753c04c6608dade2f06a23.zip |
Adding upstream version 125.0.1.upstream/125.0.1
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
Diffstat (limited to 'third_party/jpeg-xl/lib/jxl/enc_heuristics.cc')
-rw-r--r-- | third_party/jpeg-xl/lib/jxl/enc_heuristics.cc | 188 |
1 files changed, 110 insertions, 78 deletions
diff --git a/third_party/jpeg-xl/lib/jxl/enc_heuristics.cc b/third_party/jpeg-xl/lib/jxl/enc_heuristics.cc index 9d6bf11184..685558ac7c 100644 --- a/third_party/jpeg-xl/lib/jxl/enc_heuristics.cc +++ b/third_party/jpeg-xl/lib/jxl/enc_heuristics.cc @@ -149,8 +149,10 @@ void FindBestBlockEntropyModel(const CompressParams& cparams, const ImageI& rqf, std::vector<uint8_t> remap((qft.size() + 1) * kNumOrders); std::iota(remap.begin(), remap.end(), 0); std::vector<uint8_t> clusters(remap); - size_t nb_clusters = Clamp1((int)(tot / size_for_ctx_model / 2), 2, 9); - size_t nb_clusters_chroma = Clamp1((int)(tot / size_for_ctx_model / 3), 1, 5); + size_t nb_clusters = + Clamp1(static_cast<int>(tot / size_for_ctx_model / 2), 2, 9); + size_t nb_clusters_chroma = + Clamp1(static_cast<int>(tot / size_for_ctx_model / 3), 1, 5); // This is O(n^2 log n), but n is small. while (clusters.size() > nb_clusters) { std::sort(clusters.begin(), clusters.end(), @@ -181,8 +183,8 @@ void FindBestBlockEntropyModel(const CompressParams& cparams, const ImageI& rqf, // for chroma, only use up to nb_clusters_chroma separate block contexts // (those for the biggest clusters) for (size_t i = remap.size(); i < remap.size() * 3; i++) { - ctx_map[i] = num + Clamp1((int)remap[i % remap.size()], 0, - (int)nb_clusters_chroma - 1); + ctx_map[i] = num + Clamp1(static_cast<int>(remap[i % remap.size()]), 0, + static_cast<int>(nb_clusters_chroma) - 1); } block_ctx_map->num_ctxs = *std::max_element(ctx_map.begin(), ctx_map.end()) + 1; @@ -190,9 +192,9 @@ void FindBestBlockEntropyModel(const CompressParams& cparams, const ImageI& rqf, namespace { -void FindBestDequantMatrices(const CompressParams& cparams, - ModularFrameEncoder* modular_frame_encoder, - DequantMatrices* dequant_matrices) { +Status FindBestDequantMatrices(const CompressParams& cparams, + ModularFrameEncoder* modular_frame_encoder, + DequantMatrices* dequant_matrices) { // TODO(veluca): quant matrices for no-gaborish. // TODO(veluca): heuristics for in-bitstream quant tables. *dequant_matrices = DequantMatrices(); @@ -204,13 +206,14 @@ void FindBestDequantMatrices(const CompressParams& cparams, DctQuantWeightParams dct_params(weights); std::vector<QuantEncoding> encodings(DequantMatrices::kNum, QuantEncoding::DCT(dct_params)); - DequantMatricesSetCustom(dequant_matrices, encodings, - modular_frame_encoder); + JXL_RETURN_IF_ERROR(DequantMatricesSetCustom(dequant_matrices, encodings, + modular_frame_encoder)); float dc_weights[3] = {1.0f / cparams.max_error[0], 1.0f / cparams.max_error[1], 1.0f / cparams.max_error[2]}; DequantMatricesSetCustomDC(dequant_matrices, dc_weights); } + return true; } void StoreMin2(const float v, float& min1, float& min2) { @@ -226,9 +229,9 @@ void StoreMin2(const float v, float& min1, float& min2) { void CreateMask(const ImageF& image, ImageF& mask) { for (size_t y = 0; y < image.ysize(); y++) { - auto* row_n = y > 0 ? image.Row(y - 1) : image.Row(y); - auto* row_in = image.Row(y); - auto* row_s = y + 1 < image.ysize() ? image.Row(y + 1) : image.Row(y); + const auto* row_n = y > 0 ? image.Row(y - 1) : image.Row(y); + const auto* row_in = image.Row(y); + const auto* row_s = y + 1 < image.ysize() ? image.Row(y + 1) : image.Row(y); auto* row_out = mask.Row(y); for (size_t x = 0; x < image.xsize(); x++) { // Center, west, east, north, south values and their absolute difference @@ -258,7 +261,7 @@ void CreateMask(const ImageF& image, ImageF& mask) { // by the decoder. Ringing is slightly reduced by clamping the values of the // resulting pixels within certain bounds of a small region in the original // image. -void DownsampleImage2_Sharper(const ImageF& input, ImageF* output) { +Status DownsampleImage2_Sharper(const ImageF& input, ImageF* output) { const int64_t kernelx = 12; const int64_t kernely = 12; @@ -315,11 +318,12 @@ void DownsampleImage2_Sharper(const ImageF& input, ImageF* output) { int64_t xsize = input.xsize(); int64_t ysize = input.ysize(); - ImageF box_downsample(xsize, ysize); + JXL_ASSIGN_OR_RETURN(ImageF box_downsample, ImageF::Create(xsize, ysize)); CopyImageTo(input, &box_downsample); - DownsampleImage(&box_downsample, 2); + JXL_ASSIGN_OR_RETURN(box_downsample, DownsampleImage(box_downsample, 2)); - ImageF mask(box_downsample.xsize(), box_downsample.ysize()); + JXL_ASSIGN_OR_RETURN(ImageF mask, ImageF::Create(box_downsample.xsize(), + box_downsample.ysize())); CreateMask(box_downsample, mask); for (size_t y = 0; y < output->ysize(); y++) { @@ -379,50 +383,54 @@ void DownsampleImage2_Sharper(const ImageF& input, ImageF* output) { } } } + return true; } } // namespace -void DownsampleImage2_Sharper(Image3F* opsin) { +Status DownsampleImage2_Sharper(Image3F* opsin) { // Allocate extra space to avoid a reallocation when padding. - Image3F downsampled(DivCeil(opsin->xsize(), 2) + kBlockDim, - DivCeil(opsin->ysize(), 2) + kBlockDim); + JXL_ASSIGN_OR_RETURN(Image3F downsampled, + Image3F::Create(DivCeil(opsin->xsize(), 2) + kBlockDim, + DivCeil(opsin->ysize(), 2) + kBlockDim)); downsampled.ShrinkTo(downsampled.xsize() - kBlockDim, downsampled.ysize() - kBlockDim); for (size_t c = 0; c < 3; c++) { - DownsampleImage2_Sharper(opsin->Plane(c), &downsampled.Plane(c)); + JXL_RETURN_IF_ERROR( + DownsampleImage2_Sharper(opsin->Plane(c), &downsampled.Plane(c))); } *opsin = std::move(downsampled); + return true; } namespace { // The default upsampling kernels used by Upsampler in the decoder. -static const constexpr int64_t kSize = 5; +const constexpr int64_t kSize = 5; -static const float kernel00[25] = { +const float kernel00[25] = { -0.01716200f, -0.03452303f, -0.04022174f, -0.02921014f, -0.00624645f, -0.03452303f, 0.14111091f, 0.28896755f, 0.00278718f, -0.01610267f, -0.04022174f, 0.28896755f, 0.56661550f, 0.03777607f, -0.01986694f, -0.02921014f, 0.00278718f, 0.03777607f, -0.03144731f, -0.01185068f, -0.00624645f, -0.01610267f, -0.01986694f, -0.01185068f, -0.00213539f, }; -static const float kernel01[25] = { +const float kernel01[25] = { -0.00624645f, -0.01610267f, -0.01986694f, -0.01185068f, -0.00213539f, -0.02921014f, 0.00278718f, 0.03777607f, -0.03144731f, -0.01185068f, -0.04022174f, 0.28896755f, 0.56661550f, 0.03777607f, -0.01986694f, -0.03452303f, 0.14111091f, 0.28896755f, 0.00278718f, -0.01610267f, -0.01716200f, -0.03452303f, -0.04022174f, -0.02921014f, -0.00624645f, }; -static const float kernel10[25] = { +const float kernel10[25] = { -0.00624645f, -0.02921014f, -0.04022174f, -0.03452303f, -0.01716200f, -0.01610267f, 0.00278718f, 0.28896755f, 0.14111091f, -0.03452303f, -0.01986694f, 0.03777607f, 0.56661550f, 0.28896755f, -0.04022174f, -0.01185068f, -0.03144731f, 0.03777607f, 0.00278718f, -0.02921014f, -0.00213539f, -0.01185068f, -0.01986694f, -0.01610267f, -0.00624645f, }; -static const float kernel11[25] = { +const float kernel11[25] = { -0.00213539f, -0.01185068f, -0.01986694f, -0.01610267f, -0.00624645f, -0.01185068f, -0.03144731f, 0.03777607f, 0.00278718f, -0.02921014f, -0.01986694f, 0.03777607f, 0.56661550f, 0.28896755f, -0.04022174f, @@ -435,14 +443,14 @@ static const float kernel11[25] = { // TODO(lode): use Upsampler instead. However, it requires pre-initialization // and padding on the left side of the image which requires refactoring the // other code using this. -static void UpsampleImage(const ImageF& input, ImageF* output) { +void UpsampleImage(const ImageF& input, ImageF* output) { int64_t xsize = input.xsize(); int64_t ysize = input.ysize(); int64_t xsize2 = output->xsize(); int64_t ysize2 = output->ysize(); for (int64_t y = 0; y < ysize2; y++) { for (int64_t x = 0; x < xsize2; x++) { - auto kernel = kernel00; + const auto* kernel = kernel00; if ((x & 1) && (y & 1)) { kernel = kernel11; } else if (x & 1) { @@ -492,7 +500,7 @@ static void UpsampleImage(const ImageF& input, ImageF* output) { // Returns the derivative of Upsampler, with respect to input pixel x2, y2, to // output pixel x, y (ignoring the clamping). float UpsamplerDeriv(int64_t x2, int64_t y2, int64_t x, int64_t y) { - auto kernel = kernel00; + const auto* kernel = kernel00; if ((x & 1) && (y & 1)) { kernel = kernel11; } else if (x & 1) { @@ -597,11 +605,9 @@ void ReduceRinging(const ImageF& initial, const ImageF& mask, ImageF& down) { float max = initial.Row(y)[x]; for (int64_t yi = -1; yi < 2; yi++) { for (int64_t xi = -1; xi < 2; xi++) { - int64_t x2 = (int64_t)x + xi; - int64_t y2 = (int64_t)y + yi; - if (x2 < 0 || y2 < 0 || x2 >= (int64_t)xsize2 || - y2 >= (int64_t)ysize2) - continue; + int64_t x2 = static_cast<int64_t>(x) + xi; + int64_t y2 = static_cast<int64_t>(y) + yi; + if (x2 < 0 || y2 < 0 || x2 >= xsize2 || y2 >= ysize2) continue; min = std::min<float>(min, initial.Row(y2)[x2]); max = std::max<float>(max, initial.Row(y2)[x2]); } @@ -625,32 +631,35 @@ void ReduceRinging(const ImageF& initial, const ImageF& mask, ImageF& down) { } // TODO(lode): move this to a separate file enc_downsample.cc -void DownsampleImage2_Iterative(const ImageF& orig, ImageF* output) { +Status DownsampleImage2_Iterative(const ImageF& orig, ImageF* output) { int64_t xsize = orig.xsize(); int64_t ysize = orig.ysize(); int64_t xsize2 = DivCeil(orig.xsize(), 2); int64_t ysize2 = DivCeil(orig.ysize(), 2); - ImageF box_downsample(xsize, ysize); + JXL_ASSIGN_OR_RETURN(ImageF box_downsample, ImageF::Create(xsize, ysize)); CopyImageTo(orig, &box_downsample); - DownsampleImage(&box_downsample, 2); - ImageF mask(box_downsample.xsize(), box_downsample.ysize()); + JXL_ASSIGN_OR_RETURN(box_downsample, DownsampleImage(box_downsample, 2)); + JXL_ASSIGN_OR_RETURN(ImageF mask, ImageF::Create(box_downsample.xsize(), + box_downsample.ysize())); CreateMask(box_downsample, mask); output->ShrinkTo(xsize2, ysize2); // Initial result image using the sharper downsampling. // Allocate extra space to avoid a reallocation when padding. - ImageF initial(DivCeil(orig.xsize(), 2) + kBlockDim, - DivCeil(orig.ysize(), 2) + kBlockDim); + JXL_ASSIGN_OR_RETURN(ImageF initial, + ImageF::Create(DivCeil(orig.xsize(), 2) + kBlockDim, + DivCeil(orig.ysize(), 2) + kBlockDim)); initial.ShrinkTo(initial.xsize() - kBlockDim, initial.ysize() - kBlockDim); - DownsampleImage2_Sharper(orig, &initial); + JXL_RETURN_IF_ERROR(DownsampleImage2_Sharper(orig, &initial)); - ImageF down(initial.xsize(), initial.ysize()); + JXL_ASSIGN_OR_RETURN(ImageF down, + ImageF::Create(initial.xsize(), initial.ysize())); CopyImageTo(initial, &down); - ImageF up(xsize, ysize); - ImageF corr(xsize, ysize); - ImageF corr2(xsize2, ysize2); + JXL_ASSIGN_OR_RETURN(ImageF up, ImageF::Create(xsize, ysize)); + JXL_ASSIGN_OR_RETURN(ImageF corr, ImageF::Create(xsize, ysize)); + JXL_ASSIGN_OR_RETURN(ImageF corr2, ImageF::Create(xsize2, ysize2)); // In the weights map, relatively higher values will allow less ringing but // also less sharpness. With all constant values, it optimizes equally @@ -659,25 +668,25 @@ void DownsampleImage2_Iterative(const ImageF& orig, ImageF* output) { // TODO(lode): Make use of the weights field for anti-ringing and clamping, // the values are all set to 1 for now, but it is intended to be used for // reducing ringing based on the mask, and taking clamping into account. - ImageF weights(xsize, ysize); + JXL_ASSIGN_OR_RETURN(ImageF weights, ImageF::Create(xsize, ysize)); for (size_t y = 0; y < weights.ysize(); y++) { auto* row = weights.Row(y); for (size_t x = 0; x < weights.xsize(); x++) { row[x] = 1; } } - ImageF weights2(xsize2, ysize2); + JXL_ASSIGN_OR_RETURN(ImageF weights2, ImageF::Create(xsize2, ysize2)); AntiUpsample(weights, &weights2); const size_t num_it = 3; for (size_t it = 0; it < num_it; ++it) { UpsampleImage(down, &up); - corr = LinComb<float>(1, orig, -1, up); + JXL_ASSIGN_OR_RETURN(corr, LinComb<float>(1, orig, -1, up)); ElwiseMul(corr, weights, &corr); AntiUpsample(corr, &corr2); ElwiseDiv(corr2, weights2, &corr2); - down = LinComb<float>(1, down, 1, corr2); + JXL_ASSIGN_OR_RETURN(down, LinComb<float>(1, down, 1, corr2)); } ReduceRinging(initial, mask, down); @@ -690,32 +699,40 @@ void DownsampleImage2_Iterative(const ImageF& orig, ImageF* output) { output->Row(y)[x] = v; } } + return true; } } // namespace -void DownsampleImage2_Iterative(Image3F* opsin) { +Status DownsampleImage2_Iterative(Image3F* opsin) { // Allocate extra space to avoid a reallocation when padding. - Image3F downsampled(DivCeil(opsin->xsize(), 2) + kBlockDim, - DivCeil(opsin->ysize(), 2) + kBlockDim); + JXL_ASSIGN_OR_RETURN(Image3F downsampled, + Image3F::Create(DivCeil(opsin->xsize(), 2) + kBlockDim, + DivCeil(opsin->ysize(), 2) + kBlockDim)); downsampled.ShrinkTo(downsampled.xsize() - kBlockDim, downsampled.ysize() - kBlockDim); - Image3F rgb(opsin->xsize(), opsin->ysize()); + JXL_ASSIGN_OR_RETURN(Image3F rgb, + Image3F::Create(opsin->xsize(), opsin->ysize())); OpsinParams opsin_params; // TODO(user): use the ones that are actually used opsin_params.Init(kDefaultIntensityTarget); OpsinToLinear(*opsin, Rect(rgb), nullptr, &rgb, opsin_params); - ImageF mask(opsin->xsize(), opsin->ysize()); + JXL_ASSIGN_OR_RETURN(ImageF mask, + ImageF::Create(opsin->xsize(), opsin->ysize())); ButteraugliParams butter_params; - ButteraugliComparator butter(rgb, butter_params); - butter.Mask(&mask); - ImageF mask_fuzzy(opsin->xsize(), opsin->ysize()); + JXL_ASSIGN_OR_RETURN(std::unique_ptr<ButteraugliComparator> butter, + ButteraugliComparator::Make(rgb, butter_params)); + JXL_RETURN_IF_ERROR(butter->Mask(&mask)); + JXL_ASSIGN_OR_RETURN(ImageF mask_fuzzy, + ImageF::Create(opsin->xsize(), opsin->ysize())); for (size_t c = 0; c < 3; c++) { - DownsampleImage2_Iterative(opsin->Plane(c), &downsampled.Plane(c)); + JXL_RETURN_IF_ERROR( + DownsampleImage2_Iterative(opsin->Plane(c), &downsampled.Plane(c))); } *opsin = std::move(downsampled); + return true; } Status LossyFrameHeuristics(const FrameHeader& frame_header, @@ -739,10 +756,11 @@ Status LossyFrameHeuristics(const FrameHeader& frame_header, BlockCtxMap& block_ctx_map = shared.block_ctx_map; // Find and subtract splines. + if (cparams.custom_splines.HasAny()) { + image_features.splines = cparams.custom_splines; + } if (!streaming_mode && cparams.speed_tier <= SpeedTier::kSquirrel) { - if (cparams.custom_splines.HasAny()) { - image_features.splines = cparams.custom_splines; - } else { + if (!cparams.custom_splines.HasAny()) { image_features.splines = FindSplines(*opsin); } JXL_RETURN_IF_ERROR(image_features.splines.InitializeDrawCache( @@ -754,7 +772,8 @@ Status LossyFrameHeuristics(const FrameHeader& frame_header, if (!streaming_mode && ApplyOverride(cparams.patches, cparams.speed_tier <= SpeedTier::kSquirrel)) { - FindBestPatchDictionary(*opsin, enc_state, cms, pool, aux_out); + JXL_RETURN_IF_ERROR( + FindBestPatchDictionary(*opsin, enc_state, cms, pool, aux_out)); PatchDictionaryEncoder::SubtractFrom(image_features.patches, opsin); } @@ -791,10 +810,12 @@ Status LossyFrameHeuristics(const FrameHeader& frame_header, // on simple heuristics in FindBestAcStrategy, or set a constant for Falcon // mode. if (cparams.speed_tier > SpeedTier::kHare) { - initial_quant_field = - ImageF(frame_dim.xsize_blocks, frame_dim.ysize_blocks); - initial_quant_masking = - ImageF(frame_dim.xsize_blocks, frame_dim.ysize_blocks); + JXL_ASSIGN_OR_RETURN( + initial_quant_field, + ImageF::Create(frame_dim.xsize_blocks, frame_dim.ysize_blocks)); + JXL_ASSIGN_OR_RETURN( + initial_quant_masking, + ImageF::Create(frame_dim.xsize_blocks, frame_dim.ysize_blocks)); float q = 0.79 / cparams.butteraugli_distance; FillImage(q, &initial_quant_field); FillImage(1.0f / (q + 0.001f), &initial_quant_masking); @@ -805,9 +826,11 @@ Status LossyFrameHeuristics(const FrameHeader& frame_header, if (!frame_header.loop_filter.gab) { butteraugli_distance_for_iqf *= 0.73f; } - initial_quant_field = InitialQuantField( - butteraugli_distance_for_iqf, *opsin, rect, pool, 1.0f, - &initial_quant_masking, &initial_quant_masking1x1); + JXL_ASSIGN_OR_RETURN( + initial_quant_field, + InitialQuantField(butteraugli_distance_for_iqf, *opsin, rect, pool, + 1.0f, &initial_quant_masking, + &initial_quant_masking1x1)); float q = 0.39 / cparams.butteraugli_distance; quantizer.ComputeGlobalScaleAndQuant(quant_dc, q, 0); } @@ -822,18 +845,21 @@ Status LossyFrameHeuristics(const FrameHeader& frame_header, 0.99406123118127299f, 0.99719338015886894f, }; - GaborishInverse(opsin, rect, weight, pool); + JXL_RETURN_IF_ERROR(GaborishInverse(opsin, rect, weight, pool)); } if (initialize_global_state) { - FindBestDequantMatrices(cparams, modular_frame_encoder, &matrices); + JXL_RETURN_IF_ERROR( + FindBestDequantMatrices(cparams, modular_frame_encoder, &matrices)); } - cfl_heuristics.Init(rect); + JXL_RETURN_IF_ERROR(cfl_heuristics.Init(rect)); acs_heuristics.Init(*opsin, rect, initial_quant_field, initial_quant_masking, initial_quant_masking1x1, &matrices); + std::atomic<bool> has_error{false}; auto process_tile = [&](const uint32_t tid, const size_t thread) { + if (has_error) return; size_t n_enc_tiles = DivCeil(frame_dim.xsize_blocks, kEncTileDimInBlocks); size_t tx = tid % n_enc_tiles; size_t ty = tid / n_enc_tiles; @@ -856,13 +882,16 @@ Status LossyFrameHeuristics(const FrameHeader& frame_header, } // Choose block sizes. - acs_heuristics.ProcessRect(r, cmap, &ac_strategy); + acs_heuristics.ProcessRect(r, cmap, &ac_strategy, thread); // Choose amount of post-processing smoothing. // TODO(veluca): should this go *after* AdjustQuantField? - ar_heuristics.RunRect(cparams, frame_header, r, *opsin, rect, - initial_quant_field, ac_strategy, &epf_sharpness, - thread); + if (!ar_heuristics.RunRect(cparams, frame_header, r, *opsin, rect, + initial_quant_field, ac_strategy, &epf_sharpness, + thread)) { + has_error = true; + return; + } // Always set the initial quant field, so we can compute the CfL map with // more accuracy. The initial quant field might change in slower modes, but @@ -884,18 +913,21 @@ Status LossyFrameHeuristics(const FrameHeader& frame_header, DivCeil(frame_dim.xsize_blocks, kEncTileDimInBlocks) * DivCeil(frame_dim.ysize_blocks, kEncTileDimInBlocks), [&](const size_t num_threads) { + acs_heuristics.PrepareForThreads(num_threads); ar_heuristics.PrepareForThreads(num_threads); cfl_heuristics.PrepareForThreads(num_threads); return true; }, process_tile, "Enc Heuristics")); + if (has_error) return JXL_FAILURE("Enc Heuristics failed"); - acs_heuristics.Finalize(frame_dim, ac_strategy, aux_out); + JXL_RETURN_IF_ERROR(acs_heuristics.Finalize(frame_dim, ac_strategy, aux_out)); // Refine quantization levels. if (!streaming_mode) { - FindBestQuantizer(frame_header, original_pixels, *opsin, - initial_quant_field, enc_state, cms, pool, aux_out); + JXL_RETURN_IF_ERROR(FindBestQuantizer(frame_header, original_pixels, *opsin, + initial_quant_field, enc_state, cms, + pool, aux_out)); } // Choose a context model that depends on the amount of quantization for AC. |