Adding upstream version 115.7.0esr.upstream/115.7.0esr

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

1 files changed, 694 insertions, 0 deletions

diff --git a/third_party/jpeg-xl/lib/jxl/splines.cc b/third_party/jpeg-xl/lib/jxl/splines.cc
new file mode 100644
index 0000000000..04d1df8e49
--- /dev/null
+++ b/third_party/jpeg-xl/lib/jxl/splines.cc
@@ -0,0 +1,694 @@
+// 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/splines.h"
+
+#include <algorithm>
+#include <cmath>
+
+#include "lib/jxl/ans_params.h"
+#include "lib/jxl/base/printf_macros.h"
+#include "lib/jxl/base/status.h"
+#include "lib/jxl/chroma_from_luma.h"
+#include "lib/jxl/common.h"
+#include "lib/jxl/dct_scales.h"
+#include "lib/jxl/entropy_coder.h"
+#include "lib/jxl/opsin_params.h"
+
+#undef HWY_TARGET_INCLUDE
+#define HWY_TARGET_INCLUDE "lib/jxl/splines.cc"
+#include <hwy/foreach_target.h>
+#include <hwy/highway.h>
+
+#include "lib/jxl/fast_math-inl.h"
+HWY_BEFORE_NAMESPACE();
+namespace jxl {
+namespace HWY_NAMESPACE {
+namespace {
+
+// These templates are not found via ADL.
+using hwy::HWY_NAMESPACE::Mul;
+using hwy::HWY_NAMESPACE::MulAdd;
+using hwy::HWY_NAMESPACE::MulSub;
+using hwy::HWY_NAMESPACE::Sqrt;
+using hwy::HWY_NAMESPACE::Sub;
+
+// Given a set of DCT coefficients, this returns the result of performing cosine
+// interpolation on the original samples.
+float ContinuousIDCT(const float dct[32], const float t) {
+  // We compute here the DCT-3 of the `dct` vector, rescaled by a factor of
+  // sqrt(32). This is such that an input vector vector {x, 0, ..., 0} produces
+  // a constant result of x. dct[0] was scaled in Dequantize() to allow uniform
+  // treatment of all the coefficients.
+  constexpr float kMultipliers[32] = {
+      kPi / 32 * 0,  kPi / 32 * 1,  kPi / 32 * 2,  kPi / 32 * 3,  kPi / 32 * 4,
+      kPi / 32 * 5,  kPi / 32 * 6,  kPi / 32 * 7,  kPi / 32 * 8,  kPi / 32 * 9,
+      kPi / 32 * 10, kPi / 32 * 11, kPi / 32 * 12, kPi / 32 * 13, kPi / 32 * 14,
+      kPi / 32 * 15, kPi / 32 * 16, kPi / 32 * 17, kPi / 32 * 18, kPi / 32 * 19,
+      kPi / 32 * 20, kPi / 32 * 21, kPi / 32 * 22, kPi / 32 * 23, kPi / 32 * 24,
+      kPi / 32 * 25, kPi / 32 * 26, kPi / 32 * 27, kPi / 32 * 28, kPi / 32 * 29,
+      kPi / 32 * 30, kPi / 32 * 31,
+  };
+  HWY_CAPPED(float, 32) df;
+  auto result = Zero(df);
+  const auto tandhalf = Set(df, t + 0.5f);
+  for (int i = 0; i < 32; i += Lanes(df)) {
+    auto cos_arg = Mul(LoadU(df, kMultipliers + i), tandhalf);
+    auto cos = FastCosf(df, cos_arg);
+    auto local_res = Mul(LoadU(df, dct + i), cos);
+    result = MulAdd(Set(df, kSqrt2), local_res, result);
+  }
+  return GetLane(SumOfLanes(df, result));
+}
+
+template <typename DF>
+void DrawSegment(DF df, const SplineSegment& segment, const bool add,
+                 const size_t y, const size_t x, float* JXL_RESTRICT rows[3]) {
+  Rebind<int32_t, DF> di;
+  const auto inv_sigma = Set(df, segment.inv_sigma);
+  const auto half = Set(df, 0.5f);
+  const auto one_over_2s2 = Set(df, 0.353553391f);
+  const auto sigma_over_4_times_intensity =
+      Set(df, segment.sigma_over_4_times_intensity);
+  const auto dx = Sub(ConvertTo(df, Iota(di, x)), Set(df, segment.center_x));
+  const auto dy = Set(df, y - segment.center_y);
+  const auto sqd = MulAdd(dx, dx, Mul(dy, dy));
+  const auto distance = Sqrt(sqd);
+  const auto one_dimensional_factor =
+      Sub(FastErff(df, Mul(MulAdd(distance, half, one_over_2s2), inv_sigma)),
+          FastErff(df, Mul(MulSub(distance, half, one_over_2s2), inv_sigma)));
+  auto local_intensity =
+      Mul(sigma_over_4_times_intensity,
+          Mul(one_dimensional_factor, one_dimensional_factor));
+  for (size_t c = 0; c < 3; ++c) {
+    const auto cm = Set(df, add ? segment.color[c] : -segment.color[c]);
+    const auto in = LoadU(df, rows[c] + x);
+    StoreU(MulAdd(cm, local_intensity, in), df, rows[c] + x);
+  }
+}
+
+void DrawSegment(const SplineSegment& segment, const bool add, const size_t y,
+                 const ssize_t x0, ssize_t x1, float* JXL_RESTRICT rows[3]) {
+  ssize_t x =
+      std::max<ssize_t>(x0, segment.center_x - segment.maximum_distance + 0.5f);
+  // one-past-the-end
+  x1 =
+      std::min<ssize_t>(x1, segment.center_x + segment.maximum_distance + 1.5f);
+  HWY_FULL(float) df;
+  for (; x + static_cast<ssize_t>(Lanes(df)) <= x1; x += Lanes(df)) {
+    DrawSegment(df, segment, add, y, x, rows);
+  }
+  for (; x < x1; ++x) {
+    DrawSegment(HWY_CAPPED(float, 1)(), segment, add, y, x, rows);
+  }
+}
+
+void ComputeSegments(const Spline::Point& center, const float intensity,
+                     const float color[3], const float sigma,
+                     std::vector<SplineSegment>& segments,
+                     std::vector<std::pair<size_t, size_t>>& segments_by_y) {
+  // Sanity check sigma, inverse sigma and intensity
+  if (!(std::isfinite(sigma) && sigma != 0.0f && std::isfinite(1.0f / sigma) &&
+        std::isfinite(intensity))) {
+    return;
+  }
+#if JXL_HIGH_PRECISION
+  constexpr float kDistanceExp = 5;
+#else
+  // About 30% faster.
+  constexpr float kDistanceExp = 3;
+#endif
+  // We cap from below colors to at least 0.01.
+  float max_color = 0.01f;
+  for (size_t c = 0; c < 3; c++) {
+    max_color = std::max(max_color, std::abs(color[c] * intensity));
+  }
+  // Distance beyond which max_color*intensity*exp(-d^2 / (2 * sigma^2)) drops
+  // below 10^-kDistanceExp.
+  const float maximum_distance =
+      std::sqrt(-2 * sigma * sigma *
+                (std::log(0.1) * kDistanceExp - std::log(max_color)));
+  SplineSegment segment;
+  segment.center_y = center.y;
+  segment.center_x = center.x;
+  memcpy(segment.color, color, sizeof(segment.color));
+  segment.inv_sigma = 1.0f / sigma;
+  segment.sigma_over_4_times_intensity = .25f * sigma * intensity;
+  segment.maximum_distance = maximum_distance;
+  ssize_t y0 = center.y - maximum_distance + .5f;
+  ssize_t y1 = center.y + maximum_distance + 1.5f;  // one-past-the-end
+  for (ssize_t y = std::max<ssize_t>(y0, 0); y < y1; y++) {
+    segments_by_y.emplace_back(y, segments.size());
+  }
+  segments.push_back(segment);
+}
+
+void DrawSegments(float* JXL_RESTRICT row_x, float* JXL_RESTRICT row_y,
+                  float* JXL_RESTRICT row_b, const Rect& image_rect,
+                  const bool add, const SplineSegment* segments,
+                  const size_t* segment_indices,
+                  const size_t* segment_y_start) {
+  JXL_ASSERT(image_rect.ysize() == 1);
+  float* JXL_RESTRICT rows[3] = {row_x - image_rect.x0(),
+                                 row_y - image_rect.x0(),
+                                 row_b - image_rect.x0()};
+  size_t y = image_rect.y0();
+  for (size_t i = segment_y_start[y]; i < segment_y_start[y + 1]; i++) {
+    DrawSegment(segments[segment_indices[i]], add, y, image_rect.x0(),
+                image_rect.x0() + image_rect.xsize(), rows);
+  }
+}
+
+void SegmentsFromPoints(
+    const Spline& spline,
+    const std::vector<std::pair<Spline::Point, float>>& points_to_draw,
+    const float arc_length, std::vector<SplineSegment>& segments,
+    std::vector<std::pair<size_t, size_t>>& segments_by_y) {
+  const float inv_arc_length = 1.0f / arc_length;
+  int k = 0;
+  for (const auto& point_to_draw : points_to_draw) {
+    const Spline::Point& point = point_to_draw.first;
+    const float multiplier = point_to_draw.second;
+    const float progress_along_arc =
+        std::min(1.f, (k * kDesiredRenderingDistance) * inv_arc_length);
+    ++k;
+    float color[3];
+    for (size_t c = 0; c < 3; ++c) {
+      color[c] =
+          ContinuousIDCT(spline.color_dct[c], (32 - 1) * progress_along_arc);
+    }
+    const float sigma =
+        ContinuousIDCT(spline.sigma_dct, (32 - 1) * progress_along_arc);
+    ComputeSegments(point, multiplier, color, sigma, segments, segments_by_y);
+  }
+}
+}  // namespace
+// NOLINTNEXTLINE(google-readability-namespace-comments)
+}  // namespace HWY_NAMESPACE
+}  // namespace jxl
+HWY_AFTER_NAMESPACE();
+
+#if HWY_ONCE
+namespace jxl {
+HWY_EXPORT(SegmentsFromPoints);
+HWY_EXPORT(DrawSegments);
+
+namespace {
+
+// It is not in spec, but reasonable limit to avoid overflows.
+template <typename T>
+Status ValidateSplinePointPos(const T& x, const T& y) {
+  constexpr T kSplinePosLimit = 1u << 23;
+  if ((x >= kSplinePosLimit) || (x <= -kSplinePosLimit) ||
+      (y >= kSplinePosLimit) || (y <= -kSplinePosLimit)) {
+    return JXL_FAILURE("Spline coordinates out of bounds");
+  }
+  return true;
+}
+
+// Maximum number of spline control points per frame is
+//   std::min(kMaxNumControlPoints, xsize * ysize / 2)
+constexpr size_t kMaxNumControlPoints = 1u << 20u;
+constexpr size_t kMaxNumControlPointsPerPixelRatio = 2;
+
+float AdjustedQuant(const int32_t adjustment) {
+  return (adjustment >= 0) ? (1.f + .125f * adjustment)
+                           : 1.f / (1.f - .125f * adjustment);
+}
+
+float InvAdjustedQuant(const int32_t adjustment) {
+  return (adjustment >= 0) ? 1.f / (1.f + .125f * adjustment)
+                           : (1.f - .125f * adjustment);
+}
+
+// X, Y, B, sigma.
+static constexpr float kChannelWeight[] = {0.0042f, 0.075f, 0.07f, .3333f};
+
+Status DecodeAllStartingPoints(std::vector<Spline::Point>* const points,
+                               BitReader* const br, ANSSymbolReader* reader,
+                               const std::vector<uint8_t>& context_map,
+                               const size_t num_splines) {
+  points->clear();
+  points->reserve(num_splines);
+  int64_t last_x = 0;
+  int64_t last_y = 0;
+  for (size_t i = 0; i < num_splines; i++) {
+    int64_t x =
+        reader->ReadHybridUint(kStartingPositionContext, br, context_map);
+    int64_t y =
+        reader->ReadHybridUint(kStartingPositionContext, br, context_map);
+    if (i != 0) {
+      x = UnpackSigned(x) + last_x;
+      y = UnpackSigned(y) + last_y;
+    }
+    JXL_RETURN_IF_ERROR(ValidateSplinePointPos(x, y));
+    points->emplace_back(static_cast<float>(x), static_cast<float>(y));
+    last_x = x;
+    last_y = y;
+  }
+  return true;
+}
+
+struct Vector {
+  float x, y;
+  Vector operator-() const { return {-x, -y}; }
+  Vector operator+(const Vector& other) const {
+    return {x + other.x, y + other.y};
+  }
+  float SquaredNorm() const { return x * x + y * y; }
+};
+Vector operator*(const float k, const Vector& vec) {
+  return {k * vec.x, k * vec.y};
+}
+
+Spline::Point operator+(const Spline::Point& p, const Vector& vec) {
+  return {p.x + vec.x, p.y + vec.y};
+}
+Vector operator-(const Spline::Point& a, const Spline::Point& b) {
+  return {a.x - b.x, a.y - b.y};
+}
+
+// TODO(eustas): avoid making a copy of "points".
+void DrawCentripetalCatmullRomSpline(std::vector<Spline::Point> points,
+                                     std::vector<Spline::Point>& result) {
+  if (points.empty()) return;
+  if (points.size() == 1) {
+    result.push_back(points[0]);
+    return;
+  }
+  // Number of points to compute between each control point.
+  static constexpr int kNumPoints = 16;
+  result.reserve((points.size() - 1) * kNumPoints + 1);
+  points.insert(points.begin(), points[0] + (points[0] - points[1]));
+  points.push_back(points[points.size() - 1] +
+                   (points[points.size() - 1] - points[points.size() - 2]));
+  // points has at least 4 elements at this point.
+  for (size_t start = 0; start < points.size() - 3; ++start) {
+    // 4 of them are used, and we draw from p[1] to p[2].
+    const Spline::Point* const p = &points[start];
+    result.push_back(p[1]);
+    float d[3];
+    float t[4];
+    t[0] = 0;
+    for (int k = 0; k < 3; ++k) {
+      // TODO(eustas): for each segment delta is calculated 3 times...
+      // TODO(eustas): restrict d[k] with reasonable limit and spec it.
+      d[k] = std::sqrt(hypotf(p[k + 1].x - p[k].x, p[k + 1].y - p[k].y));
+      t[k + 1] = t[k] + d[k];
+    }
+    for (int i = 1; i < kNumPoints; ++i) {
+      const float tt = d[0] + (static_cast<float>(i) / kNumPoints) * d[1];
+      Spline::Point a[3];
+      for (int k = 0; k < 3; ++k) {
+        // TODO(eustas): reciprocal multiplication would be faster.
+        a[k] = p[k] + ((tt - t[k]) / d[k]) * (p[k + 1] - p[k]);
+      }
+      Spline::Point b[2];
+      for (int k = 0; k < 2; ++k) {
+        b[k] = a[k] + ((tt - t[k]) / (d[k] + d[k + 1])) * (a[k + 1] - a[k]);
+      }
+      result.push_back(b[0] + ((tt - t[1]) / d[1]) * (b[1] - b[0]));
+    }
+  }
+  result.push_back(points[points.size() - 2]);
+}
+
+// Move along the line segments defined by `points`, `kDesiredRenderingDistance`
+// pixels at a time, and call `functor` with each point and the actual distance
+// to the previous point (which will always be kDesiredRenderingDistance except
+// possibly for the very last point).
+// TODO(eustas): this method always adds the last point, but never the first
+//               (unless those are one); I believe both ends matter.
+template <typename Points, typename Functor>
+void ForEachEquallySpacedPoint(const Points& points, const Functor& functor) {
+  JXL_ASSERT(!points.empty());
+  Spline::Point current = points.front();
+  functor(current, kDesiredRenderingDistance);
+  auto next = points.begin();
+  while (next != points.end()) {
+    const Spline::Point* previous = &current;
+    float arclength_from_previous = 0.f;
+    for (;;) {
+      if (next == points.end()) {
+        functor(*previous, arclength_from_previous);
+        return;
+      }
+      const float arclength_to_next =
+          std::sqrt((*next - *previous).SquaredNorm());
+      if (arclength_from_previous + arclength_to_next >=
+          kDesiredRenderingDistance) {
+        current =
+            *previous + ((kDesiredRenderingDistance - arclength_from_previous) /
+                         arclength_to_next) *
+                            (*next - *previous);
+        functor(current, kDesiredRenderingDistance);
+        break;
+      }
+      arclength_from_previous += arclength_to_next;
+      previous = &*next;
+      ++next;
+    }
+  }
+}
+
+}  // namespace
+
+QuantizedSpline::QuantizedSpline(const Spline& original,
+                                 const int32_t quantization_adjustment,
+                                 const float y_to_x, const float y_to_b) {
+  JXL_ASSERT(!original.control_points.empty());
+  control_points_.reserve(original.control_points.size() - 1);
+  const Spline::Point& starting_point = original.control_points.front();
+  int previous_x = static_cast<int>(roundf(starting_point.x)),
+      previous_y = static_cast<int>(roundf(starting_point.y));
+  int previous_delta_x = 0, previous_delta_y = 0;
+  for (auto it = original.control_points.begin() + 1;
+       it != original.control_points.end(); ++it) {
+    const int new_x = static_cast<int>(roundf(it->x));
+    const int new_y = static_cast<int>(roundf(it->y));
+    const int new_delta_x = new_x - previous_x;
+    const int new_delta_y = new_y - previous_y;
+    control_points_.emplace_back(new_delta_x - previous_delta_x,
+                                 new_delta_y - previous_delta_y);
+    previous_delta_x = new_delta_x;
+    previous_delta_y = new_delta_y;
+    previous_x = new_x;
+    previous_y = new_y;
+  }
+
+  const auto to_int = [](float v) -> int {
+    return static_cast<int>(roundf(v));
+  };
+
+  const auto quant = AdjustedQuant(quantization_adjustment);
+  const auto inv_quant = InvAdjustedQuant(quantization_adjustment);
+  for (int c : {1, 0, 2}) {
+    float factor = (c == 0) ? y_to_x : (c == 1) ? 0 : y_to_b;
+    for (int i = 0; i < 32; ++i) {
+      const float dct_factor = (i == 0) ? kSqrt2 : 1.0f;
+      const float inv_dct_factor = (i == 0) ? kSqrt0_5 : 1.0f;
+      auto restored_y =
+          color_dct_[1][i] * inv_dct_factor * kChannelWeight[1] * inv_quant;
+      auto decorellated = original.color_dct[c][i] - factor * restored_y;
+      color_dct_[c][i] =
+          to_int(decorellated * dct_factor * quant / kChannelWeight[c]);
+    }
+  }
+  for (int i = 0; i < 32; ++i) {
+    const float dct_factor = (i == 0) ? kSqrt2 : 1.0f;
+    sigma_dct_[i] =
+        to_int(original.sigma_dct[i] * dct_factor * quant / kChannelWeight[3]);
+  }
+}
+
+Status QuantizedSpline::Dequantize(const Spline::Point& starting_point,
+                                   const int32_t quantization_adjustment,
+                                   const float y_to_x, const float y_to_b,
+                                   const uint64_t image_size,
+                                   uint64_t* total_estimated_area_reached,
+                                   Spline& result) const {
+  result.control_points.clear();
+  result.control_points.reserve(control_points_.size() + 1);
+  float px = roundf(starting_point.x);
+  float py = roundf(starting_point.y);
+  JXL_RETURN_IF_ERROR(ValidateSplinePointPos(px, py));
+  int current_x = static_cast<int>(px);
+  int current_y = static_cast<int>(py);
+  result.control_points.push_back(Spline::Point{static_cast<float>(current_x),
+                                                static_cast<float>(current_y)});
+  int current_delta_x = 0, current_delta_y = 0;
+  size_t manhattan_distance = 0;
+  for (const auto& point : control_points_) {
+    current_delta_x += point.first;
+    current_delta_y += point.second;
+    manhattan_distance += abs(current_delta_x) + abs(current_delta_y);
+    JXL_RETURN_IF_ERROR(
+        ValidateSplinePointPos(current_delta_x, current_delta_y));
+    current_x += current_delta_x;
+    current_y += current_delta_y;
+    JXL_RETURN_IF_ERROR(ValidateSplinePointPos(current_x, current_y));
+    result.control_points.push_back(Spline::Point{
+        static_cast<float>(current_x), static_cast<float>(current_y)});
+  }
+
+  const auto inv_quant = InvAdjustedQuant(quantization_adjustment);
+  for (int c = 0; c < 3; ++c) {
+    for (int i = 0; i < 32; ++i) {
+      const float inv_dct_factor = (i == 0) ? kSqrt0_5 : 1.0f;
+      result.color_dct[c][i] =
+          color_dct_[c][i] * inv_dct_factor * kChannelWeight[c] * inv_quant;
+    }
+  }
+  for (int i = 0; i < 32; ++i) {
+    result.color_dct[0][i] += y_to_x * result.color_dct[1][i];
+    result.color_dct[2][i] += y_to_b * result.color_dct[1][i];
+  }
+  uint64_t width_estimate = 0;
+
+  uint64_t color[3] = {};
+  for (int c = 0; c < 3; ++c) {
+    for (int i = 0; i < 32; ++i) {
+      color[c] +=
+          static_cast<uint64_t>(ceil(inv_quant * std::abs(color_dct_[c][i])));
+    }
+  }
+  color[0] += static_cast<uint64_t>(ceil(abs(y_to_x))) * color[1];
+  color[2] += static_cast<uint64_t>(ceil(abs(y_to_b))) * color[1];
+  // This is not taking kChannelWeight into account, but up to constant factors
+  // it gives an indication of the influence of the color values on the area
+  // that will need to be rendered.
+  uint64_t logcolor = std::max(
+      uint64_t(1),
+      static_cast<uint64_t>(CeilLog2Nonzero(
+          uint64_t(1) + std::max(color[1], std::max(color[0], color[2])))));
+
+  for (int i = 0; i < 32; ++i) {
+    const float inv_dct_factor = (i == 0) ? kSqrt0_5 : 1.0f;
+    result.sigma_dct[i] =
+        sigma_dct_[i] * inv_dct_factor * kChannelWeight[3] * inv_quant;
+    // If we include the factor kChannelWeight[3]=.3333f here, we get a
+    // realistic area estimate. We leave it out to simplify the calculations,
+    // and understand that this way we underestimate the area by a factor of
+    // 1/(0.3333*0.3333). This is taken into account in the limits below.
+    uint64_t weight = std::max(
+        uint64_t(1),
+        static_cast<uint64_t>(ceil(inv_quant * std::abs(sigma_dct_[i]))));
+    width_estimate += weight * weight * logcolor;
+  }
+  *total_estimated_area_reached += (width_estimate * manhattan_distance);
+  if (*total_estimated_area_reached >
+      std::min((1024 * image_size + (uint64_t(1) << 32)),
+               (uint64_t(1) << 42))) {
+    return JXL_FAILURE("Too large total_estimated_area_reached: %" PRIu64,
+                       *total_estimated_area_reached);
+  }
+
+  return true;
+}
+
+Status QuantizedSpline::Decode(const std::vector<uint8_t>& context_map,
+                               ANSSymbolReader* const decoder,
+                               BitReader* const br,
+                               const size_t max_control_points,
+                               size_t* total_num_control_points) {
+  const size_t num_control_points =
+      decoder->ReadHybridUint(kNumControlPointsContext, br, context_map);
+  *total_num_control_points += num_control_points;
+  if (*total_num_control_points > max_control_points) {
+    return JXL_FAILURE("Too many control points: %" PRIuS,
+                       *total_num_control_points);
+  }
+  control_points_.resize(num_control_points);
+  // Maximal image dimension.
+  constexpr int64_t kDeltaLimit = 1u << 30;
+  for (std::pair<int64_t, int64_t>& control_point : control_points_) {
+    control_point.first = UnpackSigned(
+        decoder->ReadHybridUint(kControlPointsContext, br, context_map));
+    control_point.second = UnpackSigned(
+        decoder->ReadHybridUint(kControlPointsContext, br, context_map));
+    // Check delta-deltas are not outrageous; it is not in spec, but there is
+    // no reason to allow larger values.
+    if ((control_point.first >= kDeltaLimit) ||
+        (control_point.first <= -kDeltaLimit) ||
+        (control_point.second >= kDeltaLimit) ||
+        (control_point.second <= -kDeltaLimit)) {
+      return JXL_FAILURE("Spline delta-delta is out of bounds");
+    }
+  }
+
+  const auto decode_dct = [decoder, br, &context_map](int dct[32]) -> Status {
+    for (int i = 0; i < 32; ++i) {
+      dct[i] =
+          UnpackSigned(decoder->ReadHybridUint(kDCTContext, br, context_map));
+    }
+    return true;
+  };
+  for (int c = 0; c < 3; ++c) {
+    JXL_RETURN_IF_ERROR(decode_dct(color_dct_[c]));
+  }
+  JXL_RETURN_IF_ERROR(decode_dct(sigma_dct_));
+  return true;
+}
+
+void Splines::Clear() {
+  quantization_adjustment_ = 0;
+  splines_.clear();
+  starting_points_.clear();
+  segments_.clear();
+  segment_indices_.clear();
+  segment_y_start_.clear();
+}
+
+Status Splines::Decode(jxl::BitReader* br, const size_t num_pixels) {
+  std::vector<uint8_t> context_map;
+  ANSCode code;
+  JXL_RETURN_IF_ERROR(
+      DecodeHistograms(br, kNumSplineContexts, &code, &context_map));
+  ANSSymbolReader decoder(&code, br);
+  const size_t num_splines =
+      1 + decoder.ReadHybridUint(kNumSplinesContext, br, context_map);
+  size_t max_control_points = std::min(
+      kMaxNumControlPoints, num_pixels / kMaxNumControlPointsPerPixelRatio);
+  if (num_splines > max_control_points) {
+    return JXL_FAILURE("Too many splines: %" PRIuS, num_splines);
+  }
+  JXL_RETURN_IF_ERROR(DecodeAllStartingPoints(&starting_points_, br, &decoder,
+                                              context_map, num_splines));
+
+  quantization_adjustment_ = UnpackSigned(
+      decoder.ReadHybridUint(kQuantizationAdjustmentContext, br, context_map));
+
+  splines_.clear();
+  splines_.reserve(num_splines);
+  size_t num_control_points = num_splines;
+  for (size_t i = 0; i < num_splines; ++i) {
+    QuantizedSpline spline;
+    JXL_RETURN_IF_ERROR(spline.Decode(context_map, &decoder, br,
+                                      max_control_points, &num_control_points));
+    splines_.push_back(std::move(spline));
+  }
+
+  JXL_RETURN_IF_ERROR(decoder.CheckANSFinalState());
+
+  if (!HasAny()) {
+    return JXL_FAILURE("Decoded splines but got none");
+  }
+
+  return true;
+}
+
+void Splines::AddTo(Image3F* const opsin, const Rect& opsin_rect,
+                    const Rect& image_rect) const {
+  return Apply</*add=*/true>(opsin, opsin_rect, image_rect);
+}
+void Splines::AddToRow(float* JXL_RESTRICT row_x, float* JXL_RESTRICT row_y,
+                       float* JXL_RESTRICT row_b, const Rect& image_row) const {
+  return ApplyToRow</*add=*/true>(row_x, row_y, row_b, image_row);
+}
+
+void Splines::SubtractFrom(Image3F* const opsin) const {
+  return Apply</*add=*/false>(opsin, Rect(*opsin), Rect(*opsin));
+}
+
+Status Splines::InitializeDrawCache(const size_t image_xsize,
+                                    const size_t image_ysize,
+                                    const ColorCorrelationMap& cmap) {
+  // TODO(veluca): avoid storing segments that are entirely outside image
+  // boundaries.
+  segments_.clear();
+  segment_indices_.clear();
+  segment_y_start_.clear();
+  std::vector<std::pair<size_t, size_t>> segments_by_y;
+  std::vector<Spline::Point> intermediate_points;
+  uint64_t total_estimated_area_reached = 0;
+  std::vector<Spline> splines;
+  for (size_t i = 0; i < splines_.size(); ++i) {
+    Spline spline;
+    JXL_RETURN_IF_ERROR(splines_[i].Dequantize(
+        starting_points_[i], quantization_adjustment_, cmap.YtoXRatio(0),
+        cmap.YtoBRatio(0), image_xsize * image_ysize,
+        &total_estimated_area_reached, spline));
+    if (std::adjacent_find(spline.control_points.begin(),
+                           spline.control_points.end()) !=
+        spline.control_points.end()) {
+      // Otherwise division by zero might occur. Once control points coincide,
+      // the direction of curve is undefined...
+      return JXL_FAILURE(
+          "identical successive control points in spline %" PRIuS, i);
+    }
+    splines.push_back(spline);
+  }
+  // TODO(firsching) Change this into a JXL_FAILURE for level 5 codestreams.
+  if (total_estimated_area_reached >
+      std::min((8 * image_xsize * image_ysize + (uint64_t(1) << 25)),
+               (uint64_t(1) << 30))) {
+    JXL_WARNING(
+        "Large total_estimated_area_reached, expect slower decoding: %" PRIu64,
+        total_estimated_area_reached);
+  }
+
+  for (Spline& spline : splines) {
+    std::vector<std::pair<Spline::Point, float>> points_to_draw;
+    auto add_point = [&](const Spline::Point& point, const float multiplier) {
+      points_to_draw.emplace_back(point, multiplier);
+    };
+    intermediate_points.clear();
+    DrawCentripetalCatmullRomSpline(spline.control_points, intermediate_points);
+    ForEachEquallySpacedPoint(intermediate_points, add_point);
+    const float arc_length =
+        (points_to_draw.size() - 2) * kDesiredRenderingDistance +
+        points_to_draw.back().second;
+    if (arc_length <= 0.f) {
+      // This spline wouldn't have any effect.
+      continue;
+    }
+    HWY_DYNAMIC_DISPATCH(SegmentsFromPoints)
+    (spline, points_to_draw, arc_length, segments_, segments_by_y);
+  }
+
+  // TODO(eustas): consider linear sorting here.
+  std::sort(segments_by_y.begin(), segments_by_y.end());
+  segment_indices_.resize(segments_by_y.size());
+  segment_y_start_.resize(image_ysize + 1);
+  for (size_t i = 0; i < segments_by_y.size(); i++) {
+    segment_indices_[i] = segments_by_y[i].second;
+    size_t y = segments_by_y[i].first;
+    if (y < image_ysize) {
+      segment_y_start_[y + 1]++;
+    }
+  }
+  for (size_t y = 0; y < image_ysize; y++) {
+    segment_y_start_[y + 1] += segment_y_start_[y];
+  }
+  return true;
+}
+
+template <bool add>
+void Splines::ApplyToRow(float* JXL_RESTRICT row_x, float* JXL_RESTRICT row_y,
+                         float* JXL_RESTRICT row_b,
+                         const Rect& image_row) const {
+  if (segments_.empty()) return;
+  JXL_ASSERT(image_row.ysize() == 1);
+  for (size_t iy = 0; iy < image_row.ysize(); iy++) {
+    HWY_DYNAMIC_DISPATCH(DrawSegments)
+    (row_x, row_y, row_b, image_row.Line(iy), add, segments_.data(),
+     segment_indices_.data(), segment_y_start_.data());
+  }
+}
+
+template <bool add>
+void Splines::Apply(Image3F* const opsin, const Rect& opsin_rect,
+                    const Rect& image_rect) const {
+  if (segments_.empty()) return;
+  for (size_t iy = 0; iy < image_rect.ysize(); iy++) {
+    const size_t y0 = opsin_rect.Line(iy).y0();
+    const size_t x0 = opsin_rect.x0();
+    ApplyToRow<add>(opsin->PlaneRow(0, y0) + x0, opsin->PlaneRow(1, y0) + x0,
+                    opsin->PlaneRow(2, y0) + x0, image_rect.Line(iy));
+  }
+}
+
+}  // namespace jxl
+#endif  // HWY_ONCE