summaryrefslogtreecommitdiffstats
path: root/third_party/jpeg-xl/lib/jxl/splines.cc
blob: fd68c15493c06ce5a7706b14ac92cd70a7592d65 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
// 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 <cinttypes>  // PRIu64
#include <cmath>
#include <limits>

#include "lib/jxl/base/common.h"
#include "lib/jxl/base/printf_macros.h"
#include "lib/jxl/base/rect.h"
#include "lib/jxl/base/status.h"
#include "lib/jxl/chroma_from_luma.h"
#include "lib/jxl/common.h"  // JXL_HIGH_PRECISION
#include "lib/jxl/dct_scales.h"
#include "lib/jxl/dec_ans.h"
#include "lib/jxl/dec_bit_reader.h"
#include "lib/jxl/pack_signed.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/base/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 Dct32& dct, 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.data() + 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, std::llround(segment.center_x - segment.maximum_distance));
  // one-past-the-end
  x1 = std::min<ssize_t>(
      x1, std::llround(segment.center_x + segment.maximum_distance) + 1);
  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 = std::llround(center.y - maximum_distance);
  ssize_t y1 =
      std::llround(center.y + maximum_distance) + 1;  // 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.
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>(std::roundf(starting_point.x));
  int previous_y = static_cast<int>(std::roundf(starting_point.y));
  int previous_delta_x = 0;
  int 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>(std::roundf(it->x));
    const int new_y = static_cast<int>(std::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 {
    // Maximal int representable with float.
    constexpr float kMax = std::numeric_limits<int>::max() - 127;
    constexpr float kMin = -kMax;
    return static_cast<int>(std::roundf(Clamp1(v, kMin, kMax)));
  };

  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 {
  constexpr uint64_t kOne = static_cast<uint64_t>(1);
  const uint64_t area_limit =
      std::min(1024 * image_size + (kOne << 32), kOne << 42);

  result.control_points.clear();
  result.control_points.reserve(control_points_.size() + 1);
  float px = std::roundf(starting_point.x);
  float py = std::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.emplace_back(static_cast<float>(current_x),
                                     static_cast<float>(current_y));
  int current_delta_x = 0;
  int current_delta_y = 0;
  uint64_t manhattan_distance = 0;
  for (const auto& point : control_points_) {
    current_delta_x += point.first;
    current_delta_y += point.second;
    manhattan_distance += std::abs(current_delta_x) + std::abs(current_delta_y);
    if (manhattan_distance > area_limit) {
      return JXL_FAILURE("Too large manhattan_distance reached: %" PRIu64,
                         manhattan_distance);
    }
    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.emplace_back(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>(
          std::ceil(inv_quant * std::abs(color_dct_[c][i])));
    }
  }
  color[0] += static_cast<uint64_t>(std::ceil(std::abs(y_to_x))) * color[1];
  color[2] += static_cast<uint64_t>(std::ceil(std::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.
  const uint64_t max_color = std::max({color[1], color[0], color[2]});
  uint64_t logcolor =
      std::max(kOne, static_cast<uint64_t>(CeilLog2Nonzero(kOne + max_color)));

  const float weight_limit =
      std::ceil(std::sqrt((static_cast<float>(area_limit) / logcolor) /
                          std::max<size_t>(1, manhattan_distance)));

  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.
    float weight_f = std::ceil(inv_quant * std::abs(sigma_dct_[i]));
    uint64_t weight =
        static_cast<uint64_t>(std::min(weight_limit, std::max(1.0f, weight_f)));
    width_estimate += weight * weight * logcolor;
  }
  *total_estimated_area_reached += (width_estimate * manhattan_distance);
  if (*total_estimated_area_reached > area_limit) {
    return JXL_FAILURE("Too large total_estimated_area eached: %" 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);
  if (num_control_points > max_control_points) {
    return JXL_FAILURE("Too many control points: %" PRIuS, num_control_points);
  }
  *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 {
    constexpr int kWeirdNumber = std::numeric_limits<int>::min();
    for (int i = 0; i < 32; ++i) {
      dct[i] =
          UnpackSigned(decoder->ReadHybridUint(kDCTContext, br, context_map));
      if (dct[i] == kWeirdNumber) {
        return JXL_FAILURE("The weird number in spline DCT");
      }
    }
    return true;
  };
  for (auto& dct : color_dct_) {
    JXL_RETURN_IF_ERROR(decode_dct(dct));
  }
  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);
  size_t num_splines =
      decoder.ReadHybridUint(kNumSplinesContext, br, context_map);
  size_t max_control_points = std::min(
      kMaxNumControlPoints, num_pixels / kMaxNumControlPointsPerPixelRatio);
  if (num_splines > max_control_points ||
      num_splines + 1 > max_control_points) {
    return JXL_FAILURE("Too many splines: %" PRIuS, num_splines);
  }
  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 {
  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 {
  ApplyToRow</*add=*/true>(row_x, row_y, row_b, image_row);
}

void Splines::SubtractFrom(Image3F* const opsin) const {
  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 + (static_cast<uint64_t>(1) << 25)),
          (static_cast<uint64_t>(1) << 30))) {
    JXL_WARNING(
        "Large total_estimated_area_reached, expect slower decoding: %" PRIu64,
        total_estimated_area_reached);
#ifdef FUZZING_BUILD_MODE_UNSAFE_FOR_PRODUCTION
    return JXL_FAILURE("Total spline area is too large");
#endif
  }

  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