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
|
// 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/dec_noise.h"
#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
#include <algorithm>
#include <numeric>
#include <utility>
#undef HWY_TARGET_INCLUDE
#define HWY_TARGET_INCLUDE "lib/jxl/dec_noise.cc"
#include <hwy/foreach_target.h>
#include <hwy/highway.h>
#include "lib/jxl/base/compiler_specific.h"
#include "lib/jxl/chroma_from_luma.h"
#include "lib/jxl/image_ops.h"
#include "lib/jxl/opsin_params.h"
#include "lib/jxl/sanitizers.h"
#include "lib/jxl/xorshift128plus-inl.h"
HWY_BEFORE_NAMESPACE();
namespace jxl {
namespace HWY_NAMESPACE {
// These templates are not found via ADL.
using hwy::HWY_NAMESPACE::ShiftRight;
using hwy::HWY_NAMESPACE::Vec;
using D = HWY_CAPPED(float, kBlockDim);
using DI = hwy::HWY_NAMESPACE::Rebind<int, D>;
using DI8 = hwy::HWY_NAMESPACE::Repartition<uint8_t, D>;
// Converts one vector's worth of random bits to floats in [1, 2).
// NOTE: as the convolution kernel sums to 0, it doesn't matter if inputs are in
// [0, 1) or in [1, 2).
void BitsToFloat(const uint32_t* JXL_RESTRICT random_bits,
float* JXL_RESTRICT floats) {
const HWY_FULL(float) df;
const HWY_FULL(uint32_t) du;
const auto bits = Load(du, random_bits);
// 1.0 + 23 random mantissa bits = [1, 2)
const auto rand12 = BitCast(df, ShiftRight<9>(bits) | Set(du, 0x3F800000));
Store(rand12, df, floats);
}
void RandomImage(Xorshift128Plus* rng, const Rect& rect,
ImageF* JXL_RESTRICT noise) {
const size_t xsize = rect.xsize();
const size_t ysize = rect.ysize();
// May exceed the vector size, hence we have two loops over x below.
constexpr size_t kFloatsPerBatch =
Xorshift128Plus::N * sizeof(uint64_t) / sizeof(float);
HWY_ALIGN uint64_t batch[Xorshift128Plus::N];
const HWY_FULL(float) df;
const size_t N = Lanes(df);
for (size_t y = 0; y < ysize; ++y) {
float* JXL_RESTRICT row = rect.Row(noise, y);
size_t x = 0;
// Only entire batches (avoids exceeding the image padding).
for (; x + kFloatsPerBatch <= xsize; x += kFloatsPerBatch) {
rng->Fill(batch);
for (size_t i = 0; i < kFloatsPerBatch; i += Lanes(df)) {
BitsToFloat(reinterpret_cast<const uint32_t*>(batch) + i, row + x + i);
}
}
// Any remaining pixels, rounded up to vectors (safe due to padding).
rng->Fill(batch);
size_t batch_pos = 0; // < kFloatsPerBatch
for (; x < xsize; x += N) {
BitsToFloat(reinterpret_cast<const uint32_t*>(batch) + batch_pos,
row + x);
batch_pos += N;
}
}
}
// [0, max_value]
template <class D, class V>
static HWY_INLINE V Clamp0ToMax(D d, const V x, const V max_value) {
const auto clamped = Min(x, max_value);
return ZeroIfNegative(clamped);
}
// x is in [0+delta, 1+delta], delta ~= 0.06
template <class StrengthEval>
typename StrengthEval::V NoiseStrength(const StrengthEval& eval,
const typename StrengthEval::V x) {
return Clamp0ToMax(D(), eval(x), Set(D(), 1.0f));
}
// TODO(veluca): SIMD-fy.
class StrengthEvalLut {
public:
using V = Vec<D>;
explicit StrengthEvalLut(const NoiseParams& noise_params)
#if HWY_TARGET == HWY_SCALAR
: noise_params_(noise_params)
#endif
{
#if HWY_TARGET != HWY_SCALAR
uint32_t lut[8];
memcpy(lut, noise_params.lut, sizeof(lut));
for (size_t i = 0; i < 8; i++) {
low16_lut[2 * i] = (lut[i] >> 0) & 0xFF;
low16_lut[2 * i + 1] = (lut[i] >> 8) & 0xFF;
high16_lut[2 * i] = (lut[i] >> 16) & 0xFF;
high16_lut[2 * i + 1] = (lut[i] >> 24) & 0xFF;
}
#endif
}
V operator()(const V vx) const {
constexpr size_t kScale = NoiseParams::kNumNoisePoints - 2;
auto scaled_vx = Max(Zero(D()), vx * Set(D(), kScale));
auto floor_x = Floor(scaled_vx);
auto frac_x = scaled_vx - floor_x;
floor_x = IfThenElse(scaled_vx >= Set(D(), kScale), Set(D(), kScale - 1),
floor_x);
frac_x = IfThenElse(scaled_vx >= Set(D(), kScale), Set(D(), 1), frac_x);
auto floor_x_int = ConvertTo(DI(), floor_x);
#if HWY_TARGET == HWY_SCALAR
auto low = Set(D(), noise_params_.lut[floor_x_int.raw]);
auto hi = Set(D(), noise_params_.lut[floor_x_int.raw + 1]);
#else
// Set each lane's bytes to {0, 0, 2x+1, 2x}.
auto floorx_indices_low =
floor_x_int * Set(DI(), 0x0202) + Set(DI(), 0x0100);
// Set each lane's bytes to {2x+1, 2x, 0, 0}.
auto floorx_indices_hi =
floor_x_int * Set(DI(), 0x02020000) + Set(DI(), 0x01000000);
// load LUT
auto low16 = BitCast(DI(), LoadDup128(DI8(), low16_lut));
auto lowm = Set(DI(), 0xFFFF);
auto hi16 = BitCast(DI(), LoadDup128(DI8(), high16_lut));
auto him = Set(DI(), 0xFFFF0000);
// low = noise_params.lut[floor_x]
auto low =
BitCast(D(), (TableLookupBytes(low16, floorx_indices_low) & lowm) |
(TableLookupBytes(hi16, floorx_indices_hi) & him));
// hi = noise_params.lut[floor_x+1]
floorx_indices_low += Set(DI(), 0x0202);
floorx_indices_hi += Set(DI(), 0x02020000);
auto hi =
BitCast(D(), (TableLookupBytes(low16, floorx_indices_low) & lowm) |
(TableLookupBytes(hi16, floorx_indices_hi) & him));
#endif
return MulAdd(hi - low, frac_x, low);
}
private:
#if HWY_TARGET != HWY_SCALAR
// noise_params.lut transformed into two 16-bit lookup tables.
HWY_ALIGN uint8_t high16_lut[16];
HWY_ALIGN uint8_t low16_lut[16];
#else
const NoiseParams& noise_params_;
#endif
};
template <class D>
void AddNoiseToRGB(const D d, const Vec<D> rnd_noise_r,
const Vec<D> rnd_noise_g, const Vec<D> rnd_noise_cor,
const Vec<D> noise_strength_g, const Vec<D> noise_strength_r,
float ytox, float ytob, float* JXL_RESTRICT out_x,
float* JXL_RESTRICT out_y, float* JXL_RESTRICT out_b) {
const auto kRGCorr = Set(d, 0.9921875f); // 127/128
const auto kRGNCorr = Set(d, 0.0078125f); // 1/128
const auto red_noise = kRGNCorr * rnd_noise_r * noise_strength_r +
kRGCorr * rnd_noise_cor * noise_strength_r;
const auto green_noise = kRGNCorr * rnd_noise_g * noise_strength_g +
kRGCorr * rnd_noise_cor * noise_strength_g;
auto vx = Load(d, out_x);
auto vy = Load(d, out_y);
auto vb = Load(d, out_b);
vx += red_noise - green_noise + Set(d, ytox) * (red_noise + green_noise);
vy += red_noise + green_noise;
vb += Set(d, ytob) * (red_noise + green_noise);
Store(vx, d, out_x);
Store(vy, d, out_y);
Store(vb, d, out_b);
}
void AddNoise(const NoiseParams& noise_params, const Rect& noise_rect,
const Image3F& noise, const Rect& opsin_rect,
const ColorCorrelationMap& cmap, Image3F* opsin) {
if (!noise_params.HasAny()) return;
const StrengthEvalLut noise_model(noise_params);
D d;
const auto half = Set(d, 0.5f);
const size_t xsize = opsin_rect.xsize();
const size_t ysize = opsin_rect.ysize();
// With the prior subtract-random Laplacian approximation, rnd_* ranges were
// about [-1.5, 1.6]; Laplacian3 about doubles this to [-3.6, 3.6], so the
// normalizer is half of what it was before (0.5).
const auto norm_const = Set(d, 0.22f);
float ytox = cmap.YtoXRatio(0);
float ytob = cmap.YtoBRatio(0);
const size_t xsize_v = RoundUpTo(xsize, Lanes(d));
for (size_t y = 0; y < ysize; ++y) {
float* JXL_RESTRICT row_x = opsin_rect.PlaneRow(opsin, 0, y);
float* JXL_RESTRICT row_y = opsin_rect.PlaneRow(opsin, 1, y);
float* JXL_RESTRICT row_b = opsin_rect.PlaneRow(opsin, 2, y);
const float* JXL_RESTRICT row_rnd_r = noise_rect.ConstPlaneRow(noise, 0, y);
const float* JXL_RESTRICT row_rnd_g = noise_rect.ConstPlaneRow(noise, 1, y);
const float* JXL_RESTRICT row_rnd_c = noise_rect.ConstPlaneRow(noise, 2, y);
// Needed by the calls to Floor() in StrengthEvalLut. Only arithmetic and
// shuffles are otherwise done on the data, so this is safe.
msan::UnpoisonMemory(row_x + xsize, (xsize_v - xsize) * sizeof(float));
msan::UnpoisonMemory(row_y + xsize, (xsize_v - xsize) * sizeof(float));
for (size_t x = 0; x < xsize; x += Lanes(d)) {
const auto vx = Load(d, row_x + x);
const auto vy = Load(d, row_y + x);
const auto in_g = vy - vx;
const auto in_r = vy + vx;
const auto noise_strength_g = NoiseStrength(noise_model, in_g * half);
const auto noise_strength_r = NoiseStrength(noise_model, in_r * half);
const auto addit_rnd_noise_red = Load(d, row_rnd_r + x) * norm_const;
const auto addit_rnd_noise_green = Load(d, row_rnd_g + x) * norm_const;
const auto addit_rnd_noise_correlated =
Load(d, row_rnd_c + x) * norm_const;
AddNoiseToRGB(D(), addit_rnd_noise_red, addit_rnd_noise_green,
addit_rnd_noise_correlated, noise_strength_g,
noise_strength_r, ytox, ytob, row_x + x, row_y + x,
row_b + x);
}
msan::PoisonMemory(row_x + xsize, (xsize_v - xsize) * sizeof(float));
msan::PoisonMemory(row_y + xsize, (xsize_v - xsize) * sizeof(float));
msan::PoisonMemory(row_b + xsize, (xsize_v - xsize) * sizeof(float));
}
}
void RandomImage3(size_t seed, const Rect& rect, Image3F* JXL_RESTRICT noise) {
HWY_ALIGN Xorshift128Plus rng(seed);
RandomImage(&rng, rect, &noise->Plane(0));
RandomImage(&rng, rect, &noise->Plane(1));
RandomImage(&rng, rect, &noise->Plane(2));
}
// NOLINTNEXTLINE(google-readability-namespace-comments)
} // namespace HWY_NAMESPACE
} // namespace jxl
HWY_AFTER_NAMESPACE();
#if HWY_ONCE
namespace jxl {
HWY_EXPORT(AddNoise);
void AddNoise(const NoiseParams& noise_params, const Rect& noise_rect,
const Image3F& noise, const Rect& opsin_rect,
const ColorCorrelationMap& cmap, Image3F* opsin) {
return HWY_DYNAMIC_DISPATCH(AddNoise)(noise_params, noise_rect, noise,
opsin_rect, cmap, opsin);
}
HWY_EXPORT(RandomImage3);
void RandomImage3(size_t seed, const Rect& rect, Image3F* JXL_RESTRICT noise) {
return HWY_DYNAMIC_DISPATCH(RandomImage3)(seed, rect, noise);
}
void DecodeFloatParam(float precision, float* val, BitReader* br) {
const int absval_quant = br->ReadFixedBits<10>();
*val = absval_quant / precision;
}
Status DecodeNoise(BitReader* br, NoiseParams* noise_params) {
for (float& i : noise_params->lut) {
DecodeFloatParam(kNoisePrecision, &i, br);
}
return true;
}
} // namespace jxl
#endif // HWY_ONCE
|