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// 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.
//
// Author: Jyrki Alakuijala (jyrki.alakuijala@gmail.com)
//
// The physical architecture of butteraugli is based on the following naming
// convention:
// * Opsin - dynamics of the photosensitive chemicals in the retina
// with their immediate electrical processing
// * Xyb - hybrid opponent/trichromatic color space
// x is roughly red-subtract-green.
// y is yellow.
// b is blue.
// Xyb values are computed from Opsin mixing, not directly from rgb.
// * Mask - for visual masking
// * Hf - color modeling for spatially high-frequency features
// * Lf - color modeling for spatially low-frequency features
// * Diffmap - to cluster and build an image of error between the images
// * Blur - to hold the smoothing code
#include "lib/jxl/butteraugli/butteraugli.h"
#include <jxl/memory_manager.h>
#include <algorithm>
#include <cmath>
#include <cstdint>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <memory>
#include <vector>
#include "lib/jxl/image.h"
#undef HWY_TARGET_INCLUDE
#define HWY_TARGET_INCLUDE "lib/jxl/butteraugli/butteraugli.cc"
#include <hwy/foreach_target.h>
#include "lib/jxl/base/fast_math-inl.h"
#include "lib/jxl/base/rect.h"
#include "lib/jxl/base/status.h"
#include "lib/jxl/convolve.h"
#include "lib/jxl/image_ops.h"
#if BUTTERAUGLI_ENABLE_CHECKS
#include "lib/jxl/base/printf_macros.h"
#endif
#ifndef JXL_BUTTERAUGLI_ONCE
#define JXL_BUTTERAUGLI_ONCE
namespace jxl {
static const double wMfMalta = 37.0819870399;
static const double norm1Mf = 130262059.556;
static const double wMfMaltaX = 8246.75321353;
static const double norm1MfX = 1009002.70582;
static const double wHfMalta = 18.7237414387;
static const double norm1Hf = 4498534.45232;
static const double wHfMaltaX = 6923.99476109;
static const double norm1HfX = 8051.15833247;
static const double wUhfMalta = 1.10039032555;
static const double norm1Uhf = 71.7800275169;
static const double wUhfMaltaX = 173.5;
static const double norm1UhfX = 5.0;
static const double wmul[9] = {
400.0, 1.50815703118, 0,
2150.0, 10.6195433239, 16.2176043152,
29.2353797994, 0.844626970982, 0.703646627719,
};
std::vector<float> ComputeKernel(float sigma) {
const float m = 2.25; // Accuracy increases when m is increased.
const double scaler = -1.0 / (2.0 * sigma * sigma);
const int diff = std::max<int>(1, m * std::fabs(sigma));
std::vector<float> kernel(2 * diff + 1);
for (int i = -diff; i <= diff; ++i) {
kernel[i + diff] = std::exp(scaler * i * i);
}
return kernel;
}
void ConvolveBorderColumn(const ImageF& in, const std::vector<float>& kernel,
const size_t x, float* BUTTERAUGLI_RESTRICT row_out) {
const size_t offset = kernel.size() / 2;
int minx = x < offset ? 0 : x - offset;
int maxx = std::min<int>(in.xsize() - 1, x + offset);
float weight = 0.0f;
for (int j = minx; j <= maxx; ++j) {
weight += kernel[j - x + offset];
}
float scale = 1.0f / weight;
for (size_t y = 0; y < in.ysize(); ++y) {
const float* BUTTERAUGLI_RESTRICT row_in = in.Row(y);
float sum = 0.0f;
for (int j = minx; j <= maxx; ++j) {
sum += row_in[j] * kernel[j - x + offset];
}
row_out[y] = sum * scale;
}
}
// Computes a horizontal convolution and transposes the result.
Status ConvolutionWithTranspose(const ImageF& in,
const std::vector<float>& kernel,
ImageF* BUTTERAUGLI_RESTRICT out) {
JXL_ENSURE(out->xsize() == in.ysize());
JXL_ENSURE(out->ysize() == in.xsize());
const size_t len = kernel.size();
const size_t offset = len / 2;
float weight_no_border = 0.0f;
for (size_t j = 0; j < len; ++j) {
weight_no_border += kernel[j];
}
const float scale_no_border = 1.0f / weight_no_border;
const size_t border1 = std::min(in.xsize(), offset);
const size_t border2 = in.xsize() > offset ? in.xsize() - offset : 0;
std::vector<float> scaled_kernel(len / 2 + 1);
for (size_t i = 0; i <= len / 2; ++i) {
scaled_kernel[i] = kernel[i] * scale_no_border;
}
// middle
switch (len) {
case 7: {
const float sk0 = scaled_kernel[0];
const float sk1 = scaled_kernel[1];
const float sk2 = scaled_kernel[2];
const float sk3 = scaled_kernel[3];
for (size_t y = 0; y < in.ysize(); ++y) {
const float* BUTTERAUGLI_RESTRICT row_in = in.Row(y) + border1 - offset;
for (size_t x = border1; x < border2; ++x, ++row_in) {
const float sum0 = (row_in[0] + row_in[6]) * sk0;
const float sum1 = (row_in[1] + row_in[5]) * sk1;
const float sum2 = (row_in[2] + row_in[4]) * sk2;
const float sum = (row_in[3]) * sk3 + sum0 + sum1 + sum2;
float* BUTTERAUGLI_RESTRICT row_out = out->Row(x);
row_out[y] = sum;
}
}
} break;
case 13: {
for (size_t y = 0; y < in.ysize(); ++y) {
const float* BUTTERAUGLI_RESTRICT row_in = in.Row(y) + border1 - offset;
for (size_t x = border1; x < border2; ++x, ++row_in) {
float sum0 = (row_in[0] + row_in[12]) * scaled_kernel[0];
float sum1 = (row_in[1] + row_in[11]) * scaled_kernel[1];
float sum2 = (row_in[2] + row_in[10]) * scaled_kernel[2];
float sum3 = (row_in[3] + row_in[9]) * scaled_kernel[3];
sum0 += (row_in[4] + row_in[8]) * scaled_kernel[4];
sum1 += (row_in[5] + row_in[7]) * scaled_kernel[5];
const float sum = (row_in[6]) * scaled_kernel[6];
float* BUTTERAUGLI_RESTRICT row_out = out->Row(x);
row_out[y] = sum + sum0 + sum1 + sum2 + sum3;
}
}
break;
}
case 15: {
for (size_t y = 0; y < in.ysize(); ++y) {
const float* BUTTERAUGLI_RESTRICT row_in = in.Row(y) + border1 - offset;
for (size_t x = border1; x < border2; ++x, ++row_in) {
float sum0 = (row_in[0] + row_in[14]) * scaled_kernel[0];
float sum1 = (row_in[1] + row_in[13]) * scaled_kernel[1];
float sum2 = (row_in[2] + row_in[12]) * scaled_kernel[2];
float sum3 = (row_in[3] + row_in[11]) * scaled_kernel[3];
sum0 += (row_in[4] + row_in[10]) * scaled_kernel[4];
sum1 += (row_in[5] + row_in[9]) * scaled_kernel[5];
sum2 += (row_in[6] + row_in[8]) * scaled_kernel[6];
const float sum = (row_in[7]) * scaled_kernel[7];
float* BUTTERAUGLI_RESTRICT row_out = out->Row(x);
row_out[y] = sum + sum0 + sum1 + sum2 + sum3;
}
}
break;
}
case 33: {
for (size_t y = 0; y < in.ysize(); ++y) {
const float* BUTTERAUGLI_RESTRICT row_in = in.Row(y) + border1 - offset;
for (size_t x = border1; x < border2; ++x, ++row_in) {
float sum0 = (row_in[0] + row_in[32]) * scaled_kernel[0];
float sum1 = (row_in[1] + row_in[31]) * scaled_kernel[1];
float sum2 = (row_in[2] + row_in[30]) * scaled_kernel[2];
float sum3 = (row_in[3] + row_in[29]) * scaled_kernel[3];
sum0 += (row_in[4] + row_in[28]) * scaled_kernel[4];
sum1 += (row_in[5] + row_in[27]) * scaled_kernel[5];
sum2 += (row_in[6] + row_in[26]) * scaled_kernel[6];
sum3 += (row_in[7] + row_in[25]) * scaled_kernel[7];
sum0 += (row_in[8] + row_in[24]) * scaled_kernel[8];
sum1 += (row_in[9] + row_in[23]) * scaled_kernel[9];
sum2 += (row_in[10] + row_in[22]) * scaled_kernel[10];
sum3 += (row_in[11] + row_in[21]) * scaled_kernel[11];
sum0 += (row_in[12] + row_in[20]) * scaled_kernel[12];
sum1 += (row_in[13] + row_in[19]) * scaled_kernel[13];
sum2 += (row_in[14] + row_in[18]) * scaled_kernel[14];
sum3 += (row_in[15] + row_in[17]) * scaled_kernel[15];
const float sum = (row_in[16]) * scaled_kernel[16];
float* BUTTERAUGLI_RESTRICT row_out = out->Row(x);
row_out[y] = sum + sum0 + sum1 + sum2 + sum3;
}
}
break;
}
default:
return JXL_UNREACHABLE("kernel size %d not implemented",
static_cast<int>(len));
}
// left border
for (size_t x = 0; x < border1; ++x) {
ConvolveBorderColumn(in, kernel, x, out->Row(x));
}
// right border
for (size_t x = border2; x < in.xsize(); ++x) {
ConvolveBorderColumn(in, kernel, x, out->Row(x));
}
return true;
}
// A blur somewhat similar to a 2D Gaussian blur.
//
// This is a bottleneck because the sigma can be quite large (>7). We can use
// gauss_blur.cc (runtime independent of sigma, closer to a 4*sigma truncated
// Gaussian and our 2.25 in ComputeKernel), but its boundary conditions are
// zero-valued. This leads to noticeable differences at the edges of diffmaps.
// We retain a special case for 5x5 kernels (even faster than gauss_blur),
// optionally use gauss_blur followed by fixup of the borders for large images,
// or fall back to the previous truncated FIR followed by a transpose.
Status Blur(const ImageF& in, float sigma, const ButteraugliParams& params,
BlurTemp* temp, ImageF* out) {
std::vector<float> kernel = ComputeKernel(sigma);
// Separable5 does an in-place convolution, so this fast path is not safe if
// in aliases out.
if (kernel.size() == 5 && &in != out) {
float sum_weights = 0.0f;
for (const float w : kernel) {
sum_weights += w;
}
const float scale = 1.0f / sum_weights;
const float w0 = kernel[2] * scale;
const float w1 = kernel[1] * scale;
const float w2 = kernel[0] * scale;
const WeightsSeparable5 weights = {
{HWY_REP4(w0), HWY_REP4(w1), HWY_REP4(w2)},
{HWY_REP4(w0), HWY_REP4(w1), HWY_REP4(w2)},
};
JXL_RETURN_IF_ERROR(
Separable5(in, Rect(in), weights, /*pool=*/nullptr, out));
return true;
}
ImageF* temp_t;
JXL_RETURN_IF_ERROR(temp->GetTransposed(in, &temp_t));
JXL_RETURN_IF_ERROR(ConvolutionWithTranspose(in, kernel, temp_t));
JXL_RETURN_IF_ERROR(ConvolutionWithTranspose(*temp_t, kernel, out));
return true;
}
// Allows PaddedMaltaUnit to call either function via overloading.
struct MaltaTagLF {};
struct MaltaTag {};
} // namespace jxl
#endif // JXL_BUTTERAUGLI_ONCE
#include <hwy/highway.h>
HWY_BEFORE_NAMESPACE();
namespace jxl {
namespace HWY_NAMESPACE {
// These templates are not found via ADL.
using hwy::HWY_NAMESPACE::Abs;
using hwy::HWY_NAMESPACE::Div;
using hwy::HWY_NAMESPACE::Gt;
using hwy::HWY_NAMESPACE::IfThenElse;
using hwy::HWY_NAMESPACE::IfThenElseZero;
using hwy::HWY_NAMESPACE::Lt;
using hwy::HWY_NAMESPACE::Max;
using hwy::HWY_NAMESPACE::Mul;
using hwy::HWY_NAMESPACE::MulAdd;
using hwy::HWY_NAMESPACE::MulSub;
using hwy::HWY_NAMESPACE::Neg;
using hwy::HWY_NAMESPACE::Sub;
using hwy::HWY_NAMESPACE::Vec;
using hwy::HWY_NAMESPACE::ZeroIfNegative;
template <class D, class V>
HWY_INLINE V MaximumClamp(D d, V v, double kMaxVal) {
static const double kMul = 0.724216145665;
const V mul = Set(d, kMul);
const V maxval = Set(d, kMaxVal);
// If greater than maxval or less than -maxval, replace with if_*.
const V if_pos = MulAdd(Sub(v, maxval), mul, maxval);
const V if_neg = MulSub(Add(v, maxval), mul, maxval);
const V pos_or_v = IfThenElse(Ge(v, maxval), if_pos, v);
return IfThenElse(Lt(v, Neg(maxval)), if_neg, pos_or_v);
}
// Make area around zero less important (remove it).
template <class D, class V>
HWY_INLINE V RemoveRangeAroundZero(const D d, const double kw, const V x) {
const auto w = Set(d, kw);
return IfThenElse(Gt(x, w), Sub(x, w),
IfThenElseZero(Lt(x, Neg(w)), Add(x, w)));
}
// Make area around zero more important (2x it until the limit).
template <class D, class V>
HWY_INLINE V AmplifyRangeAroundZero(const D d, const double kw, const V x) {
const auto w = Set(d, kw);
return IfThenElse(Gt(x, w), Add(x, w),
IfThenElse(Lt(x, Neg(w)), Sub(x, w), Add(x, x)));
}
// XybLowFreqToVals converts from low-frequency XYB space to the 'vals' space.
// Vals space can be converted to L2-norm space (Euclidean and normalized)
// through visual masking.
template <class D, class V>
HWY_INLINE void XybLowFreqToVals(const D d, const V& x, const V& y,
const V& b_arg, V* HWY_RESTRICT valx,
V* HWY_RESTRICT valy, V* HWY_RESTRICT valb) {
static const double xmul_scalar = 33.832837186260;
static const double ymul_scalar = 14.458268100570;
static const double bmul_scalar = 49.87984651440;
static const double y_to_b_mul_scalar = -0.362267051518;
const V xmul = Set(d, xmul_scalar);
const V ymul = Set(d, ymul_scalar);
const V bmul = Set(d, bmul_scalar);
const V y_to_b_mul = Set(d, y_to_b_mul_scalar);
const V b = MulAdd(y_to_b_mul, y, b_arg);
*valb = Mul(b, bmul);
*valx = Mul(x, xmul);
*valy = Mul(y, ymul);
}
void XybLowFreqToVals(Image3F* xyb_lf) {
// Modify range around zero code only concerns the high frequency
// planes and only the X and Y channels.
// Convert low freq xyb to vals space so that we can do a simple squared sum
// diff on the low frequencies later.
const HWY_FULL(float) d;
for (size_t y = 0; y < xyb_lf->ysize(); ++y) {
float* BUTTERAUGLI_RESTRICT row_x = xyb_lf->PlaneRow(0, y);
float* BUTTERAUGLI_RESTRICT row_y = xyb_lf->PlaneRow(1, y);
float* BUTTERAUGLI_RESTRICT row_b = xyb_lf->PlaneRow(2, y);
for (size_t x = 0; x < xyb_lf->xsize(); x += Lanes(d)) {
auto valx = Undefined(d);
auto valy = Undefined(d);
auto valb = Undefined(d);
XybLowFreqToVals(d, Load(d, row_x + x), Load(d, row_y + x),
Load(d, row_b + x), &valx, &valy, &valb);
Store(valx, d, row_x + x);
Store(valy, d, row_y + x);
Store(valb, d, row_b + x);
}
}
}
Status SuppressXByY(const ImageF& in_y, ImageF* HWY_RESTRICT inout_x) {
JXL_ENSURE(SameSize(*inout_x, in_y));
const size_t xsize = in_y.xsize();
const size_t ysize = in_y.ysize();
const HWY_FULL(float) d;
static const double suppress = 46.0;
static const double s = 0.653020556257;
const auto sv = Set(d, s);
const auto one_minus_s = Set(d, 1.0 - s);
const auto ywv = Set(d, suppress);
for (size_t y = 0; y < ysize; ++y) {
const float* HWY_RESTRICT row_y = in_y.ConstRow(y);
float* HWY_RESTRICT row_x = inout_x->Row(y);
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 scaler =
MulAdd(Div(ywv, MulAdd(vy, vy, ywv)), one_minus_s, sv);
Store(Mul(scaler, vx), d, row_x + x);
}
}
return true;
}
void Subtract(const ImageF& a, const ImageF& b, ImageF* c) {
const HWY_FULL(float) d;
for (size_t y = 0; y < a.ysize(); ++y) {
const float* row_a = a.ConstRow(y);
const float* row_b = b.ConstRow(y);
float* row_c = c->Row(y);
for (size_t x = 0; x < a.xsize(); x += Lanes(d)) {
Store(Sub(Load(d, row_a + x), Load(d, row_b + x)), d, row_c + x);
}
}
}
Status SeparateLFAndMF(const ButteraugliParams& params, const Image3F& xyb,
Image3F* lf, Image3F* mf, BlurTemp* blur_temp) {
static const double kSigmaLf = 7.15593339443;
for (int i = 0; i < 3; ++i) {
// Extract lf ...
JXL_RETURN_IF_ERROR(
Blur(xyb.Plane(i), kSigmaLf, params, blur_temp, &lf->Plane(i)));
// ... and keep everything else in mf.
Subtract(xyb.Plane(i), lf->Plane(i), &mf->Plane(i));
}
XybLowFreqToVals(lf);
return true;
}
Status SeparateMFAndHF(const ButteraugliParams& params, Image3F* mf, ImageF* hf,
BlurTemp* blur_temp) {
const HWY_FULL(float) d;
static const double kSigmaHf = 3.22489901262;
const size_t xsize = mf->xsize();
const size_t ysize = mf->ysize();
JxlMemoryManager* memory_manager = mf[0].memory_manager();
JXL_ASSIGN_OR_RETURN(hf[0], ImageF::Create(memory_manager, xsize, ysize));
JXL_ASSIGN_OR_RETURN(hf[1], ImageF::Create(memory_manager, xsize, ysize));
for (int i = 0; i < 3; ++i) {
if (i == 2) {
JXL_RETURN_IF_ERROR(
Blur(mf->Plane(i), kSigmaHf, params, blur_temp, &mf->Plane(i)));
break;
}
for (size_t y = 0; y < ysize; ++y) {
float* BUTTERAUGLI_RESTRICT row_mf = mf->PlaneRow(i, y);
float* BUTTERAUGLI_RESTRICT row_hf = hf[i].Row(y);
for (size_t x = 0; x < xsize; x += Lanes(d)) {
Store(Load(d, row_mf + x), d, row_hf + x);
}
}
JXL_RETURN_IF_ERROR(
Blur(mf->Plane(i), kSigmaHf, params, blur_temp, &mf->Plane(i)));
static const double kRemoveMfRange = 0.29;
static const double kAddMfRange = 0.1;
if (i == 0) {
for (size_t y = 0; y < ysize; ++y) {
float* BUTTERAUGLI_RESTRICT row_mf = mf->PlaneRow(0, y);
float* BUTTERAUGLI_RESTRICT row_hf = hf[0].Row(y);
for (size_t x = 0; x < xsize; x += Lanes(d)) {
auto mf = Load(d, row_mf + x);
auto hf = Sub(Load(d, row_hf + x), mf);
mf = RemoveRangeAroundZero(d, kRemoveMfRange, mf);
Store(mf, d, row_mf + x);
Store(hf, d, row_hf + x);
}
}
} else {
for (size_t y = 0; y < ysize; ++y) {
float* BUTTERAUGLI_RESTRICT row_mf = mf->PlaneRow(1, y);
float* BUTTERAUGLI_RESTRICT row_hf = hf[1].Row(y);
for (size_t x = 0; x < xsize; x += Lanes(d)) {
auto mf = Load(d, row_mf + x);
auto hf = Sub(Load(d, row_hf + x), mf);
mf = AmplifyRangeAroundZero(d, kAddMfRange, mf);
Store(mf, d, row_mf + x);
Store(hf, d, row_hf + x);
}
}
}
}
// Suppress red-green by intensity change in the high freq channels.
JXL_RETURN_IF_ERROR(SuppressXByY(hf[1], &hf[0]));
return true;
}
Status SeparateHFAndUHF(const ButteraugliParams& params, ImageF* hf,
ImageF* uhf, BlurTemp* blur_temp) {
const HWY_FULL(float) d;
const size_t xsize = hf[0].xsize();
const size_t ysize = hf[0].ysize();
JxlMemoryManager* memory_manager = hf[0].memory_manager();
static const double kSigmaUhf = 1.56416327805;
JXL_ASSIGN_OR_RETURN(uhf[0], ImageF::Create(memory_manager, xsize, ysize));
JXL_ASSIGN_OR_RETURN(uhf[1], ImageF::Create(memory_manager, xsize, ysize));
for (int i = 0; i < 2; ++i) {
// Divide hf into hf and uhf.
for (size_t y = 0; y < ysize; ++y) {
float* BUTTERAUGLI_RESTRICT row_uhf = uhf[i].Row(y);
float* BUTTERAUGLI_RESTRICT row_hf = hf[i].Row(y);
for (size_t x = 0; x < xsize; ++x) {
row_uhf[x] = row_hf[x];
}
}
JXL_RETURN_IF_ERROR(Blur(hf[i], kSigmaUhf, params, blur_temp, &hf[i]));
static const double kRemoveHfRange = 1.5;
static const double kAddHfRange = 0.132;
static const double kRemoveUhfRange = 0.04;
static const double kMaxclampHf = 28.4691806922;
static const double kMaxclampUhf = 5.19175294647;
static double kMulYHf = 2.155;
static double kMulYUhf = 2.69313763794;
if (i == 0) {
for (size_t y = 0; y < ysize; ++y) {
float* BUTTERAUGLI_RESTRICT row_uhf = uhf[0].Row(y);
float* BUTTERAUGLI_RESTRICT row_hf = hf[0].Row(y);
for (size_t x = 0; x < xsize; x += Lanes(d)) {
auto hf = Load(d, row_hf + x);
auto uhf = Sub(Load(d, row_uhf + x), hf);
hf = RemoveRangeAroundZero(d, kRemoveHfRange, hf);
uhf = RemoveRangeAroundZero(d, kRemoveUhfRange, uhf);
Store(hf, d, row_hf + x);
Store(uhf, d, row_uhf + x);
}
}
} else {
for (size_t y = 0; y < ysize; ++y) {
float* BUTTERAUGLI_RESTRICT row_uhf = uhf[1].Row(y);
float* BUTTERAUGLI_RESTRICT row_hf = hf[1].Row(y);
for (size_t x = 0; x < xsize; x += Lanes(d)) {
auto hf = Load(d, row_hf + x);
hf = MaximumClamp(d, hf, kMaxclampHf);
auto uhf = Sub(Load(d, row_uhf + x), hf);
uhf = MaximumClamp(d, uhf, kMaxclampUhf);
uhf = Mul(uhf, Set(d, kMulYUhf));
Store(uhf, d, row_uhf + x);
hf = Mul(hf, Set(d, kMulYHf));
hf = AmplifyRangeAroundZero(d, kAddHfRange, hf);
Store(hf, d, row_hf + x);
}
}
}
}
return true;
}
void DeallocateHFAndUHF(ImageF* hf, ImageF* uhf) {
for (int i = 0; i < 2; ++i) {
hf[i] = ImageF();
uhf[i] = ImageF();
}
}
Status SeparateFrequencies(size_t xsize, size_t ysize,
const ButteraugliParams& params, BlurTemp* blur_temp,
const Image3F& xyb, PsychoImage& ps) {
JxlMemoryManager* memory_manager = xyb.memory_manager();
JXL_ASSIGN_OR_RETURN(
ps.lf, Image3F::Create(memory_manager, xyb.xsize(), xyb.ysize()));
JXL_ASSIGN_OR_RETURN(
ps.mf, Image3F::Create(memory_manager, xyb.xsize(), xyb.ysize()));
JXL_RETURN_IF_ERROR(SeparateLFAndMF(params, xyb, &ps.lf, &ps.mf, blur_temp));
JXL_RETURN_IF_ERROR(SeparateMFAndHF(params, &ps.mf, &ps.hf[0], blur_temp));
JXL_RETURN_IF_ERROR(
SeparateHFAndUHF(params, &ps.hf[0], &ps.uhf[0], blur_temp));
return true;
}
namespace {
template <typename V>
BUTTERAUGLI_INLINE V Sum(V a, V b, V c, V d) {
return Add(Add(a, b), Add(c, d));
}
template <typename V>
BUTTERAUGLI_INLINE V Sum(V a, V b, V c, V d, V e) {
return Sum(a, b, c, Add(d, e));
}
template <typename V>
BUTTERAUGLI_INLINE V Sum(V a, V b, V c, V d, V e, V f, V g) {
return Sum(a, b, c, Sum(d, e, f, g));
}
template <typename V>
BUTTERAUGLI_INLINE V Sum(V a, V b, V c, V d, V e, V f, V g, V h, V i) {
return Add(Add(Sum(a, b, c, d), Sum(e, f, g, h)), i);
}
} // namespace
template <class D>
Vec<D> MaltaUnit(MaltaTagLF /*tag*/, const D df,
const float* BUTTERAUGLI_RESTRICT d, const intptr_t xs) {
const intptr_t xs3 = 3 * xs;
const auto center = LoadU(df, d);
// x grows, y constant
const auto sum_yconst = Sum(LoadU(df, d - 4), LoadU(df, d - 2), center,
LoadU(df, d + 2), LoadU(df, d + 4));
// Will return this, sum of all line kernels
auto retval = Mul(sum_yconst, sum_yconst);
{
// y grows, x constant
auto sum = Sum(LoadU(df, d - xs3 - xs), LoadU(df, d - xs - xs), center,
LoadU(df, d + xs + xs), LoadU(df, d + xs3 + xs));
retval = MulAdd(sum, sum, retval);
}
{
// both grow
auto sum = Sum(LoadU(df, d - xs3 - 3), LoadU(df, d - xs - xs - 2), center,
LoadU(df, d + xs + xs + 2), LoadU(df, d + xs3 + 3));
retval = MulAdd(sum, sum, retval);
}
{
// y grows, x shrinks
auto sum = Sum(LoadU(df, d - xs3 + 3), LoadU(df, d - xs - xs + 2), center,
LoadU(df, d + xs + xs - 2), LoadU(df, d + xs3 - 3));
retval = MulAdd(sum, sum, retval);
}
{
// y grows -4 to 4, x shrinks 1 -> -1
auto sum =
Sum(LoadU(df, d - xs3 - xs + 1), LoadU(df, d - xs - xs + 1), center,
LoadU(df, d + xs + xs - 1), LoadU(df, d + xs3 + xs - 1));
retval = MulAdd(sum, sum, retval);
}
{
// y grows -4 to 4, x grows -1 -> 1
auto sum =
Sum(LoadU(df, d - xs3 - xs - 1), LoadU(df, d - xs - xs - 1), center,
LoadU(df, d + xs + xs + 1), LoadU(df, d + xs3 + xs + 1));
retval = MulAdd(sum, sum, retval);
}
{
// x grows -4 to 4, y grows -1 to 1
auto sum = Sum(LoadU(df, d - 4 - xs), LoadU(df, d - 2 - xs), center,
LoadU(df, d + 2 + xs), LoadU(df, d + 4 + xs));
retval = MulAdd(sum, sum, retval);
}
{
// x grows -4 to 4, y shrinks 1 to -1
auto sum = Sum(LoadU(df, d - 4 + xs), LoadU(df, d - 2 + xs), center,
LoadU(df, d + 2 - xs), LoadU(df, d + 4 - xs));
retval = MulAdd(sum, sum, retval);
}
{
/* 0_________
1__*______
2___*_____
3_________
4____0____
5_________
6_____*___
7______*__
8_________ */
auto sum = Sum(LoadU(df, d - xs3 - 2), LoadU(df, d - xs - xs - 1), center,
LoadU(df, d + xs + xs + 1), LoadU(df, d + xs3 + 2));
retval = MulAdd(sum, sum, retval);
}
{
/* 0_________
1______*__
2_____*___
3_________
4____0____
5_________
6___*_____
7__*______
8_________ */
auto sum = Sum(LoadU(df, d - xs3 + 2), LoadU(df, d - xs - xs + 1), center,
LoadU(df, d + xs + xs - 1), LoadU(df, d + xs3 - 2));
retval = MulAdd(sum, sum, retval);
}
{
/* 0_________
1_________
2_*_______
3__*______
4____0____
5______*__
6_______*_
7_________
8_________ */
auto sum = Sum(LoadU(df, d - xs - xs - 3), LoadU(df, d - xs - 2), center,
LoadU(df, d + xs + 2), LoadU(df, d + xs + xs + 3));
retval = MulAdd(sum, sum, retval);
}
{
/* 0_________
1_________
2_______*_
3______*__
4____0____
5__*______
6_*_______
7_________
8_________ */
auto sum = Sum(LoadU(df, d - xs - xs + 3), LoadU(df, d - xs + 2), center,
LoadU(df, d + xs - 2), LoadU(df, d + xs + xs - 3));
retval = MulAdd(sum, sum, retval);
}
{
/* 0_________
1_________
2________*
3______*__
4____0____
5__*______
6*________
7_________
8_________ */
auto sum = Sum(LoadU(df, d + xs + xs - 4), LoadU(df, d + xs - 2), center,
LoadU(df, d - xs + 2), LoadU(df, d - xs - xs + 4));
retval = MulAdd(sum, sum, retval);
}
{
/* 0_________
1_________
2*________
3__*______
4____0____
5______*__
6________*
7_________
8_________ */
auto sum = Sum(LoadU(df, d - xs - xs - 4), LoadU(df, d - xs - 2), center,
LoadU(df, d + xs + 2), LoadU(df, d + xs + xs + 4));
retval = MulAdd(sum, sum, retval);
}
{
/* 0__*______
1_________
2___*_____
3_________
4____0____
5_________
6_____*___
7_________
8______*__ */
auto sum =
Sum(LoadU(df, d - xs3 - xs - 2), LoadU(df, d - xs - xs - 1), center,
LoadU(df, d + xs + xs + 1), LoadU(df, d + xs3 + xs + 2));
retval = MulAdd(sum, sum, retval);
}
{
/* 0______*__
1_________
2_____*___
3_________
4____0____
5_________
6___*_____
7_________
8__*______ */
auto sum =
Sum(LoadU(df, d - xs3 - xs + 2), LoadU(df, d - xs - xs + 1), center,
LoadU(df, d + xs + xs - 1), LoadU(df, d + xs3 + xs - 2));
retval = MulAdd(sum, sum, retval);
}
return retval;
}
template <class D>
Vec<D> MaltaUnit(MaltaTag /*tag*/, const D df,
const float* BUTTERAUGLI_RESTRICT d, const intptr_t xs) {
const intptr_t xs3 = 3 * xs;
const auto center = LoadU(df, d);
// x grows, y constant
const auto sum_yconst =
Sum(LoadU(df, d - 4), LoadU(df, d - 3), LoadU(df, d - 2),
LoadU(df, d - 1), center, LoadU(df, d + 1), LoadU(df, d + 2),
LoadU(df, d + 3), LoadU(df, d + 4));
// Will return this, sum of all line kernels
auto retval = Mul(sum_yconst, sum_yconst);
{
// y grows, x constant
auto sum = Sum(LoadU(df, d - xs3 - xs), LoadU(df, d - xs3),
LoadU(df, d - xs - xs), LoadU(df, d - xs), center,
LoadU(df, d + xs), LoadU(df, d + xs + xs),
LoadU(df, d + xs3), LoadU(df, d + xs3 + xs));
retval = MulAdd(sum, sum, retval);
}
{
// both grow
auto sum = Sum(LoadU(df, d - xs3 - 3), LoadU(df, d - xs - xs - 2),
LoadU(df, d - xs - 1), center, LoadU(df, d + xs + 1),
LoadU(df, d + xs + xs + 2), LoadU(df, d + xs3 + 3));
retval = MulAdd(sum, sum, retval);
}
{
// y grows, x shrinks
auto sum = Sum(LoadU(df, d - xs3 + 3), LoadU(df, d - xs - xs + 2),
LoadU(df, d - xs + 1), center, LoadU(df, d + xs - 1),
LoadU(df, d + xs + xs - 2), LoadU(df, d + xs3 - 3));
retval = MulAdd(sum, sum, retval);
}
{
// y grows -4 to 4, x shrinks 1 -> -1
auto sum = Sum(LoadU(df, d - xs3 - xs + 1), LoadU(df, d - xs3 + 1),
LoadU(df, d - xs - xs + 1), LoadU(df, d - xs), center,
LoadU(df, d + xs), LoadU(df, d + xs + xs - 1),
LoadU(df, d + xs3 - 1), LoadU(df, d + xs3 + xs - 1));
retval = MulAdd(sum, sum, retval);
}
{
// y grows -4 to 4, x grows -1 -> 1
auto sum = Sum(LoadU(df, d - xs3 - xs - 1), LoadU(df, d - xs3 - 1),
LoadU(df, d - xs - xs - 1), LoadU(df, d - xs), center,
LoadU(df, d + xs), LoadU(df, d + xs + xs + 1),
LoadU(df, d + xs3 + 1), LoadU(df, d + xs3 + xs + 1));
retval = MulAdd(sum, sum, retval);
}
{
// x grows -4 to 4, y grows -1 to 1
auto sum =
Sum(LoadU(df, d - 4 - xs), LoadU(df, d - 3 - xs), LoadU(df, d - 2 - xs),
LoadU(df, d - 1), center, LoadU(df, d + 1), LoadU(df, d + 2 + xs),
LoadU(df, d + 3 + xs), LoadU(df, d + 4 + xs));
retval = MulAdd(sum, sum, retval);
}
{
// x grows -4 to 4, y shrinks 1 to -1
auto sum =
Sum(LoadU(df, d - 4 + xs), LoadU(df, d - 3 + xs), LoadU(df, d - 2 + xs),
LoadU(df, d - 1), center, LoadU(df, d + 1), LoadU(df, d + 2 - xs),
LoadU(df, d + 3 - xs), LoadU(df, d + 4 - xs));
retval = MulAdd(sum, sum, retval);
}
{
/* 0_________
1__*______
2___*_____
3___*_____
4____0____
5_____*___
6_____*___
7______*__
8_________ */
auto sum = Sum(LoadU(df, d - xs3 - 2), LoadU(df, d - xs - xs - 1),
LoadU(df, d - xs - 1), center, LoadU(df, d + xs + 1),
LoadU(df, d + xs + xs + 1), LoadU(df, d + xs3 + 2));
retval = MulAdd(sum, sum, retval);
}
{
/* 0_________
1______*__
2_____*___
3_____*___
4____0____
5___*_____
6___*_____
7__*______
8_________ */
auto sum = Sum(LoadU(df, d - xs3 + 2), LoadU(df, d - xs - xs + 1),
LoadU(df, d - xs + 1), center, LoadU(df, d + xs - 1),
LoadU(df, d + xs + xs - 1), LoadU(df, d + xs3 - 2));
retval = MulAdd(sum, sum, retval);
}
{
/* 0_________
1_________
2_*_______
3__**_____
4____0____
5_____**__
6_______*_
7_________
8_________ */
auto sum = Sum(LoadU(df, d - xs - xs - 3), LoadU(df, d - xs - 2),
LoadU(df, d - xs - 1), center, LoadU(df, d + xs + 1),
LoadU(df, d + xs + 2), LoadU(df, d + xs + xs + 3));
retval = MulAdd(sum, sum, retval);
}
{
/* 0_________
1_________
2_______*_
3_____**__
4____0____
5__**_____
6_*_______
7_________
8_________ */
auto sum = Sum(LoadU(df, d - xs - xs + 3), LoadU(df, d - xs + 2),
LoadU(df, d - xs + 1), center, LoadU(df, d + xs - 1),
LoadU(df, d + xs - 2), LoadU(df, d + xs + xs - 3));
retval = MulAdd(sum, sum, retval);
}
{
/* 0_________
1_________
2_________
3______***
4___*0*___
5***______
6_________
7_________
8_________ */
auto sum =
Sum(LoadU(df, d + xs - 4), LoadU(df, d + xs - 3), LoadU(df, d + xs - 2),
LoadU(df, d - 1), center, LoadU(df, d + 1), LoadU(df, d - xs + 2),
LoadU(df, d - xs + 3), LoadU(df, d - xs + 4));
retval = MulAdd(sum, sum, retval);
}
{
/* 0_________
1_________
2_________
3***______
4___*0*___
5______***
6_________
7_________
8_________ */
auto sum =
Sum(LoadU(df, d - xs - 4), LoadU(df, d - xs - 3), LoadU(df, d - xs - 2),
LoadU(df, d - 1), center, LoadU(df, d + 1), LoadU(df, d + xs + 2),
LoadU(df, d + xs + 3), LoadU(df, d + xs + 4));
retval = MulAdd(sum, sum, retval);
}
{
/* 0___*_____
1___*_____
2___*_____
3____*____
4____0____
5____*____
6_____*___
7_____*___
8_____*___ */
auto sum = Sum(LoadU(df, d - xs3 - xs - 1), LoadU(df, d - xs3 - 1),
LoadU(df, d - xs - xs - 1), LoadU(df, d - xs), center,
LoadU(df, d + xs), LoadU(df, d + xs + xs + 1),
LoadU(df, d + xs3 + 1), LoadU(df, d + xs3 + xs + 1));
retval = MulAdd(sum, sum, retval);
}
{
/* 0_____*___
1_____*___
2____ *___
3____*____
4____0____
5____*____
6___*_____
7___*_____
8___*_____ */
auto sum = Sum(LoadU(df, d - xs3 - xs + 1), LoadU(df, d - xs3 + 1),
LoadU(df, d - xs - xs + 1), LoadU(df, d - xs), center,
LoadU(df, d + xs), LoadU(df, d + xs + xs - 1),
LoadU(df, d + xs3 - 1), LoadU(df, d + xs3 + xs - 1));
retval = MulAdd(sum, sum, retval);
}
return retval;
}
// Returns MaltaUnit. Avoids bounds-checks when x0 and y0 are known
// to be far enough from the image borders. "diffs" is a packed image.
template <class Tag>
static BUTTERAUGLI_INLINE float PaddedMaltaUnit(const ImageF& diffs,
const size_t x0,
const size_t y0) {
const float* BUTTERAUGLI_RESTRICT d = diffs.ConstRow(y0) + x0;
const HWY_CAPPED(float, 1) df;
if ((x0 >= 4 && y0 >= 4 && x0 < (diffs.xsize() - 4) &&
y0 < (diffs.ysize() - 4))) {
return GetLane(MaltaUnit(Tag(), df, d, diffs.PixelsPerRow()));
}
float borderimage[12 * 9]; // round up to 4
for (int dy = 0; dy < 9; ++dy) {
int y = y0 + dy - 4;
if (y < 0 || static_cast<size_t>(y) >= diffs.ysize()) {
for (int dx = 0; dx < 12; ++dx) {
borderimage[dy * 12 + dx] = 0.0f;
}
continue;
}
const float* row_diffs = diffs.ConstRow(y);
for (int dx = 0; dx < 9; ++dx) {
int x = x0 + dx - 4;
if (x < 0 || static_cast<size_t>(x) >= diffs.xsize()) {
borderimage[dy * 12 + dx] = 0.0f;
} else {
borderimage[dy * 12 + dx] = row_diffs[x];
}
}
std::fill(borderimage + dy * 12 + 9, borderimage + dy * 12 + 12, 0.0f);
}
return GetLane(MaltaUnit(Tag(), df, &borderimage[4 * 12 + 4], 12));
}
template <class Tag>
static Status MaltaDiffMapT(const Tag tag, const ImageF& lum0,
const ImageF& lum1, const double w_0gt1,
const double w_0lt1, const double norm1,
const double len, const double mulli,
ImageF* HWY_RESTRICT diffs,
ImageF* HWY_RESTRICT block_diff_ac) {
JXL_ENSURE(SameSize(lum0, lum1) && SameSize(lum0, *diffs));
const size_t xsize_ = lum0.xsize();
const size_t ysize_ = lum0.ysize();
const float kWeight0 = 0.5;
const float kWeight1 = 0.33;
const double w_pre0gt1 = mulli * std::sqrt(kWeight0 * w_0gt1) / (len * 2 + 1);
const double w_pre0lt1 = mulli * std::sqrt(kWeight1 * w_0lt1) / (len * 2 + 1);
const float norm2_0gt1 = w_pre0gt1 * norm1;
const float norm2_0lt1 = w_pre0lt1 * norm1;
for (size_t y = 0; y < ysize_; ++y) {
const float* HWY_RESTRICT row0 = lum0.ConstRow(y);
const float* HWY_RESTRICT row1 = lum1.ConstRow(y);
float* HWY_RESTRICT row_diffs = diffs->Row(y);
for (size_t x = 0; x < xsize_; ++x) {
const float absval = 0.5f * (std::abs(row0[x]) + std::abs(row1[x]));
const float diff = row0[x] - row1[x];
const float scaler = norm2_0gt1 / (static_cast<float>(norm1) + absval);
// Primary symmetric quadratic objective.
row_diffs[x] = scaler * diff;
const float scaler2 = norm2_0lt1 / (static_cast<float>(norm1) + absval);
const double fabs0 = std::fabs(row0[x]);
// Secondary half-open quadratic objectives.
const double too_small = 0.55 * fabs0;
const double too_big = 1.05 * fabs0;
if (row0[x] < 0) {
if (row1[x] > -too_small) {
double impact = scaler2 * (row1[x] + too_small);
row_diffs[x] -= impact;
} else if (row1[x] < -too_big) {
double impact = scaler2 * (-row1[x] - too_big);
row_diffs[x] += impact;
}
} else {
if (row1[x] < too_small) {
double impact = scaler2 * (too_small - row1[x]);
row_diffs[x] += impact;
} else if (row1[x] > too_big) {
double impact = scaler2 * (row1[x] - too_big);
row_diffs[x] -= impact;
}
}
}
}
size_t y0 = 0;
// Top
for (; y0 < 4; ++y0) {
float* BUTTERAUGLI_RESTRICT row_diff = block_diff_ac->Row(y0);
for (size_t x0 = 0; x0 < xsize_; ++x0) {
row_diff[x0] += PaddedMaltaUnit<Tag>(*diffs, x0, y0);
}
}
const HWY_FULL(float) df;
const size_t aligned_x = std::max(static_cast<size_t>(4), Lanes(df));
const intptr_t stride = diffs->PixelsPerRow();
// Middle
for (; y0 < ysize_ - 4; ++y0) {
const float* BUTTERAUGLI_RESTRICT row_in = diffs->ConstRow(y0);
float* BUTTERAUGLI_RESTRICT row_diff = block_diff_ac->Row(y0);
size_t x0 = 0;
for (; x0 < aligned_x; ++x0) {
row_diff[x0] += PaddedMaltaUnit<Tag>(*diffs, x0, y0);
}
for (; x0 + Lanes(df) + 4 <= xsize_; x0 += Lanes(df)) {
auto diff = Load(df, row_diff + x0);
diff = Add(diff, MaltaUnit(Tag(), df, row_in + x0, stride));
Store(diff, df, row_diff + x0);
}
for (; x0 < xsize_; ++x0) {
row_diff[x0] += PaddedMaltaUnit<Tag>(*diffs, x0, y0);
}
}
// Bottom
for (; y0 < ysize_; ++y0) {
float* BUTTERAUGLI_RESTRICT row_diff = block_diff_ac->Row(y0);
for (size_t x0 = 0; x0 < xsize_; ++x0) {
row_diff[x0] += PaddedMaltaUnit<Tag>(*diffs, x0, y0);
}
}
return true;
}
// Need non-template wrapper functions for HWY_EXPORT.
Status MaltaDiffMap(const ImageF& lum0, const ImageF& lum1, const double w_0gt1,
const double w_0lt1, const double norm1,
ImageF* HWY_RESTRICT diffs,
ImageF* HWY_RESTRICT block_diff_ac) {
const double len = 3.75;
static const double mulli = 0.39905817637;
JXL_RETURN_IF_ERROR(MaltaDiffMapT(MaltaTag(), lum0, lum1, w_0gt1, w_0lt1,
norm1, len, mulli, diffs, block_diff_ac));
return true;
}
Status MaltaDiffMapLF(const ImageF& lum0, const ImageF& lum1,
const double w_0gt1, const double w_0lt1,
const double norm1, ImageF* HWY_RESTRICT diffs,
ImageF* HWY_RESTRICT block_diff_ac) {
const double len = 3.75;
static const double mulli = 0.611612573796;
JXL_RETURN_IF_ERROR(MaltaDiffMapT(MaltaTagLF(), lum0, lum1, w_0gt1, w_0lt1,
norm1, len, mulli, diffs, block_diff_ac));
return true;
}
void CombineChannelsForMasking(const ImageF* hf, const ImageF* uhf,
ImageF* out) {
// Only X and Y components are involved in masking. B's influence
// is considered less important in the high frequency area, and we
// don't model masking from lower frequency signals.
static const float muls[3] = {
2.5f,
0.4f,
0.4f,
};
// Silly and unoptimized approach here. TODO(jyrki): rework this.
for (size_t y = 0; y < hf[0].ysize(); ++y) {
const float* BUTTERAUGLI_RESTRICT row_y_hf = hf[1].Row(y);
const float* BUTTERAUGLI_RESTRICT row_y_uhf = uhf[1].Row(y);
const float* BUTTERAUGLI_RESTRICT row_x_hf = hf[0].Row(y);
const float* BUTTERAUGLI_RESTRICT row_x_uhf = uhf[0].Row(y);
float* BUTTERAUGLI_RESTRICT row = out->Row(y);
for (size_t x = 0; x < hf[0].xsize(); ++x) {
float xdiff = (row_x_uhf[x] + row_x_hf[x]) * muls[0];
float ydiff = row_y_uhf[x] * muls[1] + row_y_hf[x] * muls[2];
row[x] = xdiff * xdiff + ydiff * ydiff;
row[x] = std::sqrt(row[x]);
}
}
}
void DiffPrecompute(const ImageF& xyb, float mul, float bias_arg, ImageF* out) {
const size_t xsize = xyb.xsize();
const size_t ysize = xyb.ysize();
const float bias = mul * bias_arg;
const float sqrt_bias = std::sqrt(bias);
for (size_t y = 0; y < ysize; ++y) {
const float* BUTTERAUGLI_RESTRICT row_in = xyb.Row(y);
float* BUTTERAUGLI_RESTRICT row_out = out->Row(y);
for (size_t x = 0; x < xsize; ++x) {
// kBias makes sqrt behave more linearly.
row_out[x] = std::sqrt(mul * std::abs(row_in[x]) + bias) - sqrt_bias;
}
}
}
// std::log(80.0) / std::log(255.0);
constexpr float kIntensityTargetNormalizationHack = 0.79079917404f;
static const float kInternalGoodQualityThreshold =
17.83f * kIntensityTargetNormalizationHack;
static const float kGlobalScale = 1.0 / kInternalGoodQualityThreshold;
void StoreMin3(const float v, float& min0, float& min1, float& min2) {
if (v < min2) {
if (v < min0) {
min2 = min1;
min1 = min0;
min0 = v;
} else if (v < min1) {
min2 = min1;
min1 = v;
} else {
min2 = v;
}
}
}
// Look for smooth areas near the area of degradation.
// If the areas area generally smooth, don't do masking.
void FuzzyErosion(const ImageF& from, ImageF* to) {
const size_t xsize = from.xsize();
const size_t ysize = from.ysize();
static const int kStep = 3;
for (size_t y = 0; y < ysize; ++y) {
for (size_t x = 0; x < xsize; ++x) {
float min0 = from.Row(y)[x];
float min1 = 2 * min0;
float min2 = min1;
if (x >= kStep) {
float v = from.Row(y)[x - kStep];
StoreMin3(v, min0, min1, min2);
if (y >= kStep) {
float v = from.Row(y - kStep)[x - kStep];
StoreMin3(v, min0, min1, min2);
}
if (y < ysize - kStep) {
float v = from.Row(y + kStep)[x - kStep];
StoreMin3(v, min0, min1, min2);
}
}
if (x < xsize - kStep) {
float v = from.Row(y)[x + kStep];
StoreMin3(v, min0, min1, min2);
if (y >= kStep) {
float v = from.Row(y - kStep)[x + kStep];
StoreMin3(v, min0, min1, min2);
}
if (y < ysize - kStep) {
float v = from.Row(y + kStep)[x + kStep];
StoreMin3(v, min0, min1, min2);
}
}
if (y >= kStep) {
float v = from.Row(y - kStep)[x];
StoreMin3(v, min0, min1, min2);
}
if (y < ysize - kStep) {
float v = from.Row(y + kStep)[x];
StoreMin3(v, min0, min1, min2);
}
to->Row(y)[x] = (0.45f * min0 + 0.3f * min1 + 0.25f * min2);
}
}
}
// Compute values of local frequency and dc masking based on the activity
// in the two images. img_diff_ac may be null.
Status Mask(const ImageF& mask0, const ImageF& mask1,
const ButteraugliParams& params, BlurTemp* blur_temp,
ImageF* BUTTERAUGLI_RESTRICT mask,
ImageF* BUTTERAUGLI_RESTRICT diff_ac) {
const size_t xsize = mask0.xsize();
const size_t ysize = mask0.ysize();
JxlMemoryManager* memory_manager = mask0.memory_manager();
JXL_ASSIGN_OR_RETURN(*mask, ImageF::Create(memory_manager, xsize, ysize));
static const float kMul = 6.19424080439;
static const float kBias = 12.61050594197;
static const float kRadius = 2.7;
JXL_ASSIGN_OR_RETURN(ImageF diff0,
ImageF::Create(memory_manager, xsize, ysize));
JXL_ASSIGN_OR_RETURN(ImageF diff1,
ImageF::Create(memory_manager, xsize, ysize));
JXL_ASSIGN_OR_RETURN(ImageF blurred0,
ImageF::Create(memory_manager, xsize, ysize));
JXL_ASSIGN_OR_RETURN(ImageF blurred1,
ImageF::Create(memory_manager, xsize, ysize));
DiffPrecompute(mask0, kMul, kBias, &diff0);
DiffPrecompute(mask1, kMul, kBias, &diff1);
JXL_RETURN_IF_ERROR(Blur(diff0, kRadius, params, blur_temp, &blurred0));
FuzzyErosion(blurred0, &diff0);
JXL_RETURN_IF_ERROR(Blur(diff1, kRadius, params, blur_temp, &blurred1));
for (size_t y = 0; y < ysize; ++y) {
for (size_t x = 0; x < xsize; ++x) {
mask->Row(y)[x] = diff0.Row(y)[x];
if (diff_ac != nullptr) {
static const float kMaskToErrorMul = 10.0;
float diff = blurred0.Row(y)[x] - blurred1.Row(y)[x];
diff_ac->Row(y)[x] += kMaskToErrorMul * diff * diff;
}
}
}
return true;
}
// `diff_ac` may be null.
Status MaskPsychoImage(const PsychoImage& pi0, const PsychoImage& pi1,
const size_t xsize, const size_t ysize,
const ButteraugliParams& params, BlurTemp* blur_temp,
ImageF* BUTTERAUGLI_RESTRICT mask,
ImageF* BUTTERAUGLI_RESTRICT diff_ac) {
JxlMemoryManager* memory_manager = pi0.hf[0].memory_manager();
JXL_ASSIGN_OR_RETURN(ImageF mask0,
ImageF::Create(memory_manager, xsize, ysize));
JXL_ASSIGN_OR_RETURN(ImageF mask1,
ImageF::Create(memory_manager, xsize, ysize));
CombineChannelsForMasking(&pi0.hf[0], &pi0.uhf[0], &mask0);
CombineChannelsForMasking(&pi1.hf[0], &pi1.uhf[0], &mask1);
JXL_RETURN_IF_ERROR(Mask(mask0, mask1, params, blur_temp, mask, diff_ac));
return true;
}
double MaskY(double delta) {
static const double offset = 0.829591754942;
static const double scaler = 0.451936922203;
static const double mul = 2.5485944793;
const double c = mul / ((scaler * delta) + offset);
const double retval = kGlobalScale * (1.0 + c);
return retval * retval;
}
double MaskDcY(double delta) {
static const double offset = 0.20025578522;
static const double scaler = 3.87449418804;
static const double mul = 0.505054525019;
const double c = mul / ((scaler * delta) + offset);
const double retval = kGlobalScale * (1.0 + c);
return retval * retval;
}
inline float MaskColor(const float color[3], const float mask) {
return color[0] * mask + color[1] * mask + color[2] * mask;
}
// Diffmap := sqrt of sum{diff images by multiplied by X and Y/B masks}
Status CombineChannelsToDiffmap(const ImageF& mask,
const Image3F& block_diff_dc,
const Image3F& block_diff_ac, float xmul,
ImageF* result) {
JXL_ENSURE(SameSize(mask, *result));
size_t xsize = mask.xsize();
size_t ysize = mask.ysize();
for (size_t y = 0; y < ysize; ++y) {
float* BUTTERAUGLI_RESTRICT row_out = result->Row(y);
for (size_t x = 0; x < xsize; ++x) {
float val = mask.Row(y)[x];
float maskval = MaskY(val);
float dc_maskval = MaskDcY(val);
float diff_dc[3];
float diff_ac[3];
for (int i = 0; i < 3; ++i) {
diff_dc[i] = block_diff_dc.PlaneRow(i, y)[x];
diff_ac[i] = block_diff_ac.PlaneRow(i, y)[x];
}
diff_ac[0] *= xmul;
diff_dc[0] *= xmul;
row_out[x] = std::sqrt(MaskColor(diff_dc, dc_maskval) +
MaskColor(diff_ac, maskval));
}
}
return true;
}
// Adds weighted L2 difference between i0 and i1 to diffmap.
static void L2Diff(const ImageF& i0, const ImageF& i1, const float w,
ImageF* BUTTERAUGLI_RESTRICT diffmap) {
if (w == 0) return;
const HWY_FULL(float) d;
const auto weight = Set(d, w);
for (size_t y = 0; y < i0.ysize(); ++y) {
const float* BUTTERAUGLI_RESTRICT row0 = i0.ConstRow(y);
const float* BUTTERAUGLI_RESTRICT row1 = i1.ConstRow(y);
float* BUTTERAUGLI_RESTRICT row_diff = diffmap->Row(y);
for (size_t x = 0; x < i0.xsize(); x += Lanes(d)) {
const auto diff = Sub(Load(d, row0 + x), Load(d, row1 + x));
const auto diff2 = Mul(diff, diff);
const auto prev = Load(d, row_diff + x);
Store(MulAdd(diff2, weight, prev), d, row_diff + x);
}
}
}
// Initializes diffmap to the weighted L2 difference between i0 and i1.
static void SetL2Diff(const ImageF& i0, const ImageF& i1, const float w,
ImageF* BUTTERAUGLI_RESTRICT diffmap) {
if (w == 0) return;
const HWY_FULL(float) d;
const auto weight = Set(d, w);
for (size_t y = 0; y < i0.ysize(); ++y) {
const float* BUTTERAUGLI_RESTRICT row0 = i0.ConstRow(y);
const float* BUTTERAUGLI_RESTRICT row1 = i1.ConstRow(y);
float* BUTTERAUGLI_RESTRICT row_diff = diffmap->Row(y);
for (size_t x = 0; x < i0.xsize(); x += Lanes(d)) {
const auto diff = Sub(Load(d, row0 + x), Load(d, row1 + x));
const auto diff2 = Mul(diff, diff);
Store(Mul(diff2, weight), d, row_diff + x);
}
}
}
// i0 is the original image.
// i1 is the deformed copy.
static void L2DiffAsymmetric(const ImageF& i0, const ImageF& i1, float w_0gt1,
float w_0lt1,
ImageF* BUTTERAUGLI_RESTRICT diffmap) {
if (w_0gt1 == 0 && w_0lt1 == 0) {
return;
}
const HWY_FULL(float) d;
const auto vw_0gt1 = Set(d, w_0gt1 * 0.8);
const auto vw_0lt1 = Set(d, w_0lt1 * 0.8);
for (size_t y = 0; y < i0.ysize(); ++y) {
const float* BUTTERAUGLI_RESTRICT row0 = i0.Row(y);
const float* BUTTERAUGLI_RESTRICT row1 = i1.Row(y);
float* BUTTERAUGLI_RESTRICT row_diff = diffmap->Row(y);
for (size_t x = 0; x < i0.xsize(); x += Lanes(d)) {
const auto val0 = Load(d, row0 + x);
const auto val1 = Load(d, row1 + x);
// Primary symmetric quadratic objective.
const auto diff = Sub(val0, val1);
auto total = MulAdd(Mul(diff, diff), vw_0gt1, Load(d, row_diff + x));
// Secondary half-open quadratic objectives.
const auto fabs0 = Abs(val0);
const auto too_small = Mul(Set(d, 0.4), fabs0);
const auto too_big = fabs0;
const auto if_neg = IfThenElse(
Gt(val1, Neg(too_small)), Add(val1, too_small),
IfThenElseZero(Lt(val1, Neg(too_big)), Sub(Neg(val1), too_big)));
const auto if_pos =
IfThenElse(Lt(val1, too_small), Sub(too_small, val1),
IfThenElseZero(Gt(val1, too_big), Sub(val1, too_big)));
const auto v = IfThenElse(Lt(val0, Zero(d)), if_neg, if_pos);
total = MulAdd(vw_0lt1, Mul(v, v), total);
Store(total, d, row_diff + x);
}
}
}
// A simple HDR compatible gamma function.
template <class DF, class V>
V Gamma(const DF df, V v) {
// ln(2) constant folded in because we want std::log but have FastLog2f.
const auto kRetMul = Set(df, 19.245013259874995f * 0.693147180559945f);
const auto kRetAdd = Set(df, -23.16046239805755);
// This should happen rarely, but may lead to a NaN in log, which is
// undesirable. Since negative photons don't exist we solve the NaNs by
// clamping here.
v = ZeroIfNegative(v);
const auto biased = Add(v, Set(df, 9.9710635769299145));
const auto log = FastLog2f(df, biased);
// We could fold this into a custom Log2 polynomial, but there would be
// relatively little gain.
return MulAdd(kRetMul, log, kRetAdd);
}
template <bool Clamp, class DF, class V>
BUTTERAUGLI_INLINE void OpsinAbsorbance(const DF df, const V& in0, const V& in1,
const V& in2, V* JXL_RESTRICT out0,
V* JXL_RESTRICT out1,
V* JXL_RESTRICT out2) {
static const double mixi0 = 0.29956550340058319;
static const double mixi1 = 0.63373087833825936;
static const double mixi2 = 0.077705617820981968;
static const double mixi3 = 1.7557483643287353;
static const double mixi4 = 0.22158691104574774;
static const double mixi5 = 0.69391388044116142;
static const double mixi6 = 0.0987313588422;
static const double mixi7 = 1.7557483643287353;
static const double mixi8 = 0.02;
static const double mixi9 = 0.02;
static const double mixi10 = 0.20480129041026129;
static const double mixi11 = 12.226454707163354;
const V mix0 = Set(df, mixi0);
const V mix1 = Set(df, mixi1);
const V mix2 = Set(df, mixi2);
const V mix3 = Set(df, mixi3);
const V mix4 = Set(df, mixi4);
const V mix5 = Set(df, mixi5);
const V mix6 = Set(df, mixi6);
const V mix7 = Set(df, mixi7);
const V mix8 = Set(df, mixi8);
const V mix9 = Set(df, mixi9);
const V mix10 = Set(df, mixi10);
const V mix11 = Set(df, mixi11);
*out0 = MulAdd(mix0, in0, MulAdd(mix1, in1, MulAdd(mix2, in2, mix3)));
*out1 = MulAdd(mix4, in0, MulAdd(mix5, in1, MulAdd(mix6, in2, mix7)));
*out2 = MulAdd(mix8, in0, MulAdd(mix9, in1, MulAdd(mix10, in2, mix11)));
if (Clamp) {
*out0 = Max(*out0, mix3);
*out1 = Max(*out1, mix7);
*out2 = Max(*out2, mix11);
}
}
// `blurred` is a temporary image used inside this function and not returned.
Status OpsinDynamicsImage(const Image3F& rgb, const ButteraugliParams& params,
Image3F* blurred, BlurTemp* blur_temp, Image3F* xyb) {
JXL_ENSURE(blurred != nullptr);
const double kSigma = 1.2;
JXL_RETURN_IF_ERROR(
Blur(rgb.Plane(0), kSigma, params, blur_temp, &blurred->Plane(0)));
JXL_RETURN_IF_ERROR(
Blur(rgb.Plane(1), kSigma, params, blur_temp, &blurred->Plane(1)));
JXL_RETURN_IF_ERROR(
Blur(rgb.Plane(2), kSigma, params, blur_temp, &blurred->Plane(2)));
const HWY_FULL(float) df;
const auto intensity_target_multiplier = Set(df, params.intensity_target);
for (size_t y = 0; y < rgb.ysize(); ++y) {
const float* row_r = rgb.ConstPlaneRow(0, y);
const float* row_g = rgb.ConstPlaneRow(1, y);
const float* row_b = rgb.ConstPlaneRow(2, y);
const float* row_blurred_r = blurred->ConstPlaneRow(0, y);
const float* row_blurred_g = blurred->ConstPlaneRow(1, y);
const float* row_blurred_b = blurred->ConstPlaneRow(2, y);
float* row_out_x = xyb->PlaneRow(0, y);
float* row_out_y = xyb->PlaneRow(1, y);
float* row_out_b = xyb->PlaneRow(2, y);
const auto min = Set(df, 1e-4f);
for (size_t x = 0; x < rgb.xsize(); x += Lanes(df)) {
auto sensitivity0 = Undefined(df);
auto sensitivity1 = Undefined(df);
auto sensitivity2 = Undefined(df);
{
// Calculate sensitivity based on the smoothed image gamma derivative.
auto pre_mixed0 = Undefined(df);
auto pre_mixed1 = Undefined(df);
auto pre_mixed2 = Undefined(df);
OpsinAbsorbance<true>(
df, Mul(Load(df, row_blurred_r + x), intensity_target_multiplier),
Mul(Load(df, row_blurred_g + x), intensity_target_multiplier),
Mul(Load(df, row_blurred_b + x), intensity_target_multiplier),
&pre_mixed0, &pre_mixed1, &pre_mixed2);
pre_mixed0 = Max(pre_mixed0, min);
pre_mixed1 = Max(pre_mixed1, min);
pre_mixed2 = Max(pre_mixed2, min);
sensitivity0 = Div(Gamma(df, pre_mixed0), pre_mixed0);
sensitivity1 = Div(Gamma(df, pre_mixed1), pre_mixed1);
sensitivity2 = Div(Gamma(df, pre_mixed2), pre_mixed2);
sensitivity0 = Max(sensitivity0, min);
sensitivity1 = Max(sensitivity1, min);
sensitivity2 = Max(sensitivity2, min);
}
auto cur_mixed0 = Undefined(df);
auto cur_mixed1 = Undefined(df);
auto cur_mixed2 = Undefined(df);
OpsinAbsorbance<false>(
df, Mul(Load(df, row_r + x), intensity_target_multiplier),
Mul(Load(df, row_g + x), intensity_target_multiplier),
Mul(Load(df, row_b + x), intensity_target_multiplier), &cur_mixed0,
&cur_mixed1, &cur_mixed2);
cur_mixed0 = Mul(cur_mixed0, sensitivity0);
cur_mixed1 = Mul(cur_mixed1, sensitivity1);
cur_mixed2 = Mul(cur_mixed2, sensitivity2);
// This is a kludge. The negative values should be zeroed away before
// blurring. Ideally there would be no negative values in the first place.
const auto min01 = Set(df, 1.7557483643287353f);
const auto min2 = Set(df, 12.226454707163354f);
cur_mixed0 = Max(cur_mixed0, min01);
cur_mixed1 = Max(cur_mixed1, min01);
cur_mixed2 = Max(cur_mixed2, min2);
Store(Sub(cur_mixed0, cur_mixed1), df, row_out_x + x);
Store(Add(cur_mixed0, cur_mixed1), df, row_out_y + x);
Store(cur_mixed2, df, row_out_b + x);
}
}
return true;
}
Status ButteraugliDiffmapInPlace(Image3F& image0, Image3F& image1,
const ButteraugliParams& params,
ImageF& diffmap) {
// image0 and image1 are in linear sRGB color space
const size_t xsize = image0.xsize();
const size_t ysize = image0.ysize();
JxlMemoryManager* memory_manager = image0.memory_manager();
BlurTemp blur_temp;
{
// Convert image0 and image1 to XYB in-place
JXL_ASSIGN_OR_RETURN(Image3F temp,
Image3F::Create(memory_manager, xsize, ysize));
JXL_RETURN_IF_ERROR(
OpsinDynamicsImage(image0, params, &temp, &blur_temp, &image0));
JXL_RETURN_IF_ERROR(
OpsinDynamicsImage(image1, params, &temp, &blur_temp, &image1));
}
// image0 and image1 are in XYB color space
JXL_ASSIGN_OR_RETURN(ImageF block_diff_dc,
ImageF::Create(memory_manager, xsize, ysize));
ZeroFillImage(&block_diff_dc);
{
// separate out LF components from image0 and image1 and compute the dc
// diff image from them
JXL_ASSIGN_OR_RETURN(Image3F lf0,
Image3F::Create(memory_manager, xsize, ysize));
JXL_ASSIGN_OR_RETURN(Image3F lf1,
Image3F::Create(memory_manager, xsize, ysize));
JXL_RETURN_IF_ERROR(
SeparateLFAndMF(params, image0, &lf0, &image0, &blur_temp));
JXL_RETURN_IF_ERROR(
SeparateLFAndMF(params, image1, &lf1, &image1, &blur_temp));
for (size_t c = 0; c < 3; ++c) {
L2Diff(lf0.Plane(c), lf1.Plane(c), wmul[6 + c], &block_diff_dc);
}
}
// image0 and image1 are MF residuals (before blurring) in XYB color space
ImageF hf0[2];
ImageF hf1[2];
JXL_RETURN_IF_ERROR(SeparateMFAndHF(params, &image0, &hf0[0], &blur_temp));
JXL_RETURN_IF_ERROR(SeparateMFAndHF(params, &image1, &hf1[0], &blur_temp));
// image0 and image1 are MF-images in XYB color space
JXL_ASSIGN_OR_RETURN(ImageF block_diff_ac,
ImageF::Create(memory_manager, xsize, ysize));
ZeroFillImage(&block_diff_ac);
// start accumulating ac diff image from MF images
{
JXL_ASSIGN_OR_RETURN(ImageF diffs,
ImageF::Create(memory_manager, xsize, ysize));
JXL_RETURN_IF_ERROR(MaltaDiffMapLF(image0.Plane(1), image1.Plane(1),
wMfMalta, wMfMalta, norm1Mf, &diffs,
&block_diff_ac));
JXL_RETURN_IF_ERROR(MaltaDiffMapLF(image0.Plane(0), image1.Plane(0),
wMfMaltaX, wMfMaltaX, norm1MfX, &diffs,
&block_diff_ac));
}
for (size_t c = 0; c < 3; ++c) {
L2Diff(image0.Plane(c), image1.Plane(c), wmul[3 + c], &block_diff_ac);
}
// we will not need the MF-images and more, so we deallocate them to reduce
// peak memory usage
image0 = Image3F();
image1 = Image3F();
ImageF uhf0[2];
ImageF uhf1[2];
JXL_RETURN_IF_ERROR(SeparateHFAndUHF(params, &hf0[0], &uhf0[0], &blur_temp));
JXL_RETURN_IF_ERROR(SeparateHFAndUHF(params, &hf1[0], &uhf1[0], &blur_temp));
// continue accumulating ac diff image from HF and UHF images
const float hf_asymmetry = params.hf_asymmetry;
{
JXL_ASSIGN_OR_RETURN(ImageF diffs,
ImageF::Create(memory_manager, xsize, ysize));
JXL_RETURN_IF_ERROR(MaltaDiffMap(uhf0[1], uhf1[1], wUhfMalta * hf_asymmetry,
wUhfMalta / hf_asymmetry, norm1Uhf, &diffs,
&block_diff_ac));
JXL_RETURN_IF_ERROR(MaltaDiffMap(
uhf0[0], uhf1[0], wUhfMaltaX * hf_asymmetry, wUhfMaltaX / hf_asymmetry,
norm1UhfX, &diffs, &block_diff_ac));
JXL_RETURN_IF_ERROR(MaltaDiffMapLF(
hf0[1], hf1[1], wHfMalta * std::sqrt(hf_asymmetry),
wHfMalta / std::sqrt(hf_asymmetry), norm1Hf, &diffs, &block_diff_ac));
JXL_RETURN_IF_ERROR(MaltaDiffMapLF(
hf0[0], hf1[0], wHfMaltaX * std::sqrt(hf_asymmetry),
wHfMaltaX / std::sqrt(hf_asymmetry), norm1HfX, &diffs, &block_diff_ac));
}
for (size_t c = 0; c < 2; ++c) {
L2DiffAsymmetric(hf0[c], hf1[c], wmul[c] * hf_asymmetry,
wmul[c] / hf_asymmetry, &block_diff_ac);
}
// compute mask image from HF and UHF X and Y images
JXL_ASSIGN_OR_RETURN(ImageF mask,
ImageF::Create(memory_manager, xsize, ysize));
{
JXL_ASSIGN_OR_RETURN(ImageF mask0,
ImageF::Create(memory_manager, xsize, ysize));
JXL_ASSIGN_OR_RETURN(ImageF mask1,
ImageF::Create(memory_manager, xsize, ysize));
CombineChannelsForMasking(&hf0[0], &uhf0[0], &mask0);
CombineChannelsForMasking(&hf1[0], &uhf1[0], &mask1);
DeallocateHFAndUHF(&hf1[0], &uhf1[0]);
DeallocateHFAndUHF(&hf0[0], &uhf0[0]);
JXL_RETURN_IF_ERROR(
Mask(mask0, mask1, params, &blur_temp, &mask, &block_diff_ac));
}
// compute final diffmap from mask image and ac and dc diff images
JXL_ASSIGN_OR_RETURN(diffmap, ImageF::Create(memory_manager, xsize, ysize));
for (size_t y = 0; y < ysize; ++y) {
const float* row_dc = block_diff_dc.Row(y);
const float* row_ac = block_diff_ac.Row(y);
float* row_out = diffmap.Row(y);
for (size_t x = 0; x < xsize; ++x) {
const float val = mask.Row(y)[x];
row_out[x] = sqrt(row_dc[x] * MaskDcY(val) + row_ac[x] * MaskY(val));
}
}
return true;
}
// NOLINTNEXTLINE(google-readability-namespace-comments)
} // namespace HWY_NAMESPACE
} // namespace jxl
HWY_AFTER_NAMESPACE();
#if HWY_ONCE
namespace jxl {
HWY_EXPORT(SeparateFrequencies); // Local function.
HWY_EXPORT(MaskPsychoImage); // Local function.
HWY_EXPORT(L2DiffAsymmetric); // Local function.
HWY_EXPORT(L2Diff); // Local function.
HWY_EXPORT(SetL2Diff); // Local function.
HWY_EXPORT(CombineChannelsToDiffmap); // Local function.
HWY_EXPORT(MaltaDiffMap); // Local function.
HWY_EXPORT(MaltaDiffMapLF); // Local function.
HWY_EXPORT(OpsinDynamicsImage); // Local function.
HWY_EXPORT(ButteraugliDiffmapInPlace); // Local function.
#if BUTTERAUGLI_ENABLE_CHECKS
static inline bool IsNan(const float x) {
uint32_t bits;
memcpy(&bits, &x, sizeof(bits));
const uint32_t bitmask_exp = 0x7F800000;
return (bits & bitmask_exp) == bitmask_exp && (bits & 0x7FFFFF);
}
static inline bool IsNan(const double x) {
uint64_t bits;
memcpy(&bits, &x, sizeof(bits));
return (0x7ff0000000000001ULL <= bits && bits <= 0x7fffffffffffffffULL) ||
(0xfff0000000000001ULL <= bits && bits <= 0xffffffffffffffffULL);
}
static inline void CheckImage(const ImageF& image, const char* name) {
for (size_t y = 0; y < image.ysize(); ++y) {
const float* BUTTERAUGLI_RESTRICT row = image.Row(y);
for (size_t x = 0; x < image.xsize(); ++x) {
if (IsNan(row[x])) {
printf("NAN: Image %s @ %" PRIuS ",%" PRIuS " (of %" PRIuS ",%" PRIuS
")\n",
name, x, y, image.xsize(), image.ysize());
exit(1);
}
}
}
}
#define CHECK_NAN(x, str) \
do { \
if (IsNan(x)) { \
printf("%d: %s\n", __LINE__, str); \
abort(); \
} \
} while (0)
#define CHECK_IMAGE(image, name) CheckImage(image, name)
#else // BUTTERAUGLI_ENABLE_CHECKS
#define CHECK_NAN(x, str)
#define CHECK_IMAGE(image, name)
#endif // BUTTERAUGLI_ENABLE_CHECKS
// Calculate a 2x2 subsampled image for purposes of recursive butteraugli at
// multiresolution.
static StatusOr<Image3F> SubSample2x(const Image3F& in) {
size_t xs = (in.xsize() + 1) / 2;
size_t ys = (in.ysize() + 1) / 2;
JxlMemoryManager* memory_manager = in.memory_manager();
JXL_ASSIGN_OR_RETURN(Image3F retval, Image3F::Create(memory_manager, xs, ys));
for (size_t c = 0; c < 3; ++c) {
for (size_t y = 0; y < ys; ++y) {
for (size_t x = 0; x < xs; ++x) {
retval.PlaneRow(c, y)[x] = 0;
}
}
}
for (size_t c = 0; c < 3; ++c) {
for (size_t y = 0; y < in.ysize(); ++y) {
for (size_t x = 0; x < in.xsize(); ++x) {
retval.PlaneRow(c, y / 2)[x / 2] += 0.25f * in.PlaneRow(c, y)[x];
}
}
if ((in.xsize() & 1) != 0) {
for (size_t y = 0; y < retval.ysize(); ++y) {
size_t last_column = retval.xsize() - 1;
retval.PlaneRow(c, y)[last_column] *= 2.0f;
}
}
if ((in.ysize() & 1) != 0) {
for (size_t x = 0; x < retval.xsize(); ++x) {
size_t last_row = retval.ysize() - 1;
retval.PlaneRow(c, last_row)[x] *= 2.0f;
}
}
}
return retval;
}
// Supersample src by 2x and add it to dest.
static void AddSupersampled2x(const ImageF& src, float w, ImageF& dest) {
for (size_t y = 0; y < dest.ysize(); ++y) {
for (size_t x = 0; x < dest.xsize(); ++x) {
// There will be less errors from the more averaged images.
// We take it into account to some extent using a scaler.
static const double kHeuristicMixingValue = 0.3;
dest.Row(y)[x] *= 1.0 - kHeuristicMixingValue * w;
dest.Row(y)[x] += w * src.Row(y / 2)[x / 2];
}
}
}
Image3F* ButteraugliComparator::Temp() const {
bool was_in_use = temp_in_use_.test_and_set(std::memory_order_acq_rel);
if (was_in_use) return nullptr;
return &temp_;
}
void ButteraugliComparator::ReleaseTemp() const { temp_in_use_.clear(); }
ButteraugliComparator::ButteraugliComparator(size_t xsize, size_t ysize,
const ButteraugliParams& params)
: xsize_(xsize), ysize_(ysize), params_(params) {}
StatusOr<std::unique_ptr<ButteraugliComparator>> ButteraugliComparator::Make(
const Image3F& rgb0, const ButteraugliParams& params) {
size_t xsize = rgb0.xsize();
size_t ysize = rgb0.ysize();
JxlMemoryManager* memory_manager = rgb0.memory_manager();
std::unique_ptr<ButteraugliComparator> result =
std::unique_ptr<ButteraugliComparator>(
new ButteraugliComparator(xsize, ysize, params));
JXL_ASSIGN_OR_RETURN(result->temp_,
Image3F::Create(memory_manager, xsize, ysize));
if (xsize < 8 || ysize < 8) {
return result;
}
JXL_ASSIGN_OR_RETURN(Image3F xyb0,
Image3F::Create(memory_manager, xsize, ysize));
JXL_RETURN_IF_ERROR(HWY_DYNAMIC_DISPATCH(OpsinDynamicsImage)(
rgb0, params, result->Temp(), &result->blur_temp_, &xyb0));
result->ReleaseTemp();
JXL_RETURN_IF_ERROR(HWY_DYNAMIC_DISPATCH(SeparateFrequencies)(
xsize, ysize, params, &result->blur_temp_, xyb0, result->pi0_));
// Awful recursive construction of samples of different resolution.
// This is an after-thought and possibly somewhat parallel in
// functionality with the PsychoImage multi-resolution approach.
JXL_ASSIGN_OR_RETURN(Image3F subsampledRgb0, SubSample2x(rgb0));
JXL_ASSIGN_OR_RETURN(result->sub_,
ButteraugliComparator::Make(subsampledRgb0, params));
return result;
}
Status ButteraugliComparator::Mask(ImageF* BUTTERAUGLI_RESTRICT mask) const {
return HWY_DYNAMIC_DISPATCH(MaskPsychoImage)(
pi0_, pi0_, xsize_, ysize_, params_, &blur_temp_, mask, nullptr);
}
Status ButteraugliComparator::Diffmap(const Image3F& rgb1,
ImageF& result) const {
JxlMemoryManager* memory_manager = rgb1.memory_manager();
if (xsize_ < 8 || ysize_ < 8) {
ZeroFillImage(&result);
return true;
}
JXL_ASSIGN_OR_RETURN(Image3F xyb1,
Image3F::Create(memory_manager, xsize_, ysize_));
JXL_RETURN_IF_ERROR(HWY_DYNAMIC_DISPATCH(OpsinDynamicsImage)(
rgb1, params_, Temp(), &blur_temp_, &xyb1));
ReleaseTemp();
JXL_RETURN_IF_ERROR(DiffmapOpsinDynamicsImage(xyb1, result));
if (sub_) {
if (sub_->xsize_ < 8 || sub_->ysize_ < 8) {
return true;
}
JXL_ASSIGN_OR_RETURN(
Image3F sub_xyb,
Image3F::Create(memory_manager, sub_->xsize_, sub_->ysize_));
JXL_ASSIGN_OR_RETURN(Image3F subsampledRgb1, SubSample2x(rgb1));
JXL_RETURN_IF_ERROR(HWY_DYNAMIC_DISPATCH(OpsinDynamicsImage)(
subsampledRgb1, params_, sub_->Temp(), &sub_->blur_temp_, &sub_xyb));
sub_->ReleaseTemp();
ImageF subresult;
JXL_RETURN_IF_ERROR(sub_->DiffmapOpsinDynamicsImage(sub_xyb, subresult));
AddSupersampled2x(subresult, 0.5, result);
}
return true;
}
Status ButteraugliComparator::DiffmapOpsinDynamicsImage(const Image3F& xyb1,
ImageF& result) const {
JxlMemoryManager* memory_manager = xyb1.memory_manager();
if (xsize_ < 8 || ysize_ < 8) {
ZeroFillImage(&result);
return true;
}
PsychoImage pi1;
JXL_RETURN_IF_ERROR(HWY_DYNAMIC_DISPATCH(SeparateFrequencies)(
xsize_, ysize_, params_, &blur_temp_, xyb1, pi1));
JXL_ASSIGN_OR_RETURN(result, ImageF::Create(memory_manager, xsize_, ysize_));
return DiffmapPsychoImage(pi1, result);
}
namespace {
Status MaltaDiffMap(const ImageF& lum0, const ImageF& lum1, const double w_0gt1,
const double w_0lt1, const double norm1,
ImageF* HWY_RESTRICT diffs,
Image3F* HWY_RESTRICT block_diff_ac, size_t c) {
return HWY_DYNAMIC_DISPATCH(MaltaDiffMap)(lum0, lum1, w_0gt1, w_0lt1, norm1,
diffs, &block_diff_ac->Plane(c));
}
Status MaltaDiffMapLF(const ImageF& lum0, const ImageF& lum1,
const double w_0gt1, const double w_0lt1,
const double norm1, ImageF* HWY_RESTRICT diffs,
Image3F* HWY_RESTRICT block_diff_ac, size_t c) {
return HWY_DYNAMIC_DISPATCH(MaltaDiffMapLF)(lum0, lum1, w_0gt1, w_0lt1, norm1,
diffs, &block_diff_ac->Plane(c));
}
} // namespace
Status ButteraugliComparator::DiffmapPsychoImage(const PsychoImage& pi1,
ImageF& diffmap) const {
JxlMemoryManager* memory_manager = diffmap.memory_manager();
if (xsize_ < 8 || ysize_ < 8) {
ZeroFillImage(&diffmap);
return true;
}
const float hf_asymmetry_ = params_.hf_asymmetry;
const float xmul_ = params_.xmul;
JXL_ASSIGN_OR_RETURN(ImageF diffs,
ImageF::Create(memory_manager, xsize_, ysize_));
JXL_ASSIGN_OR_RETURN(Image3F block_diff_ac,
Image3F::Create(memory_manager, xsize_, ysize_));
ZeroFillImage(&block_diff_ac);
JXL_RETURN_IF_ERROR(MaltaDiffMap(
pi0_.uhf[1], pi1.uhf[1], wUhfMalta * hf_asymmetry_,
wUhfMalta / hf_asymmetry_, norm1Uhf, &diffs, &block_diff_ac, 1));
JXL_RETURN_IF_ERROR(MaltaDiffMap(
pi0_.uhf[0], pi1.uhf[0], wUhfMaltaX * hf_asymmetry_,
wUhfMaltaX / hf_asymmetry_, norm1UhfX, &diffs, &block_diff_ac, 0));
JXL_RETURN_IF_ERROR(MaltaDiffMapLF(
pi0_.hf[1], pi1.hf[1], wHfMalta * std::sqrt(hf_asymmetry_),
wHfMalta / std::sqrt(hf_asymmetry_), norm1Hf, &diffs, &block_diff_ac, 1));
JXL_RETURN_IF_ERROR(MaltaDiffMapLF(pi0_.hf[0], pi1.hf[0],
wHfMaltaX * std::sqrt(hf_asymmetry_),
wHfMaltaX / std::sqrt(hf_asymmetry_),
norm1HfX, &diffs, &block_diff_ac, 0));
JXL_RETURN_IF_ERROR(MaltaDiffMapLF(pi0_.mf.Plane(1), pi1.mf.Plane(1),
wMfMalta, wMfMalta, norm1Mf, &diffs,
&block_diff_ac, 1));
JXL_RETURN_IF_ERROR(MaltaDiffMapLF(pi0_.mf.Plane(0), pi1.mf.Plane(0),
wMfMaltaX, wMfMaltaX, norm1MfX, &diffs,
&block_diff_ac, 0));
JXL_ASSIGN_OR_RETURN(Image3F block_diff_dc,
Image3F::Create(memory_manager, xsize_, ysize_));
for (size_t c = 0; c < 3; ++c) {
if (c < 2) { // No blue channel error accumulated at HF.
HWY_DYNAMIC_DISPATCH(L2DiffAsymmetric)
(pi0_.hf[c], pi1.hf[c], wmul[c] * hf_asymmetry_, wmul[c] / hf_asymmetry_,
&block_diff_ac.Plane(c));
}
HWY_DYNAMIC_DISPATCH(L2Diff)
(pi0_.mf.Plane(c), pi1.mf.Plane(c), wmul[3 + c], &block_diff_ac.Plane(c));
HWY_DYNAMIC_DISPATCH(SetL2Diff)
(pi0_.lf.Plane(c), pi1.lf.Plane(c), wmul[6 + c], &block_diff_dc.Plane(c));
}
ImageF mask;
JXL_RETURN_IF_ERROR(HWY_DYNAMIC_DISPATCH(MaskPsychoImage)(
pi0_, pi1, xsize_, ysize_, params_, &blur_temp_, &mask,
&block_diff_ac.Plane(1)));
JXL_RETURN_IF_ERROR(HWY_DYNAMIC_DISPATCH(CombineChannelsToDiffmap)(
mask, block_diff_dc, block_diff_ac, xmul_, &diffmap));
return true;
}
double ButteraugliScoreFromDiffmap(const ImageF& diffmap,
const ButteraugliParams* params) {
float retval = 0.0f;
for (size_t y = 0; y < diffmap.ysize(); ++y) {
const float* BUTTERAUGLI_RESTRICT row = diffmap.ConstRow(y);
for (size_t x = 0; x < diffmap.xsize(); ++x) {
retval = std::max(retval, row[x]);
}
}
return retval;
}
Status ButteraugliDiffmap(const Image3F& rgb0, const Image3F& rgb1,
double hf_asymmetry, double xmul, ImageF& diffmap) {
ButteraugliParams params;
params.hf_asymmetry = hf_asymmetry;
params.xmul = xmul;
return ButteraugliDiffmap(rgb0, rgb1, params, diffmap);
}
template <size_t kMax>
bool ButteraugliDiffmapSmall(const Image3F& rgb0, const Image3F& rgb1,
const ButteraugliParams& params, ImageF& diffmap) {
const size_t xsize = rgb0.xsize();
const size_t ysize = rgb0.ysize();
JxlMemoryManager* memory_manager = rgb0.memory_manager();
// Butteraugli values for small (where xsize or ysize is smaller
// than 8 pixels) images are non-sensical, but most likely it is
// less disruptive to try to compute something than just give up.
// Temporarily extend the borders of the image to fit 8 x 8 size.
size_t xborder = xsize < kMax ? (kMax - xsize) / 2 : 0;
size_t yborder = ysize < kMax ? (kMax - ysize) / 2 : 0;
size_t xscaled = std::max<size_t>(kMax, xsize);
size_t yscaled = std::max<size_t>(kMax, ysize);
JXL_ASSIGN_OR_RETURN(Image3F scaled0,
Image3F::Create(memory_manager, xscaled, yscaled));
JXL_ASSIGN_OR_RETURN(Image3F scaled1,
Image3F::Create(memory_manager, xscaled, yscaled));
for (int i = 0; i < 3; ++i) {
for (size_t y = 0; y < yscaled; ++y) {
for (size_t x = 0; x < xscaled; ++x) {
size_t x2 = std::min<size_t>(xsize - 1, x > xborder ? x - xborder : 0);
size_t y2 = std::min<size_t>(ysize - 1, y > yborder ? y - yborder : 0);
scaled0.PlaneRow(i, y)[x] = rgb0.PlaneRow(i, y2)[x2];
scaled1.PlaneRow(i, y)[x] = rgb1.PlaneRow(i, y2)[x2];
}
}
}
ImageF diffmap_scaled;
const bool ok = ButteraugliDiffmap(scaled0, scaled1, params, diffmap_scaled);
JXL_ASSIGN_OR_RETURN(diffmap, ImageF::Create(memory_manager, xsize, ysize));
for (size_t y = 0; y < ysize; ++y) {
for (size_t x = 0; x < xsize; ++x) {
diffmap.Row(y)[x] = diffmap_scaled.Row(y + yborder)[x + xborder];
}
}
return ok;
}
Status ButteraugliDiffmap(const Image3F& rgb0, const Image3F& rgb1,
const ButteraugliParams& params, ImageF& diffmap) {
const size_t xsize = rgb0.xsize();
const size_t ysize = rgb0.ysize();
if (xsize < 1 || ysize < 1) {
return JXL_FAILURE("Zero-sized image");
}
if (!SameSize(rgb0, rgb1)) {
return JXL_FAILURE("Size mismatch");
}
static const int kMax = 8;
if (xsize < kMax || ysize < kMax) {
return ButteraugliDiffmapSmall<kMax>(rgb0, rgb1, params, diffmap);
}
JXL_ASSIGN_OR_RETURN(std::unique_ptr<ButteraugliComparator> butteraugli,
ButteraugliComparator::Make(rgb0, params));
JXL_RETURN_IF_ERROR(butteraugli->Diffmap(rgb1, diffmap));
return true;
}
bool ButteraugliInterface(const Image3F& rgb0, const Image3F& rgb1,
float hf_asymmetry, float xmul, ImageF& diffmap,
double& diffvalue) {
ButteraugliParams params;
params.hf_asymmetry = hf_asymmetry;
params.xmul = xmul;
return ButteraugliInterface(rgb0, rgb1, params, diffmap, diffvalue);
}
bool ButteraugliInterface(const Image3F& rgb0, const Image3F& rgb1,
const ButteraugliParams& params, ImageF& diffmap,
double& diffvalue) {
if (!ButteraugliDiffmap(rgb0, rgb1, params, diffmap)) {
return false;
}
diffvalue = ButteraugliScoreFromDiffmap(diffmap, ¶ms);
return true;
}
Status ButteraugliInterfaceInPlace(Image3F&& rgb0, Image3F&& rgb1,
const ButteraugliParams& params,
ImageF& diffmap, double& diffvalue) {
const size_t xsize = rgb0.xsize();
const size_t ysize = rgb0.ysize();
if (xsize < 1 || ysize < 1) {
return JXL_FAILURE("Zero-sized image");
}
if (!SameSize(rgb0, rgb1)) {
return JXL_FAILURE("Size mismatch");
}
static const int kMax = 8;
if (xsize < kMax || ysize < kMax) {
bool ok = ButteraugliDiffmapSmall<kMax>(rgb0, rgb1, params, diffmap);
diffvalue = ButteraugliScoreFromDiffmap(diffmap, ¶ms);
return ok;
}
ImageF subdiffmap;
if (xsize >= 15 && ysize >= 15) {
JXL_ASSIGN_OR_RETURN(Image3F rgb0_sub, SubSample2x(rgb0));
JXL_ASSIGN_OR_RETURN(Image3F rgb1_sub, SubSample2x(rgb1));
JXL_RETURN_IF_ERROR(HWY_DYNAMIC_DISPATCH(ButteraugliDiffmapInPlace)(
rgb0_sub, rgb1_sub, params, subdiffmap));
}
JXL_RETURN_IF_ERROR(HWY_DYNAMIC_DISPATCH(ButteraugliDiffmapInPlace)(
rgb0, rgb1, params, diffmap));
if (xsize >= 15 && ysize >= 15) {
AddSupersampled2x(subdiffmap, 0.5, diffmap);
}
diffvalue = ButteraugliScoreFromDiffmap(diffmap, ¶ms);
return true;
}
double ButteraugliFuzzyClass(double score) {
static const double fuzzy_width_up = 4.8;
static const double fuzzy_width_down = 4.8;
static const double m0 = 2.0;
static const double scaler = 0.7777;
double val;
if (score < 1.0) {
// val in [scaler .. 2.0]
val = m0 / (1.0 + exp((score - 1.0) * fuzzy_width_down));
val -= 1.0; // from [1 .. 2] to [0 .. 1]
val *= 2.0 - scaler; // from [0 .. 1] to [0 .. 2.0 - scaler]
val += scaler; // from [0 .. 2.0 - scaler] to [scaler .. 2.0]
} else {
// val in [0 .. scaler]
val = m0 / (1.0 + exp((score - 1.0) * fuzzy_width_up));
val *= scaler;
}
return val;
}
// #define PRINT_OUT_NORMALIZATION
double ButteraugliFuzzyInverse(double seek) {
double pos = 0;
// NOLINTNEXTLINE(clang-analyzer-security.FloatLoopCounter)
for (double range = 1.0; range >= 1e-10; range *= 0.5) {
double cur = ButteraugliFuzzyClass(pos);
if (cur < seek) {
pos -= range;
} else {
pos += range;
}
}
#ifdef PRINT_OUT_NORMALIZATION
if (seek == 1.0) {
fprintf(stderr, "Fuzzy inverse %g\n", pos);
}
#endif
return pos;
}
#ifdef PRINT_OUT_NORMALIZATION
static double print_out_normalization = ButteraugliFuzzyInverse(1.0);
#endif
namespace {
void ScoreToRgb(double score, double good_threshold, double bad_threshold,
float rgb[3]) {
double heatmap[12][3] = {
{0, 0, 0}, {0, 0, 1},
{0, 1, 1}, {0, 1, 0}, // Good level
{1, 1, 0}, {1, 0, 0}, // Bad level
{1, 0, 1}, {0.5, 0.5, 1.0},
{1.0, 0.5, 0.5}, // Pastel colors for the very bad quality range.
{1.0, 1.0, 0.5}, {1, 1, 1},
{1, 1, 1}, // Last color repeated to have a solid range of white.
};
if (score < good_threshold) {
score = (score / good_threshold) * 0.3;
} else if (score < bad_threshold) {
score = 0.3 +
(score - good_threshold) / (bad_threshold - good_threshold) * 0.15;
} else {
score = 0.45 + (score - bad_threshold) / (bad_threshold * 12) * 0.5;
}
static const int kTableSize = sizeof(heatmap) / sizeof(heatmap[0]);
score = std::min<double>(std::max<double>(score * (kTableSize - 1), 0.0),
kTableSize - 2);
int ix = static_cast<int>(score);
ix = std::min(std::max(0, ix), kTableSize - 2); // Handle NaN
double mix = score - ix;
for (int i = 0; i < 3; ++i) {
double v = mix * heatmap[ix + 1][i] + (1 - mix) * heatmap[ix][i];
rgb[i] = pow(v, 0.5);
}
}
} // namespace
StatusOr<Image3F> CreateHeatMapImage(const ImageF& distmap,
double good_threshold,
double bad_threshold) {
JxlMemoryManager* memory_manager = distmap.memory_manager();
JXL_ASSIGN_OR_RETURN(
Image3F heatmap,
Image3F::Create(memory_manager, distmap.xsize(), distmap.ysize()));
for (size_t y = 0; y < distmap.ysize(); ++y) {
const float* BUTTERAUGLI_RESTRICT row_distmap = distmap.ConstRow(y);
float* BUTTERAUGLI_RESTRICT row_h0 = heatmap.PlaneRow(0, y);
float* BUTTERAUGLI_RESTRICT row_h1 = heatmap.PlaneRow(1, y);
float* BUTTERAUGLI_RESTRICT row_h2 = heatmap.PlaneRow(2, y);
for (size_t x = 0; x < distmap.xsize(); ++x) {
const float d = row_distmap[x];
float rgb[3];
ScoreToRgb(d, good_threshold, bad_threshold, rgb);
row_h0[x] = rgb[0];
row_h1[x] = rgb[1];
row_h2[x] = rgb[2];
}
}
return heatmap;
}
} // namespace jxl
#endif // HWY_ONCE