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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)
#ifndef LIB_JXL_BUTTERAUGLI_BUTTERAUGLI_H_
#define LIB_JXL_BUTTERAUGLI_BUTTERAUGLI_H_
#include <jxl/memory_manager.h>
#include <atomic>
#include <cstddef>
#include <cstdlib>
#include <cstring>
#include <memory>
#include "lib/jxl/base/compiler_specific.h"
#include "lib/jxl/base/status.h"
#include "lib/jxl/image.h"
#if !defined(BUTTERAUGLI_ENABLE_CHECKS)
#define BUTTERAUGLI_ENABLE_CHECKS 0
#endif
#define BUTTERAUGLI_RESTRICT JXL_RESTRICT
// This is the main interface to butteraugli image similarity
// analysis function.
namespace jxl {
struct ButteraugliParams {
// Multiplier for penalizing new HF artifacts more than blurring away
// features. 1.0=neutral.
float hf_asymmetry = 1.0f;
// Multiplier for the psychovisual difference in the X channel.
float xmul = 1.0f;
// Number of nits that correspond to 1.0f input values.
float intensity_target = 80.0f;
};
// ButteraugliInterface defines the public interface for butteraugli.
//
// It calculates the difference between rgb0 and rgb1.
//
// rgb0 and rgb1 contain the images. rgb0[c][px] and rgb1[c][px] contains
// the red image for c == 0, green for c == 1, blue for c == 2. Location index
// px is calculated as y * xsize + x.
//
// Value of pixels of images rgb0 and rgb1 need to be represented as raw
// intensity. Most image formats store gamma corrected intensity in pixel
// values. This gamma correction has to be removed, by applying the following
// function to values in the 0-1 range:
// butteraugli_val = pow(input_val, gamma);
// A typical value of gamma is 2.2. It is usually stored in the image header.
// Take care not to confuse that value with its inverse. The gamma value should
// be always greater than one.
// Butteraugli does not work as intended if the caller does not perform
// gamma correction.
//
// hf_asymmetry is a multiplier for penalizing new HF artifacts more than
// blurring away features (1.0 -> neutral).
//
// diffmap will contain an image of the size xsize * ysize, containing
// localized differences for values px (indexed with the px the same as rgb0
// and rgb1). diffvalue will give a global score of similarity.
//
// A diffvalue smaller than kButteraugliGood indicates that images can be
// observed as the same image.
// diffvalue larger than kButteraugliBad indicates that a difference between
// the images can be observed.
// A diffvalue between kButteraugliGood and kButteraugliBad indicates that
// a subtle difference can be observed between the images.
//
// Returns true on success.
bool ButteraugliInterface(const Image3F &rgb0, const Image3F &rgb1,
const ButteraugliParams ¶ms, ImageF &diffmap,
double &diffvalue);
// Deprecated (calls the previous function)
bool ButteraugliInterface(const Image3F &rgb0, const Image3F &rgb1,
float hf_asymmetry, float xmul, ImageF &diffmap,
double &diffvalue);
// Same as ButteraugliInterface, but reuses rgb0 and rgb1 for other purposes
// inside the function after they are not needed any more, and it ignores
// params.xmul.
Status ButteraugliInterfaceInPlace(Image3F &&rgb0, Image3F &&rgb1,
const ButteraugliParams ¶ms,
ImageF &diffmap, double &diffvalue);
// Converts the butteraugli score into fuzzy class values that are continuous
// at the class boundary. The class boundary location is based on human
// raters, but the slope is arbitrary. Particularly, it does not reflect
// the expectation value of probabilities of the human raters. It is just
// expected that a smoother class boundary will allow for higher-level
// optimization algorithms to work faster.
//
// Returns 2.0 for a perfect match, and 1.0 for 'ok', 0.0 for bad. Because the
// scoring is fuzzy, a butteraugli score of 0.96 would return a class of
// around 1.9.
double ButteraugliFuzzyClass(double score);
// Input values should be in range 0 (bad) to 2 (good). Use
// kButteraugliNormalization as normalization.
double ButteraugliFuzzyInverse(double seek);
// Implementation details, don't use anything below or your code will
// break in the future.
#ifdef _MSC_VER
#define BUTTERAUGLI_INLINE __forceinline
#else
#define BUTTERAUGLI_INLINE inline
#endif
#ifdef __clang__
// Early versions of Clang did not support __builtin_assume_aligned.
#define BUTTERAUGLI_HAS_ASSUME_ALIGNED __has_builtin(__builtin_assume_aligned)
#elif defined(__GNUC__)
#define BUTTERAUGLI_HAS_ASSUME_ALIGNED 1
#else
#define BUTTERAUGLI_HAS_ASSUME_ALIGNED 0
#endif
// Returns a void* pointer which the compiler then assumes is N-byte aligned.
// Example: float* JXL_RESTRICT aligned = (float*)JXL_ASSUME_ALIGNED(in, 32);
//
// The assignment semantics are required by GCC/Clang. ICC provides an in-place
// __assume_aligned, whereas MSVC's __assume appears unsuitable.
#if BUTTERAUGLI_HAS_ASSUME_ALIGNED
#define BUTTERAUGLI_ASSUME_ALIGNED(ptr, align) \
__builtin_assume_aligned((ptr), (align))
#else
#define BUTTERAUGLI_ASSUME_ALIGNED(ptr, align) (ptr)
#endif // BUTTERAUGLI_HAS_ASSUME_ALIGNED
struct PsychoImage {
ImageF uhf[2]; // XY
ImageF hf[2]; // XY
Image3F mf; // XYB
Image3F lf; // XYB
};
// Blur needs a transposed image.
// Hold it here and only allocate on demand to reduce memory usage.
struct BlurTemp {
Status GetTransposed(const ImageF &in, ImageF **out) {
JxlMemoryManager *memory_manager = in.memory_manager();
if (transposed_temp.xsize() == 0) {
JXL_ASSIGN_OR_RETURN(
transposed_temp,
ImageF::Create(memory_manager, in.ysize(), in.xsize()));
}
*out = &transposed_temp;
return true;
}
ImageF transposed_temp;
};
class ButteraugliComparator {
public:
// Butteraugli is calibrated at xmul = 1.0. We add a multiplier here so that
// we can test the hypothesis that a higher weighing of the X channel would
// improve results at higher Butteraugli values.
virtual ~ButteraugliComparator() = default;
static StatusOr<std::unique_ptr<ButteraugliComparator>> Make(
const Image3F &rgb0, const ButteraugliParams ¶ms);
// Computes the butteraugli map between the original image given in the
// constructor and the distorted image give here.
Status Diffmap(const Image3F &rgb1, ImageF &result) const;
// Same as above, but OpsinDynamicsImage() was already applied.
Status DiffmapOpsinDynamicsImage(const Image3F &xyb1, ImageF &result) const;
// Same as above, but the frequency decomposition was already applied.
Status DiffmapPsychoImage(const PsychoImage &pi1, ImageF &diffmap) const;
Status Mask(ImageF *BUTTERAUGLI_RESTRICT mask) const;
private:
ButteraugliComparator(size_t xsize, size_t ysize,
const ButteraugliParams ¶ms);
Image3F *Temp() const;
void ReleaseTemp() const;
const size_t xsize_;
const size_t ysize_;
ButteraugliParams params_;
PsychoImage pi0_;
// Shared temporary image storage to reduce the number of allocations;
// obtained via Temp(), must call ReleaseTemp when no longer needed.
mutable Image3F temp_;
mutable std::atomic_flag temp_in_use_ = ATOMIC_FLAG_INIT;
mutable BlurTemp blur_temp_;
std::unique_ptr<ButteraugliComparator> sub_;
};
// Deprecated.
Status ButteraugliDiffmap(const Image3F &rgb0, const Image3F &rgb1,
double hf_asymmetry, double xmul, ImageF &diffmap);
Status ButteraugliDiffmap(const Image3F &rgb0, const Image3F &rgb1,
const ButteraugliParams ¶ms, ImageF &diffmap);
double ButteraugliScoreFromDiffmap(const ImageF &diffmap,
const ButteraugliParams *params = nullptr);
// Generate rgb-representation of the distance between two images.
StatusOr<Image3F> CreateHeatMapImage(const ImageF &distmap,
double good_threshold,
double bad_threshold);
} // namespace jxl
#endif // LIB_JXL_BUTTERAUGLI_BUTTERAUGLI_H_