Source code
Revision control
Copy as Markdown
Other Tools
// 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/enc_adaptive_quantization.h"
#include <jxl/memory_manager.h>
#include <algorithm>
#include <atomic>
#include <cmath>
#include <cstddef>
#include <cstdlib>
#include <string>
#include <vector>
#include "lib/jxl/memory_manager_internal.h"
#undef HWY_TARGET_INCLUDE
#define HWY_TARGET_INCLUDE "lib/jxl/enc_adaptive_quantization.cc"
#include <hwy/foreach_target.h>
#include <hwy/highway.h>
#include "lib/jxl/ac_strategy.h"
#include "lib/jxl/base/common.h"
#include "lib/jxl/base/compiler_specific.h"
#include "lib/jxl/base/data_parallel.h"
#include "lib/jxl/base/fast_math-inl.h"
#include "lib/jxl/base/rect.h"
#include "lib/jxl/base/status.h"
#include "lib/jxl/butteraugli/butteraugli.h"
#include "lib/jxl/convolve.h"
#include "lib/jxl/dec_cache.h"
#include "lib/jxl/dec_group.h"
#include "lib/jxl/enc_aux_out.h"
#include "lib/jxl/enc_butteraugli_comparator.h"
#include "lib/jxl/enc_cache.h"
#include "lib/jxl/enc_debug_image.h"
#include "lib/jxl/enc_group.h"
#include "lib/jxl/enc_modular.h"
#include "lib/jxl/enc_params.h"
#include "lib/jxl/enc_transforms-inl.h"
#include "lib/jxl/epf.h"
#include "lib/jxl/frame_dimensions.h"
#include "lib/jxl/image.h"
#include "lib/jxl/image_bundle.h"
#include "lib/jxl/image_ops.h"
#include "lib/jxl/quant_weights.h"
// Set JXL_DEBUG_ADAPTIVE_QUANTIZATION to 1 to enable debugging.
#ifndef JXL_DEBUG_ADAPTIVE_QUANTIZATION
#define JXL_DEBUG_ADAPTIVE_QUANTIZATION 0
#endif
HWY_BEFORE_NAMESPACE();
namespace jxl {
namespace HWY_NAMESPACE {
namespace {
// These templates are not found via ADL.
using hwy::HWY_NAMESPACE::AbsDiff;
using hwy::HWY_NAMESPACE::Add;
using hwy::HWY_NAMESPACE::And;
using hwy::HWY_NAMESPACE::Gt;
using hwy::HWY_NAMESPACE::IfThenElseZero;
using hwy::HWY_NAMESPACE::Max;
using hwy::HWY_NAMESPACE::Min;
using hwy::HWY_NAMESPACE::Rebind;
using hwy::HWY_NAMESPACE::Sqrt;
using hwy::HWY_NAMESPACE::ZeroIfNegative;
// The following functions modulate an exponent (out_val) and return the updated
// value. Their descriptor is limited to 8 lanes for 8x8 blocks.
// Hack for mask estimation. Eventually replace this code with butteraugli's
// masking.
float ComputeMaskForAcStrategyUse(const float out_val) {
const float kMul = 1.0f;
const float kOffset = 0.001f;
return kMul / (out_val + kOffset);
}
template <class D, class V>
V ComputeMask(const D d, const V out_val) {
const auto kBase = Set(d, -0.7647f);
const auto kMul4 = Set(d, 9.4708735624378946f);
const auto kMul2 = Set(d, 17.35036561631863f);
const auto kOffset2 = Set(d, 302.59587815579727f);
const auto kMul3 = Set(d, 6.7943250517376494f);
const auto kOffset3 = Set(d, 3.7179635626140772f);
const auto kOffset4 = Mul(Set(d, 0.25f), kOffset3);
const auto kMul0 = Set(d, 0.80061762862741759f);
const auto k1 = Set(d, 1.0f);
// Avoid division by zero.
const auto v1 = Max(Mul(out_val, kMul0), Set(d, 1e-3f));
const auto v2 = Div(k1, Add(v1, kOffset2));
const auto v3 = Div(k1, MulAdd(v1, v1, kOffset3));
const auto v4 = Div(k1, MulAdd(v1, v1, kOffset4));
// TODO(jyrki):
// A log or two here could make sense. In butteraugli we have effectively
// log(log(x + C)) for this kind of use, as a single log is used in
// saturating visual masking and here the modulation values are exponential,
// another log would counter that.
return Add(kBase, MulAdd(kMul4, v4, MulAdd(kMul2, v2, Mul(kMul3, v3))));
}
// mul and mul2 represent a scaling difference between jxl and butteraugli.
const float kSGmul = 226.77216153508914f;
const float kSGmul2 = 1.0f / 73.377132366608819f;
const float kLog2 = 0.693147181f;
// Includes correction factor for std::log -> log2.
const float kSGRetMul = kSGmul2 * 18.6580932135f * kLog2;
const float kSGVOffset = 7.7825991679894591f;
template <bool invert, typename D, typename V>
V RatioOfDerivativesOfCubicRootToSimpleGamma(const D d, V v) {
// The opsin space in jxl is the cubic root of photons, i.e., v * v * v
// is related to the number of photons.
//
// SimpleGamma(v * v * v) is the psychovisual space in butteraugli.
// This ratio allows quantization to move from jxl's opsin space to
// butteraugli's log-gamma space.
float kEpsilon = 1e-2;
v = ZeroIfNegative(v);
const auto kNumMul = Set(d, kSGRetMul * 3 * kSGmul);
const auto kVOffset = Set(d, kSGVOffset * kLog2 + kEpsilon);
const auto kDenMul = Set(d, kLog2 * kSGmul);
const auto v2 = Mul(v, v);
const auto num = MulAdd(kNumMul, v2, Set(d, kEpsilon));
const auto den = MulAdd(Mul(kDenMul, v), v2, kVOffset);
return invert ? Div(num, den) : Div(den, num);
}
template <bool invert = false>
float RatioOfDerivativesOfCubicRootToSimpleGamma(float v) {
using DScalar = HWY_CAPPED(float, 1);
auto vscalar = Load(DScalar(), &v);
return GetLane(
RatioOfDerivativesOfCubicRootToSimpleGamma<invert>(DScalar(), vscalar));
}
// TODO(veluca): this function computes an approximation of the derivative of
// SimpleGamma with (f(x+eps)-f(x))/eps. Consider two-sided approximation or
// exact derivatives. For reference, SimpleGamma was:
/*
template <typename D, typename V>
V SimpleGamma(const D d, V v) {
// A simple HDR compatible gamma function.
const auto mul = Set(d, kSGmul);
const auto kRetMul = Set(d, kSGRetMul);
const auto kRetAdd = Set(d, kSGmul2 * -20.2789020414f);
const auto kVOffset = Set(d, kSGVOffset);
v *= mul;
// This should happen rarely, but may lead to a NaN, which is rather
// undesirable. Since negative photons don't exist we solve the NaNs by
// clamping here.
// TODO(veluca): with FastLog2f, this no longer leads to NaNs.
v = ZeroIfNegative(v);
return kRetMul * FastLog2f(d, v + kVOffset) + kRetAdd;
}
*/
template <class D, class V>
V GammaModulation(const D d, const size_t x, const size_t y,
const ImageF& xyb_x, const ImageF& xyb_y, const Rect& rect,
const V out_val) {
const float kBias = 0.16f;
JXL_DASSERT(kBias > jxl::cms::kOpsinAbsorbanceBias[0]);
JXL_DASSERT(kBias > jxl::cms::kOpsinAbsorbanceBias[1]);
JXL_DASSERT(kBias > jxl::cms::kOpsinAbsorbanceBias[2]);
auto overall_ratio = Zero(d);
auto bias = Set(d, kBias);
for (size_t dy = 0; dy < 8; ++dy) {
const float* const JXL_RESTRICT row_in_x = rect.ConstRow(xyb_x, y + dy);
const float* const JXL_RESTRICT row_in_y = rect.ConstRow(xyb_y, y + dy);
for (size_t dx = 0; dx < 8; dx += Lanes(d)) {
const auto iny = Add(Load(d, row_in_y + x + dx), bias);
const auto inx = Load(d, row_in_x + x + dx);
const auto r = Sub(iny, inx);
const auto ratio_r =
RatioOfDerivativesOfCubicRootToSimpleGamma</*invert=*/true>(d, r);
overall_ratio = Add(overall_ratio, ratio_r);
const auto g = Add(iny, inx);
const auto ratio_g =
RatioOfDerivativesOfCubicRootToSimpleGamma</*invert=*/true>(d, g);
overall_ratio = Add(overall_ratio, ratio_g);
}
}
overall_ratio = Mul(SumOfLanes(d, overall_ratio), Set(d, 0.5f / 64));
// ideally -1.0, but likely optimal correction adds some entropy, so slightly
// less than that.
const auto kGamma = Set(d, 0.1005613337192697f);
return MulAdd(kGamma, FastLog2f(d, overall_ratio), out_val);
}
// Change precision in 8x8 blocks that have significant amounts of blue
// content (but are not close to solid blue).
// This is based on the idea that M and L cone activations saturate the
// S (blue) receptors, and the S reception becomes more important when
// both M and L levels are low. In that case M and L receptors may be
// observing S-spectra instead and viewing them with higher spatial
// accuracy, justifying spending more bits here.
template <class D, class V>
V BlueModulation(const D d, const size_t x, const size_t y,
const ImageF& planex, const ImageF& planey,
const ImageF& planeb, const Rect& rect, const V out_val) {
auto sum = Zero(d);
static const float kLimit = 0.027121074570634722;
static const float kOffset = 0.084381641171960495;
for (size_t dy = 0; dy < 8; ++dy) {
const float* JXL_RESTRICT row_in_x = rect.ConstRow(planex, y + dy) + x;
const float* JXL_RESTRICT row_in_y = rect.ConstRow(planey, y + dy) + x;
const float* JXL_RESTRICT row_in_b = rect.ConstRow(planeb, y + dy) + x;
for (size_t dx = 0; dx < 8; dx += Lanes(d)) {
const auto p_x = Load(d, row_in_x + dx);
const auto p_b = Load(d, row_in_b + dx);
const auto p_y_raw = Add(Load(d, row_in_y + dx), Set(d, kOffset));
const auto p_y_effective = Add(p_y_raw, Abs(p_x));
sum = Add(sum,
IfThenElseZero(Gt(p_b, p_y_effective),
Min(Sub(p_b, p_y_effective), Set(d, kLimit))));
}
}
static const float kMul = 0.14207000358439159;
sum = SumOfLanes(d, sum);
float scalar_sum = GetLane(sum);
// If it is all blue, don't boost the quantization.
// All blue likely means low frequency blue. Let's not make the most
// perfect sky ever.
if (scalar_sum >= 32 * kLimit) {
scalar_sum = 64 * kLimit - scalar_sum;
}
static const float kMaxLimit = 15.398788439047934f;
if (scalar_sum >= kMaxLimit * kLimit) {
scalar_sum = kMaxLimit * kLimit;
}
scalar_sum *= kMul;
return Add(Set(d, scalar_sum), out_val);
}
// Change precision in 8x8 blocks that have high frequency content.
template <class D, class V>
V HfModulation(const D d, const size_t x, const size_t y, const ImageF& xyb_y,
const Rect& rect, const V out_val) {
// Zero out the invalid differences for the rightmost value per row.
const Rebind<uint32_t, D> du;
HWY_ALIGN constexpr uint32_t kMaskRight[kBlockDim] = {~0u, ~0u, ~0u, ~0u,
~0u, ~0u, ~0u, 0};
// Sums of deltas of y and x components between (approximate)
// 4-connected pixels.
auto sum_y = Zero(d);
static const float valmin_y = 0.0206;
auto valminv_y = Set(d, valmin_y);
for (size_t dy = 0; dy < 8; ++dy) {
const float* JXL_RESTRICT row_in_y = rect.ConstRow(xyb_y, y + dy) + x;
const float* JXL_RESTRICT row_in_y_next =
dy == 7 ? row_in_y : rect.ConstRow(xyb_y, y + dy + 1) + x;
// In SCALAR, there is no guarantee of having extra row padding.
// Hence, we need to ensure we don't access pixels outside the row itself.
// In SIMD modes, however, rows are padded, so it's safe to access one
// garbage value after the row. The vector then gets masked with kMaskRight
// to remove the influence of that value.
#if HWY_TARGET != HWY_SCALAR
for (size_t dx = 0; dx < 8; dx += Lanes(d)) {
#else
for (size_t dx = 0; dx < 7; dx += Lanes(d)) {
#endif
const auto mask = BitCast(d, Load(du, kMaskRight + dx));
{
const auto p_y = Load(d, row_in_y + dx);
const auto pr_y = LoadU(d, row_in_y + dx + 1);
sum_y = Add(sum_y, And(mask, Min(valminv_y, AbsDiff(p_y, pr_y))));
const auto pd_y = Load(d, row_in_y_next + dx);
sum_y = Add(sum_y, Min(valminv_y, AbsDiff(p_y, pd_y)));
}
}
#if HWY_TARGET == HWY_SCALAR
const auto p_y = Load(d, row_in_y + 7);
const auto pd_y = Load(d, row_in_y_next + 7);
sum_y = Add(sum_y, Min(valminv_y, AbsDiff(p_y, pd_y)));
#endif
}
static const float kMul_y = -0.38;
sum_y = SumOfLanes(d, sum_y);
float scalar_sum_y = GetLane(sum_y);
scalar_sum_y *= kMul_y;
// higher value -> more bpp
float kOffset = 0.42;
scalar_sum_y += kOffset;
return Add(Set(d, scalar_sum_y), out_val);
}
void PerBlockModulations(const float butteraugli_target, const ImageF& xyb_x,
const ImageF& xyb_y, const ImageF& xyb_b,
const Rect& rect_in, const float scale,
const Rect& rect_out, ImageF* out) {
float base_level = 0.48f * scale;
float kDampenRampStart = 2.0f;
float kDampenRampEnd = 14.0f;
float dampen = 1.0f;
if (butteraugli_target >= kDampenRampStart) {
dampen = 1.0f - ((butteraugli_target - kDampenRampStart) /
(kDampenRampEnd - kDampenRampStart));
if (dampen < 0) {
dampen = 0;
}
}
const float mul = scale * dampen;
const float add = (1.0f - dampen) * base_level;
for (size_t iy = rect_out.y0(); iy < rect_out.y1(); iy++) {
const size_t y = iy * 8;
float* const JXL_RESTRICT row_out = out->Row(iy);
const HWY_CAPPED(float, kBlockDim) df;
for (size_t ix = rect_out.x0(); ix < rect_out.x1(); ix++) {
size_t x = ix * 8;
auto out_val = Set(df, row_out[ix]);
out_val = ComputeMask(df, out_val);
out_val = HfModulation(df, x, y, xyb_y, rect_in, out_val);
out_val = GammaModulation(df, x, y, xyb_x, xyb_y, rect_in, out_val);
out_val = BlueModulation(df, x, y, xyb_x, xyb_y, xyb_b, rect_in, out_val);
// We want multiplicative quantization field, so everything
// until this point has been modulating the exponent.
row_out[ix] = FastPow2f(GetLane(out_val) * 1.442695041f) * mul + add;
}
}
}
template <typename D, typename V>
V MaskingSqrt(const D d, V v) {
static const float kLogOffset = 27.505837037000106f;
static const float kMul = 211.66567973503678f;
const auto mul_v = Set(d, kMul * 1e8);
const auto offset_v = Set(d, kLogOffset);
return Mul(Set(d, 0.25f), Sqrt(MulAdd(v, Sqrt(mul_v), offset_v)));
}
float MaskingSqrt(const float v) {
using DScalar = HWY_CAPPED(float, 1);
auto vscalar = Load(DScalar(), &v);
return GetLane(MaskingSqrt(DScalar(), vscalar));
}
void StoreMin4(const float v, float& min0, float& min1, float& min2,
float& min3) {
if (v < min3) {
if (v < min0) {
min3 = min2;
min2 = min1;
min1 = min0;
min0 = v;
} else if (v < min1) {
min3 = min2;
min2 = min1;
min1 = v;
} else if (v < min2) {
min3 = min2;
min2 = v;
} else {
min3 = v;
}
}
}
// Look for smooth areas near the area of degradation.
// If the areas are generally smooth, don't do masking.
// Output is downsampled 2x.
Status FuzzyErosion(const float butteraugli_target, const Rect& from_rect,
const ImageF& from, const Rect& to_rect, ImageF* to) {
const size_t xsize = from.xsize();
const size_t ysize = from.ysize();
constexpr int kStep = 1;
static_assert(kStep == 1, "Step must be 1");
JXL_ENSURE(to_rect.xsize() * 2 == from_rect.xsize());
JXL_ENSURE(to_rect.ysize() * 2 == from_rect.ysize());
static const float kMulBase0 = 0.125;
static const float kMulBase1 = 0.10;
static const float kMulBase2 = 0.09;
static const float kMulBase3 = 0.06;
static const float kMulAdd0 = 0.0;
static const float kMulAdd1 = -0.10;
static const float kMulAdd2 = -0.09;
static const float kMulAdd3 = -0.06;
float mul = 0.0;
if (butteraugli_target < 2.0f) {
mul = (2.0f - butteraugli_target) * (1.0f / 2.0f);
}
float kMul0 = kMulBase0 + mul * kMulAdd0;
float kMul1 = kMulBase1 + mul * kMulAdd1;
float kMul2 = kMulBase2 + mul * kMulAdd2;
float kMul3 = kMulBase3 + mul * kMulAdd3;
static const float kTotal = 0.29959705784054957;
float norm = kTotal / (kMul0 + kMul1 + kMul2 + kMul3);
kMul0 *= norm;
kMul1 *= norm;
kMul2 *= norm;
kMul3 *= norm;
for (size_t fy = 0; fy < from_rect.ysize(); ++fy) {
size_t y = fy + from_rect.y0();
size_t ym1 = y >= kStep ? y - kStep : y;
size_t yp1 = y + kStep < ysize ? y + kStep : y;
const float* rowt = from.Row(ym1);
const float* row = from.Row(y);
const float* rowb = from.Row(yp1);
float* row_out = to_rect.Row(to, fy / 2);
for (size_t fx = 0; fx < from_rect.xsize(); ++fx) {
size_t x = fx + from_rect.x0();
size_t xm1 = x >= kStep ? x - kStep : x;
size_t xp1 = x + kStep < xsize ? x + kStep : x;
float min0 = row[x];
float min1 = row[xm1];
float min2 = row[xp1];
float min3 = rowt[xm1];
// Sort the first four values.
if (min0 > min1) std::swap(min0, min1);
if (min0 > min2) std::swap(min0, min2);
if (min0 > min3) std::swap(min0, min3);
if (min1 > min2) std::swap(min1, min2);
if (min1 > min3) std::swap(min1, min3);
if (min2 > min3) std::swap(min2, min3);
// The remaining five values of a 3x3 neighbourhood.
StoreMin4(rowt[x], min0, min1, min2, min3);
StoreMin4(rowt[xp1], min0, min1, min2, min3);
StoreMin4(rowb[xm1], min0, min1, min2, min3);
StoreMin4(rowb[x], min0, min1, min2, min3);
StoreMin4(rowb[xp1], min0, min1, min2, min3);
float v = kMul0 * min0 + kMul1 * min1 + kMul2 * min2 + kMul3 * min3;
if (fx % 2 == 0 && fy % 2 == 0) {
row_out[fx / 2] = v;
} else {
row_out[fx / 2] += v;
}
}
}
return true;
}
struct AdaptiveQuantizationImpl {
Status PrepareBuffers(JxlMemoryManager* memory_manager, size_t num_threads) {
JXL_ASSIGN_OR_RETURN(
diff_buffer,
ImageF::Create(memory_manager, kEncTileDim + 8, num_threads));
for (size_t i = pre_erosion.size(); i < num_threads; i++) {
JXL_ASSIGN_OR_RETURN(
ImageF tmp,
ImageF::Create(memory_manager, kEncTileDimInBlocks * 2 + 2,
kEncTileDimInBlocks * 2 + 2));
pre_erosion.emplace_back(std::move(tmp));
}
return true;
}
Status ComputeTile(float butteraugli_target, float scale, const Image3F& xyb,
const Rect& rect_in, const Rect& rect_out,
const int thread, ImageF* mask, ImageF* mask1x1) {
JXL_ENSURE(rect_in.x0() % kBlockDim == 0);
JXL_ENSURE(rect_in.y0() % kBlockDim == 0);
const size_t xsize = xyb.xsize();
const size_t ysize = xyb.ysize();
// The XYB gamma is 3.0 to be able to decode faster with two muls.
// Butteraugli's gamma is matching the gamma of human eye, around 2.6.
// We approximate the gamma difference by adding one cubic root into
// the adaptive quantization. This gives us a total gamma of 2.6666
// for quantization uses.
const float match_gamma_offset = 0.019;
const HWY_FULL(float) df;
size_t y_start_1x1 = rect_in.y0() + rect_out.y0() * 8;
size_t y_end_1x1 = y_start_1x1 + rect_out.ysize() * 8;
size_t x_start_1x1 = rect_in.x0() + rect_out.x0() * 8;
size_t x_end_1x1 = x_start_1x1 + rect_out.xsize() * 8;
if (rect_in.x0() != 0 && rect_out.x0() == 0) x_start_1x1 -= 2;
if (rect_in.x1() < xsize && rect_out.x1() * 8 == rect_in.xsize()) {
x_end_1x1 += 2;
}
if (rect_in.y0() != 0 && rect_out.y0() == 0) y_start_1x1 -= 2;
if (rect_in.y1() < ysize && rect_out.y1() * 8 == rect_in.ysize()) {
y_end_1x1 += 2;
}
// Computes image (padded to multiple of 8x8) of local pixel differences.
// Subsample both directions by 4.
// 1x1 Laplacian of intensity.
for (size_t y = y_start_1x1; y < y_end_1x1; ++y) {
const size_t y2 = y + 1 < ysize ? y + 1 : y;
const size_t y1 = y > 0 ? y - 1 : y;
const float* row_in = xyb.ConstPlaneRow(1, y);
const float* row_in1 = xyb.ConstPlaneRow(1, y1);
const float* row_in2 = xyb.ConstPlaneRow(1, y2);
float* mask1x1_out = mask1x1->Row(y);
auto scalar_pixel1x1 = [&](size_t x) {
const size_t x2 = x + 1 < xsize ? x + 1 : x;
const size_t x1 = x > 0 ? x - 1 : x;
const float base =
0.25f * (row_in2[x] + row_in1[x] + row_in[x1] + row_in[x2]);
const float gammac = RatioOfDerivativesOfCubicRootToSimpleGamma(
row_in[x] + match_gamma_offset);
float diff = fabs(gammac * (row_in[x] - base));
static const double kScaler = 1.0;
diff *= kScaler;
diff = log1p(diff);
static const float kMul = 1.0;
static const float kOffset = 0.01;
mask1x1_out[x] = kMul / (diff + kOffset);
};
for (size_t x = x_start_1x1; x < x_end_1x1; ++x) {
scalar_pixel1x1(x);
}
}
size_t y_start = rect_in.y0() + rect_out.y0() * 8;
size_t y_end = y_start + rect_out.ysize() * 8;
size_t x_start = rect_in.x0() + rect_out.x0() * 8;
size_t x_end = x_start + rect_out.xsize() * 8;
if (x_start != 0) x_start -= 4;
if (x_end != xsize) x_end += 4;
if (y_start != 0) y_start -= 4;
if (y_end != ysize) y_end += 4;
JXL_RETURN_IF_ERROR(pre_erosion[thread].ShrinkTo((x_end - x_start) / 4,
(y_end - y_start) / 4));
static const float limit = 0.2f;
for (size_t y = y_start; y < y_end; ++y) {
size_t y2 = y + 1 < ysize ? y + 1 : y;
size_t y1 = y > 0 ? y - 1 : y;
const float* row_in = xyb.ConstPlaneRow(1, y);
const float* row_in1 = xyb.ConstPlaneRow(1, y1);
const float* row_in2 = xyb.ConstPlaneRow(1, y2);
float* JXL_RESTRICT row_out = diff_buffer.Row(thread);
auto scalar_pixel = [&](size_t x) {
const size_t x2 = x + 1 < xsize ? x + 1 : x;
const size_t x1 = x > 0 ? x - 1 : x;
const float base =
0.25f * (row_in2[x] + row_in1[x] + row_in[x1] + row_in[x2]);
const float gammac = RatioOfDerivativesOfCubicRootToSimpleGamma(
row_in[x] + match_gamma_offset);
float diff = gammac * (row_in[x] - base);
diff *= diff;
if (diff >= limit) {
diff = limit;
}
diff = MaskingSqrt(diff);
if ((y % 4) != 0) {
row_out[x - x_start] += diff;
} else {
row_out[x - x_start] = diff;
}
};
size_t x = x_start;
// First pixel of the row.
if (x_start == 0) {
scalar_pixel(x_start);
++x;
}
// SIMD
const auto match_gamma_offset_v = Set(df, match_gamma_offset);
const auto quarter = Set(df, 0.25f);
for (; x + 1 + Lanes(df) < x_end; x += Lanes(df)) {
const auto in = LoadU(df, row_in + x);
const auto in_r = LoadU(df, row_in + x + 1);
const auto in_l = LoadU(df, row_in + x - 1);
const auto in_t = LoadU(df, row_in2 + x);
const auto in_b = LoadU(df, row_in1 + x);
auto base = Mul(quarter, Add(Add(in_r, in_l), Add(in_t, in_b)));
auto gammacv =
RatioOfDerivativesOfCubicRootToSimpleGamma</*invert=*/false>(
df, Add(in, match_gamma_offset_v));
auto diff = Mul(gammacv, Sub(in, base));
diff = Mul(diff, diff);
diff = Min(diff, Set(df, limit));
diff = MaskingSqrt(df, diff);
if ((y & 3) != 0) {
diff = Add(diff, LoadU(df, row_out + x - x_start));
}
StoreU(diff, df, row_out + x - x_start);
}
// Scalar
for (; x < x_end; ++x) {
scalar_pixel(x);
}
if (y % 4 == 3) {
float* row_d_out = pre_erosion[thread].Row((y - y_start) / 4);
for (size_t x = 0; x < (x_end - x_start) / 4; x++) {
row_d_out[x] = (row_out[x * 4] + row_out[x * 4 + 1] +
row_out[x * 4 + 2] + row_out[x * 4 + 3]) *
0.25f;
}
}
}
JXL_ENSURE(x_start % (kBlockDim / 2) == 0);
JXL_ENSURE(y_start % (kBlockDim / 2) == 0);
Rect from_rect(x_start % 8 == 0 ? 0 : 1, y_start % 8 == 0 ? 0 : 1,
rect_out.xsize() * 2, rect_out.ysize() * 2);
JXL_RETURN_IF_ERROR(FuzzyErosion(butteraugli_target, from_rect,
pre_erosion[thread], rect_out, &aq_map));
for (size_t y = 0; y < rect_out.ysize(); ++y) {
const float* aq_map_row = rect_out.ConstRow(aq_map, y);
float* mask_row = rect_out.Row(mask, y);
for (size_t x = 0; x < rect_out.xsize(); ++x) {
mask_row[x] = ComputeMaskForAcStrategyUse(aq_map_row[x]);
}
}
PerBlockModulations(butteraugli_target, xyb.Plane(0), xyb.Plane(1),
xyb.Plane(2), rect_in, scale, rect_out, &aq_map);
return true;
}
std::vector<ImageF> pre_erosion;
ImageF aq_map;
ImageF diff_buffer;
};
Status Blur1x1Masking(JxlMemoryManager* memory_manager, ThreadPool* pool,
ImageF* mask1x1, const Rect& rect) {
// Blur the mask1x1 to obtain the masking image.
// Before blurring it contains an image of absolute value of the
// Laplacian of the intensity channel.
static const float kFilterMask1x1[5] = {
static_cast<float>(0.25647067633737227),
static_cast<float>(0.2050056912354399075),
static_cast<float>(0.154082048668497307),
static_cast<float>(0.08149576591362004441),
static_cast<float>(0.0512750104812308467),
};
double sum =
1.0 + 4 * (kFilterMask1x1[0] + kFilterMask1x1[1] + kFilterMask1x1[2] +
kFilterMask1x1[4] + 2 * kFilterMask1x1[3]);
if (sum < 1e-5) {
sum = 1e-5;
}
const float normalize = static_cast<float>(1.0 / sum);
const float normalize_mul = normalize;
WeightsSymmetric5 weights =
WeightsSymmetric5{{HWY_REP4(normalize)},
{HWY_REP4(normalize_mul * kFilterMask1x1[0])},
{HWY_REP4(normalize_mul * kFilterMask1x1[2])},
{HWY_REP4(normalize_mul * kFilterMask1x1[1])},
{HWY_REP4(normalize_mul * kFilterMask1x1[4])},
{HWY_REP4(normalize_mul * kFilterMask1x1[3])}};
JXL_ASSIGN_OR_RETURN(
ImageF temp, ImageF::Create(memory_manager, rect.xsize(), rect.ysize()));
JXL_RETURN_IF_ERROR(Symmetric5(*mask1x1, rect, weights, pool, &temp));
*mask1x1 = std::move(temp);
return true;
}
StatusOr<ImageF> AdaptiveQuantizationMap(const float butteraugli_target,
const Image3F& xyb, const Rect& rect,
float scale, ThreadPool* pool,
ImageF* mask, ImageF* mask1x1) {
JXL_ENSURE(rect.xsize() % kBlockDim == 0);
JXL_ENSURE(rect.ysize() % kBlockDim == 0);
AdaptiveQuantizationImpl impl;
const size_t xsize_blocks = rect.xsize() / kBlockDim;
const size_t ysize_blocks = rect.ysize() / kBlockDim;
JxlMemoryManager* memory_manager = xyb.memory_manager();
JXL_ASSIGN_OR_RETURN(
impl.aq_map, ImageF::Create(memory_manager, xsize_blocks, ysize_blocks));
JXL_ASSIGN_OR_RETURN(
*mask, ImageF::Create(memory_manager, xsize_blocks, ysize_blocks));
JXL_ASSIGN_OR_RETURN(
*mask1x1, ImageF::Create(memory_manager, xyb.xsize(), xyb.ysize()));
const auto prepare = [&](const size_t num_threads) -> Status {
JXL_RETURN_IF_ERROR(impl.PrepareBuffers(memory_manager, num_threads));
return true;
};
const auto process_tile = [&](const uint32_t tid,
const size_t thread) -> Status {
size_t n_enc_tiles = DivCeil(xsize_blocks, kEncTileDimInBlocks);
size_t tx = tid % n_enc_tiles;
size_t ty = tid / n_enc_tiles;
size_t by0 = ty * kEncTileDimInBlocks;
size_t by1 = std::min((ty + 1) * kEncTileDimInBlocks, ysize_blocks);
size_t bx0 = tx * kEncTileDimInBlocks;
size_t bx1 = std::min((tx + 1) * kEncTileDimInBlocks, xsize_blocks);
Rect rect_out(bx0, by0, bx1 - bx0, by1 - by0);
JXL_RETURN_IF_ERROR(impl.ComputeTile(butteraugli_target, scale, xyb, rect,
rect_out, thread, mask, mask1x1));
return true;
};
size_t num_tiles = DivCeil(xsize_blocks, kEncTileDimInBlocks) *
DivCeil(ysize_blocks, kEncTileDimInBlocks);
JXL_RETURN_IF_ERROR(RunOnPool(pool, 0, num_tiles, prepare, process_tile,
"AQ DiffPrecompute"));
JXL_RETURN_IF_ERROR(Blur1x1Masking(memory_manager, pool, mask1x1, rect));
return std::move(impl).aq_map;
}
} // namespace
// NOLINTNEXTLINE(google-readability-namespace-comments)
} // namespace HWY_NAMESPACE
} // namespace jxl
HWY_AFTER_NAMESPACE();
#if HWY_ONCE
namespace jxl {
HWY_EXPORT(AdaptiveQuantizationMap);
namespace {
// If true, prints the quantization maps at each iteration.
constexpr bool FLAGS_dump_quant_state = false;
Status DumpHeatmap(const CompressParams& cparams, const AuxOut* aux_out,
const std::string& label, const ImageF& image,
float good_threshold, float bad_threshold) {
if (JXL_DEBUG_ADAPTIVE_QUANTIZATION) {
JXL_ASSIGN_OR_RETURN(
Image3F heatmap,
CreateHeatMapImage(image, good_threshold, bad_threshold));
char filename[200];
snprintf(filename, sizeof(filename), "%s%05d", label.c_str(),
aux_out->num_butteraugli_iters);
JXL_RETURN_IF_ERROR(DumpImage(cparams, filename, heatmap));
}
return true;
}
Status DumpHeatmaps(const CompressParams& cparams, const AuxOut* aux_out,
float butteraugli_target, const ImageF& quant_field,
const ImageF& tile_heatmap, const ImageF& bt_diffmap) {
if (JXL_DEBUG_ADAPTIVE_QUANTIZATION) {
JxlMemoryManager* memory_manager = quant_field.memory_manager();
if (!WantDebugOutput(cparams)) return true;
JXL_ASSIGN_OR_RETURN(ImageF inv_qmap,
ImageF::Create(memory_manager, quant_field.xsize(),
quant_field.ysize()));
for (size_t y = 0; y < quant_field.ysize(); ++y) {
const float* JXL_RESTRICT row_q = quant_field.ConstRow(y);
float* JXL_RESTRICT row_inv_q = inv_qmap.Row(y);
for (size_t x = 0; x < quant_field.xsize(); ++x) {
row_inv_q[x] = 1.0f / row_q[x]; // never zero
}
}
JXL_RETURN_IF_ERROR(DumpHeatmap(cparams, aux_out, "quant_heatmap", inv_qmap,
4.0f * butteraugli_target,
6.0f * butteraugli_target));
JXL_RETURN_IF_ERROR(DumpHeatmap(cparams, aux_out, "tile_heatmap",
tile_heatmap, butteraugli_target,
1.5f * butteraugli_target));
// matches heat maps produced by the command line tool.
JXL_RETURN_IF_ERROR(DumpHeatmap(cparams, aux_out, "bt_diffmap", bt_diffmap,
ButteraugliFuzzyInverse(1.5),
ButteraugliFuzzyInverse(0.5)));
}
return true;
}
StatusOr<ImageF> TileDistMap(const ImageF& distmap, int tile_size, int margin,
const AcStrategyImage& ac_strategy) {
const int tile_xsize = (distmap.xsize() + tile_size - 1) / tile_size;
const int tile_ysize = (distmap.ysize() + tile_size - 1) / tile_size;
JxlMemoryManager* memory_manager = distmap.memory_manager();
JXL_ASSIGN_OR_RETURN(ImageF tile_distmap,
ImageF::Create(memory_manager, tile_xsize, tile_ysize));
size_t distmap_stride = tile_distmap.PixelsPerRow();
for (int tile_y = 0; tile_y < tile_ysize; ++tile_y) {
AcStrategyRow ac_strategy_row = ac_strategy.ConstRow(tile_y);
float* JXL_RESTRICT dist_row = tile_distmap.Row(tile_y);
for (int tile_x = 0; tile_x < tile_xsize; ++tile_x) {
AcStrategy acs = ac_strategy_row[tile_x];
if (!acs.IsFirstBlock()) continue;
int this_tile_xsize = acs.covered_blocks_x() * tile_size;
int this_tile_ysize = acs.covered_blocks_y() * tile_size;
int y_begin = std::max<int>(0, tile_size * tile_y - margin);
int y_end = std::min<int>(distmap.ysize(),
tile_size * tile_y + this_tile_ysize + margin);
int x_begin = std::max<int>(0, tile_size * tile_x - margin);
int x_end = std::min<int>(distmap.xsize(),
tile_size * tile_x + this_tile_xsize + margin);
float dist_norm = 0.0;
double pixels = 0;
for (int y = y_begin; y < y_end; ++y) {
float ymul = 1.0;
constexpr float kBorderMul = 0.98f;
constexpr float kCornerMul = 0.7f;
if (margin != 0 && (y == y_begin || y == y_end - 1)) {
ymul = kBorderMul;
}
const float* const JXL_RESTRICT row = distmap.Row(y);
for (int x = x_begin; x < x_end; ++x) {
float xmul = ymul;
if (margin != 0 && (x == x_begin || x == x_end - 1)) {
if (xmul == 1.0) {
xmul = kBorderMul;
} else {
xmul = kCornerMul;
}
}
float v = row[x];
v *= v;
v *= v;
v *= v;
v *= v;
dist_norm += xmul * v;
pixels += xmul;
}
}
if (pixels == 0) pixels = 1;
// 16th norm is less than the max norm, we reduce the difference
// with this normalization factor.
constexpr float kTileNorm = 1.2f;
const float tile_dist =
kTileNorm * std::pow(dist_norm / pixels, 1.0f / 16.0f);
dist_row[tile_x] = tile_dist;
for (size_t iy = 0; iy < acs.covered_blocks_y(); iy++) {
for (size_t ix = 0; ix < acs.covered_blocks_x(); ix++) {
dist_row[tile_x + distmap_stride * iy + ix] = tile_dist;
}
}
}
}
return tile_distmap;
}
const float kDcQuantPow = 0.83f;
const float kDcQuant = 1.095924047623553f;
const float kAcQuant = 0.725f;
// Computes the decoded image for a given set of compression parameters.
StatusOr<ImageBundle> RoundtripImage(const FrameHeader& frame_header,
const Image3F& opsin,
PassesEncoderState* enc_state,
const JxlCmsInterface& cms,
ThreadPool* pool) {
JxlMemoryManager* memory_manager = enc_state->memory_manager();
std::unique_ptr<PassesDecoderState> dec_state =
jxl::make_unique<PassesDecoderState>(memory_manager);
JXL_RETURN_IF_ERROR(dec_state->output_encoding_info.SetFromMetadata(
*enc_state->shared.metadata));
dec_state->shared = &enc_state->shared;
JXL_ENSURE(opsin.ysize() % kBlockDim == 0);
const size_t xsize_groups = DivCeil(opsin.xsize(), kGroupDim);
const size_t ysize_groups = DivCeil(opsin.ysize(), kGroupDim);
const size_t num_groups = xsize_groups * ysize_groups;
size_t num_special_frames = enc_state->special_frames.size();
size_t num_passes = enc_state->progressive_splitter.GetNumPasses();
JXL_ASSIGN_OR_RETURN(ModularFrameEncoder modular_frame_encoder,
ModularFrameEncoder::Create(memory_manager, frame_header,
enc_state->cparams, false));
JXL_RETURN_IF_ERROR(InitializePassesEncoder(frame_header, opsin, Rect(opsin),
cms, pool, enc_state,
&modular_frame_encoder, nullptr));
JXL_RETURN_IF_ERROR(dec_state->Init(frame_header));
JXL_RETURN_IF_ERROR(dec_state->InitForAC(num_passes, pool));
ImageBundle decoded(memory_manager, &enc_state->shared.metadata->m);
decoded.origin = frame_header.frame_origin;
JXL_ASSIGN_OR_RETURN(
Image3F tmp,
Image3F::Create(memory_manager, opsin.xsize(), opsin.ysize()));
JXL_RETURN_IF_ERROR(decoded.SetFromImage(
std::move(tmp), dec_state->output_encoding_info.color_encoding));
PassesDecoderState::PipelineOptions options;
options.use_slow_render_pipeline = false;
options.coalescing = false;
options.render_spotcolors = false;
options.render_noise = false;
// Same as frame_header.nonserialized_metadata->m
const ImageMetadata& metadata = *decoded.metadata();
JXL_RETURN_IF_ERROR(dec_state->PreparePipeline(
frame_header, &enc_state->shared.metadata->m, &decoded, options));
AlignedArray<GroupDecCache> group_dec_caches;
const auto allocate_storage = [&](const size_t num_threads) -> Status {
JXL_RETURN_IF_ERROR(
dec_state->render_pipeline->PrepareForThreads(num_threads,
/*use_group_ids=*/false));
JXL_ASSIGN_OR_RETURN(group_dec_caches, AlignedArray<GroupDecCache>::Create(
memory_manager, num_threads));
return true;
};
const auto process_group = [&](const uint32_t group_index,
const size_t thread) -> Status {
if (frame_header.loop_filter.epf_iters > 0) {
JXL_RETURN_IF_ERROR(
ComputeSigma(frame_header.loop_filter,
dec_state->shared->frame_dim.BlockGroupRect(group_index),
dec_state.get()));
}
RenderPipelineInput input =
dec_state->render_pipeline->GetInputBuffers(group_index, thread);
JXL_RETURN_IF_ERROR(DecodeGroupForRoundtrip(
frame_header, enc_state->coeffs, group_index, dec_state.get(),
&group_dec_caches[thread], thread, input, nullptr, nullptr));
for (size_t c = 0; c < metadata.num_extra_channels; c++) {
std::pair<ImageF*, Rect> ri = input.GetBuffer(3 + c);
FillPlane(0.0f, ri.first, ri.second);
}
JXL_RETURN_IF_ERROR(input.Done());
return true;
};
JXL_RETURN_IF_ERROR(RunOnPool(pool, 0, num_groups, allocate_storage,
process_group, "AQ loop"));
// Ensure we don't create any new special frames.
enc_state->special_frames.resize(num_special_frames);
return decoded;
}
constexpr int kMaxButteraugliIters = 4;
Status FindBestQuantization(const FrameHeader& frame_header,
const Image3F& linear, const Image3F& opsin,
ImageF& quant_field, PassesEncoderState* enc_state,
const JxlCmsInterface& cms, ThreadPool* pool,
AuxOut* aux_out) {
const CompressParams& cparams = enc_state->cparams;
if (cparams.resampling > 1 &&
cparams.original_butteraugli_distance <= 4.0 * cparams.resampling) {
// For downsampled opsin image, the butteraugli based adaptive quantization
// loop would only make the size bigger without improving the distance much,
// so in this case we enable it only for very high butteraugli targets.
return true;
}
JxlMemoryManager* memory_manager = enc_state->memory_manager();
Quantizer& quantizer = enc_state->shared.quantizer;
ImageI& raw_quant_field = enc_state->shared.raw_quant_field;
const float butteraugli_target = cparams.butteraugli_distance;
const float original_butteraugli = cparams.original_butteraugli_distance;
ButteraugliParams params;
params.intensity_target = 80.f;
JxlButteraugliComparator comparator(params, cms);
JXL_RETURN_IF_ERROR(comparator.SetLinearReferenceImage(linear));
bool lower_is_better =
(comparator.GoodQualityScore() < comparator.BadQualityScore());
const float initial_quant_dc = InitialQuantDC(butteraugli_target);
JXL_RETURN_IF_ERROR(AdjustQuantField(enc_state->shared.ac_strategy,
Rect(quant_field), original_butteraugli,
&quant_field));
ImageF tile_distmap;
JXL_ASSIGN_OR_RETURN(
ImageF initial_quant_field,
ImageF::Create(memory_manager, quant_field.xsize(), quant_field.ysize()));
JXL_RETURN_IF_ERROR(CopyImageTo(quant_field, &initial_quant_field));
float initial_qf_min;
float initial_qf_max;
ImageMinMax(initial_quant_field, &initial_qf_min, &initial_qf_max);
float initial_qf_ratio = initial_qf_max / initial_qf_min;
float qf_max_deviation_low = std::sqrt(250 / initial_qf_ratio);
float asymmetry = 2;
if (qf_max_deviation_low < asymmetry) asymmetry = qf_max_deviation_low;
float qf_lower = initial_qf_min / (asymmetry * qf_max_deviation_low);
float qf_higher = initial_qf_max * (qf_max_deviation_low / asymmetry);
JXL_ENSURE(qf_higher / qf_lower < 253);
constexpr int kOriginalComparisonRound = 1;
int iters = kMaxButteraugliIters;
if (cparams.speed_tier != SpeedTier::kTortoise) {
iters = 2;
}
for (int i = 0; i < iters + 1; ++i) {
if (JXL_DEBUG_ADAPTIVE_QUANTIZATION) {
printf("\nQuantization field:\n");
for (size_t y = 0; y < quant_field.ysize(); ++y) {
for (size_t x = 0; x < quant_field.xsize(); ++x) {
printf(" %.5f", quant_field.Row(y)[x]);
}
printf("\n");
}
}
JXL_RETURN_IF_ERROR(quantizer.SetQuantField(initial_quant_dc, quant_field,
&raw_quant_field));
JXL_ASSIGN_OR_RETURN(
ImageBundle dec_linear,
RoundtripImage(frame_header, opsin, enc_state, cms, pool));
float score;
ImageF diffmap;
JXL_RETURN_IF_ERROR(comparator.CompareWith(dec_linear, &diffmap, &score));
if (!lower_is_better) {
score = -score;
ScaleImage(-1.0f, &diffmap);
}
JXL_ASSIGN_OR_RETURN(tile_distmap,
TileDistMap(diffmap, 8 * cparams.resampling, 0,
enc_state->shared.ac_strategy));
if (JXL_DEBUG_ADAPTIVE_QUANTIZATION && WantDebugOutput(cparams)) {
JXL_RETURN_IF_ERROR(DumpImage(cparams, ("dec" + ToString(i)).c_str(),
*dec_linear.color()));
JXL_RETURN_IF_ERROR(DumpHeatmaps(cparams, aux_out, butteraugli_target,
quant_field, tile_distmap, diffmap));
}
if (aux_out != nullptr) ++aux_out->num_butteraugli_iters;
if (JXL_DEBUG_ADAPTIVE_QUANTIZATION) {
float minval;
float maxval;
ImageMinMax(quant_field, &minval, &maxval);
printf("\nButteraugli iter: %d/%d\n", i, kMaxButteraugliIters);
printf("Butteraugli distance: %f (target = %f)\n", score,
original_butteraugli);
printf("quant range: %f ... %f DC quant: %f\n", minval, maxval,
initial_quant_dc);
if (FLAGS_dump_quant_state) {
quantizer.DumpQuantizationMap(raw_quant_field);
}
}
if (i == iters) break;
double kPow[8] = {
0.2, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
};
double kPowMod[8] = {
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
};
if (i == kOriginalComparisonRound) {
// Don't allow optimization to make the quant field a lot worse than
// what the initial guess was. This allows the AC field to have enough
// precision to reduce the oscillations due to the dc reconstruction.
double kInitMul = 0.6;
const double kOneMinusInitMul = 1.0 - kInitMul;
for (size_t y = 0; y < quant_field.ysize(); ++y) {
float* const JXL_RESTRICT row_q = quant_field.Row(y);
const float* const JXL_RESTRICT row_init = initial_quant_field.Row(y);
for (size_t x = 0; x < quant_field.xsize(); ++x) {
double clamp = kOneMinusInitMul * row_q[x] + kInitMul * row_init[x];
if (row_q[x] < clamp) {
row_q[x] = clamp;
if (row_q[x] > qf_higher) row_q[x] = qf_higher;
if (row_q[x] < qf_lower) row_q[x] = qf_lower;
}
}
}
}
double cur_pow = 0.0;
if (i < 7) {
cur_pow = kPow[i] + (original_butteraugli - 1.0) * kPowMod[i];
if (cur_pow < 0) {
cur_pow = 0;
}
}
if (cur_pow == 0.0) {
for (size_t y = 0; y < quant_field.ysize(); ++y) {
const float* const JXL_RESTRICT row_dist = tile_distmap.Row(y);
float* const JXL_RESTRICT row_q = quant_field.Row(y);
for (size_t x = 0; x < quant_field.xsize(); ++x) {
const float diff = row_dist[x] / original_butteraugli;
if (diff > 1.0f) {
float old = row_q[x];
row_q[x] *= diff;
int qf_old =
static_cast<int>(std::lround(old * quantizer.InvGlobalScale()));
int qf_new = static_cast<int>(
std::lround(row_q[x] * quantizer.InvGlobalScale()));
if (qf_old == qf_new) {
row_q[x] = old + quantizer.Scale();
}
}
if (row_q[x] > qf_higher) row_q[x] = qf_higher;
if (row_q[x] < qf_lower) row_q[x] = qf_lower;
}
}
} else {
for (size_t y = 0; y < quant_field.ysize(); ++y) {
const float* const JXL_RESTRICT row_dist = tile_distmap.Row(y);
float* const JXL_RESTRICT row_q = quant_field.Row(y);
for (size_t x = 0; x < quant_field.xsize(); ++x) {
const float diff = row_dist[x] / original_butteraugli;
if (diff <= 1.0f) {
row_q[x] *= std::pow(diff, cur_pow);
} else {
float old = row_q[x];
row_q[x] *= diff;
int qf_old =
static_cast<int>(std::lround(old * quantizer.InvGlobalScale()));
int qf_new = static_cast<int>(
std::lround(row_q[x] * quantizer.InvGlobalScale()));
if (qf_old == qf_new) {
row_q[x] = old + quantizer.Scale();
}
}
if (row_q[x] > qf_higher) row_q[x] = qf_higher;
if (row_q[x] < qf_lower) row_q[x] = qf_lower;
}
}
}
}
JXL_RETURN_IF_ERROR(
quantizer.SetQuantField(initial_quant_dc, quant_field, &raw_quant_field));
return true;
}
Status FindBestQuantizationMaxError(const FrameHeader& frame_header,
const Image3F& opsin, ImageF& quant_field,
PassesEncoderState* enc_state,
const JxlCmsInterface& cms,
ThreadPool* pool, AuxOut* aux_out) {
// TODO(szabadka): Make this work for non-opsin color spaces.
const CompressParams& cparams = enc_state->cparams;
Quantizer& quantizer = enc_state->shared.quantizer;
ImageI& raw_quant_field = enc_state->shared.raw_quant_field;
// TODO(veluca): better choice of this value.
const float initial_quant_dc =
16 * std::sqrt(0.1f / cparams.butteraugli_distance);
JXL_RETURN_IF_ERROR(
AdjustQuantField(enc_state->shared.ac_strategy, Rect(quant_field),
cparams.original_butteraugli_distance, &quant_field));
const float inv_max_err[3] = {1.0f / enc_state->cparams.max_error[0],
1.0f / enc_state->cparams.max_error[1],
1.0f / enc_state->cparams.max_error[2]};
for (int i = 0; i < kMaxButteraugliIters + 1; ++i) {
JXL_RETURN_IF_ERROR(quantizer.SetQuantField(initial_quant_dc, quant_field,
&raw_quant_field));
if (JXL_DEBUG_ADAPTIVE_QUANTIZATION && aux_out) {
JXL_RETURN_IF_ERROR(
DumpXybImage(cparams, ("ops" + ToString(i)).c_str(), opsin));
}
JXL_ASSIGN_OR_RETURN(
ImageBundle decoded,
RoundtripImage(frame_header, opsin, enc_state, cms, pool));
if (JXL_DEBUG_ADAPTIVE_QUANTIZATION && aux_out) {
JXL_RETURN_IF_ERROR(DumpXybImage(cparams, ("dec" + ToString(i)).c_str(),
*decoded.color()));
}
for (size_t by = 0; by < enc_state->shared.frame_dim.ysize_blocks; by++) {
AcStrategyRow ac_strategy_row =
enc_state->shared.ac_strategy.ConstRow(by);
for (size_t bx = 0; bx < enc_state->shared.frame_dim.xsize_blocks; bx++) {
AcStrategy acs = ac_strategy_row[bx];
if (!acs.IsFirstBlock()) continue;
float max_error = 0;
for (size_t c = 0; c < 3; c++) {
for (size_t y = by * kBlockDim;
y < (by + acs.covered_blocks_y()) * kBlockDim; y++) {
if (y >= decoded.ysize()) continue;
const float* JXL_RESTRICT in_row = opsin.ConstPlaneRow(c, y);
const float* JXL_RESTRICT dec_row =
decoded.color()->ConstPlaneRow(c, y);
for (size_t x = bx * kBlockDim;
x < (bx + acs.covered_blocks_x()) * kBlockDim; x++) {
if (x >= decoded.xsize()) continue;
max_error = std::max(
std::abs(in_row[x] - dec_row[x]) * inv_max_err[c], max_error);
}
}
}
// Target an error between max_error/2 and max_error.
// If the error in the varblock is above the target, increase the qf to
// compensate. If the error is below the target, decrease the qf.
// However, to avoid an excessive increase of the qf, only do so if the
// error is less than half the maximum allowed error.
const float qf_mul = (max_error < 0.5f) ? max_error * 2.0f
: (max_error > 1.0f) ? max_error
: 1.0f;
for (size_t qy = by; qy < by + acs.covered_blocks_y(); qy++) {
float* JXL_RESTRICT quant_field_row = quant_field.Row(qy);
for (size_t qx = bx; qx < bx + acs.covered_blocks_x(); qx++) {
quant_field_row[qx] *= qf_mul;
}
}
}
}
}
JXL_RETURN_IF_ERROR(
quantizer.SetQuantField(initial_quant_dc, quant_field, &raw_quant_field));
return true;
}
} // namespace
Status AdjustQuantField(const AcStrategyImage& ac_strategy, const Rect& rect,
float butteraugli_target, ImageF* quant_field) {
// Replace the whole quant_field in non-8x8 blocks with the maximum of each
// 8x8 block.
size_t stride = quant_field->PixelsPerRow();
// At low distances it is great to use max, but mean works better
// at high distances. We interpolate between them for a distance
// range.
float mean_max_mixer = 1.0f;
{
static const float kLimit = 1.54138f;
static const float kMul = 0.56391f;
static const float kMin = 0.0f;
if (butteraugli_target > kLimit) {
mean_max_mixer -= (butteraugli_target - kLimit) * kMul;
if (mean_max_mixer < kMin) {
mean_max_mixer = kMin;
}
}
}
for (size_t y = 0; y < rect.ysize(); ++y) {
AcStrategyRow ac_strategy_row = ac_strategy.ConstRow(rect, y);
float* JXL_RESTRICT quant_row = rect.Row(quant_field, y);
for (size_t x = 0; x < rect.xsize(); ++x) {
AcStrategy acs = ac_strategy_row[x];
if (!acs.IsFirstBlock()) continue;
JXL_ENSURE(x + acs.covered_blocks_x() <= quant_field->xsize());
JXL_ENSURE(y + acs.covered_blocks_y() <= quant_field->ysize());
float max = quant_row[x];
float mean = 0.0;
for (size_t iy = 0; iy < acs.covered_blocks_y(); iy++) {
for (size_t ix = 0; ix < acs.covered_blocks_x(); ix++) {
mean += quant_row[x + ix + iy * stride];
max = std::max(quant_row[x + ix + iy * stride], max);
}
}
mean /= acs.covered_blocks_y() * acs.covered_blocks_x();
if (acs.covered_blocks_y() * acs.covered_blocks_x() >= 4) {
max *= mean_max_mixer;
max += (1.0f - mean_max_mixer) * mean;
}
for (size_t iy = 0; iy < acs.covered_blocks_y(); iy++) {
for (size_t ix = 0; ix < acs.covered_blocks_x(); ix++) {
quant_row[x + ix + iy * stride] = max;
}
}
}
}
return true;
}
float InitialQuantDC(float butteraugli_target) {
const float kDcMul = 0.3; // Butteraugli target where non-linearity kicks in.
const float butteraugli_target_dc = std::max<float>(
0.5f * butteraugli_target,
std::min<float>(butteraugli_target,
kDcMul * std::pow((1.0f / kDcMul) * butteraugli_target,
kDcQuantPow)));
// We want the maximum DC value to be at most 2**15 * kInvDCQuant / quant_dc.
// The maximum DC value might not be in the kXybRange because of inverse
// gaborish, so we add some slack to the maximum theoretical quant obtained
// this way (64).
return std::min(kDcQuant / butteraugli_target_dc, 50.f);
}
StatusOr<ImageF> InitialQuantField(const float butteraugli_target,
const Image3F& opsin, const Rect& rect,
ThreadPool* pool, float rescale,
ImageF* mask, ImageF* mask1x1) {
const float quant_ac = kAcQuant / butteraugli_target;
return HWY_DYNAMIC_DISPATCH(AdaptiveQuantizationMap)(
butteraugli_target, opsin, rect, quant_ac * rescale, pool, mask, mask1x1);
}
Status FindBestQuantizer(const FrameHeader& frame_header, const Image3F* linear,
const Image3F& opsin, ImageF& quant_field,
PassesEncoderState* enc_state,
const JxlCmsInterface& cms, ThreadPool* pool,
AuxOut* aux_out, double rescale) {
const CompressParams& cparams = enc_state->cparams;
if (cparams.max_error_mode) {
JXL_RETURN_IF_ERROR(FindBestQuantizationMaxError(
frame_header, opsin, quant_field, enc_state, cms, pool, aux_out));
} else if (linear && cparams.speed_tier <= SpeedTier::kKitten) {
// Normal encoding to a butteraugli score.
JXL_RETURN_IF_ERROR(FindBestQuantization(frame_header, *linear, opsin,
quant_field, enc_state, cms, pool,
aux_out));
}
return true;
}
} // namespace jxl
#endif // HWY_ONCE