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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.
#include <stdio.h>
#include <cmath>
#include <string>
#undef HWY_TARGET_INCLUDE
#define HWY_TARGET_INCLUDE "lib/jxl/rational_polynomial_test.cc"
#include <hwy/foreach_target.h>
#include <hwy/highway.h>
#include <hwy/tests/test_util-inl.h>
#include "lib/jxl/base/status.h"
#include "lib/jxl/common.h"
#include "lib/jxl/rational_polynomial-inl.h"
HWY_BEFORE_NAMESPACE();
namespace jxl {
namespace HWY_NAMESPACE {
using T = float; // required by EvalLog2
using D = HWY_FULL(T);
// These templates are not found via ADL.
using hwy::HWY_NAMESPACE::Add;
using hwy::HWY_NAMESPACE::GetLane;
using hwy::HWY_NAMESPACE::ShiftLeft;
using hwy::HWY_NAMESPACE::ShiftRight;
using hwy::HWY_NAMESPACE::Sub;
// Generic: only computes polynomial
struct EvalPoly {
template <size_t NP, size_t NQ>
T operator()(T x, const T (&p)[NP], const T (&q)[NQ]) const {
const HWY_FULL(T) d;
const auto vx = Set(d, x);
const auto approx = EvalRationalPolynomial(d, vx, p, q);
return GetLane(approx);
}
};
// Range reduction for log2
struct EvalLog2 {
template <size_t NP, size_t NQ>
T operator()(T x, const T (&p)[NP], const T (&q)[NQ]) const {
const HWY_FULL(T) d;
auto vx = Set(d, x);
const HWY_FULL(int32_t) di;
const auto x_bits = BitCast(di, vx);
// Cannot handle negative numbers / NaN.
JXL_DASSERT(AllTrue(di, Eq(Abs(x_bits), x_bits)));
// Range reduction to [-1/3, 1/3] - 3 integer, 2 float ops
const auto exp_bits = Sub(x_bits, Set(di, 0x3f2aaaab)); // = 2/3
// Shifted exponent = log2; also used to clear mantissa.
const auto exp_shifted = ShiftRight<23>(exp_bits);
const auto mantissa = BitCast(d, Sub(x_bits, ShiftLeft<23>(exp_shifted)));
const auto exp_val = ConvertTo(d, exp_shifted);
vx = Sub(mantissa, Set(d, 1.0f));
const auto approx = Add(EvalRationalPolynomial(d, vx, p, q), exp_val);
return GetLane(approx);
}
};
// Functions to approximate:
T LinearToSrgb8Direct(T val) {
if (val < 0.0) return 0.0;
if (val >= 255.0) return 255.0;
if (val <= 10.0 / 12.92) return val * 12.92;
return 255.0 * (std::pow(val / 255.0, 1.0 / 2.4) * 1.055 - 0.055);
}
T SimpleGamma(T v) {
static const T kGamma = 0.387494322593;
static const T limit = 43.01745241042018;
T bright = v - limit;
if (bright >= 0) {
static const T mul = 0.0383723643799;
v -= bright * mul;
}
static const T limit2 = 94.68634353321337;
T bright2 = v - limit2;
if (bright2 >= 0) {
static const T mul = 0.22885405968;
v -= bright2 * mul;
}
static const T offset = 0.156775786057;
static const T scale = 8.898059160493739;
T retval = scale * (offset + pow(v, kGamma));
return retval;
}
// Runs CaratheodoryFejer and verifies the polynomial using a lot of samples to
// return the biggest error.
template <size_t NP, size_t NQ, class Eval>
T RunApproximation(T x0, T x1, const T (&p)[NP], const T (&q)[NQ],
const Eval& eval, T func_to_approx(T)) {
float maxerr = 0;
T lastPrint = 0;
// NOLINTNEXTLINE(clang-analyzer-security.FloatLoopCounter)
for (T x = x0; x <= x1; x += (x1 - x0) / 10000.0) {
const T f = func_to_approx(x);
const T g = eval(x, p, q);
maxerr = std::max(fabsf(g - f), maxerr);
if (x == x0 || x - lastPrint > (x1 - x0) / 20.0) {
printf("x: %11.6f, f: %11.6f, g: %11.6f, e: %11.6f\n", x, f, g,
fabs(g - f));
lastPrint = x;
}
}
return maxerr;
}
void TestSimpleGamma() {
const T p[4 * (6 + 1)] = {
HWY_REP4(-5.0646949363741811E-05), HWY_REP4(6.7369380528439771E-05),
HWY_REP4(8.9376652530412794E-05), HWY_REP4(2.1153513301520462E-06),
HWY_REP4(-6.9130322970386449E-08), HWY_REP4(3.9424752749293728E-10),
HWY_REP4(1.2360288207619576E-13)};
const T q[4 * (6 + 1)] = {
HWY_REP4(-6.6389733798591366E-06), HWY_REP4(1.3299859726565908E-05),
HWY_REP4(3.8538748358398873E-06), HWY_REP4(-2.8707687262928236E-08),
HWY_REP4(-6.6897385800005434E-10), HWY_REP4(6.1428748869186003E-12),
HWY_REP4(-2.5475738169252870E-15)};
const T err = RunApproximation(0.77, 274.579999999999984, p, q, EvalPoly(),
SimpleGamma);
EXPECT_LT(err, 0.05);
}
void TestLinearToSrgb8Direct() {
const T p[4 * (5 + 1)] = {
HWY_REP4(-9.5357499040105154E-05), HWY_REP4(4.6761186249798248E-04),
HWY_REP4(2.5708174333943594E-04), HWY_REP4(1.5250087770436082E-05),
HWY_REP4(1.1946768008931187E-07), HWY_REP4(5.9916446295972850E-11)};
const T q[4 * (4 + 1)] = {
HWY_REP4(1.8932479758079768E-05), HWY_REP4(2.7312342474687321E-05),
HWY_REP4(4.3901204783327006E-06), HWY_REP4(1.0417787306920273E-07),
HWY_REP4(3.0084206762140419E-10)};
const T err =
RunApproximation(0.77, 255, p, q, EvalPoly(), LinearToSrgb8Direct);
EXPECT_LT(err, 0.05);
}
void TestExp() {
const T p[4 * (2 + 1)] = {HWY_REP4(9.6266879665530902E-01),
HWY_REP4(4.8961265681586763E-01),
HWY_REP4(8.2619259189548433E-02)};
const T q[4 * (2 + 1)] = {HWY_REP4(9.6259895571622622E-01),
HWY_REP4(-4.7272457588933831E-01),
HWY_REP4(7.4802088567547664E-02)};
const T err =
RunApproximation(-1, 1, p, q, EvalPoly(), [](T x) { return T(exp(x)); });
EXPECT_LT(err, 1E-4);
}
void TestNegExp() {
// 4,3 is the min required for monotonicity; max error in 0,10: 751 ppm
// no benefit for k>50.
const T p[4 * (4 + 1)] = {
HWY_REP4(5.9580258551150123E-02), HWY_REP4(-2.5073728806886408E-02),
HWY_REP4(4.1561830213689248E-03), HWY_REP4(-3.1815408488900372E-04),
HWY_REP4(9.3866690094906802E-06)};
const T q[4 * (3 + 1)] = {
HWY_REP4(5.9579108238812878E-02), HWY_REP4(3.4542074345478582E-02),
HWY_REP4(8.7263562483501714E-03), HWY_REP4(1.4095109143061216E-03)};
const T err =
RunApproximation(0, 10, p, q, EvalPoly(), [](T x) { return T(exp(-x)); });
EXPECT_LT(err, sizeof(T) == 8 ? 2E-5 : 3E-5);
}
void TestSin() {
const T p[4 * (6 + 1)] = {
HWY_REP4(1.5518122109203780E-05), HWY_REP4(2.3388958643675966E+00),
HWY_REP4(-8.6705520940849157E-01), HWY_REP4(-1.9702294764873535E-01),
HWY_REP4(1.2193404314472320E-01), HWY_REP4(-1.7373966109788839E-02),
HWY_REP4(7.8829435883034796E-04)};
const T q[4 * (5 + 1)] = {
HWY_REP4(2.3394371422557279E+00), HWY_REP4(-8.7028221081288615E-01),
HWY_REP4(2.0052872219658430E-01), HWY_REP4(-3.2460335995264836E-02),
HWY_REP4(3.1546157932479282E-03), HWY_REP4(-1.6692542019380155E-04)};
const T err = RunApproximation(0, Pi<T>(1) * 2, p, q, EvalPoly(),
[](T x) { return T(sin(x)); });
EXPECT_LT(err, sizeof(T) == 8 ? 5E-4 : 7E-4);
}
void TestLog() {
HWY_ALIGN const T p[4 * (2 + 1)] = {HWY_REP4(-1.8503833400518310E-06),
HWY_REP4(1.4287160470083755E+00),
HWY_REP4(7.4245873327820566E-01)};
HWY_ALIGN const T q[4 * (2 + 1)] = {HWY_REP4(9.9032814277590719E-01),
HWY_REP4(1.0096718572241148E+00),
HWY_REP4(1.7409343003366853E-01)};
const T err = RunApproximation(1E-6, 1000, p, q, EvalLog2(), std::log2);
printf("%E\n", err);
}
HWY_NOINLINE void TestRationalPolynomial() {
TestSimpleGamma();
TestLinearToSrgb8Direct();
TestExp();
TestNegExp();
TestSin();
TestLog();
}
// NOLINTNEXTLINE(google-readability-namespace-comments)
} // namespace HWY_NAMESPACE
} // namespace jxl
HWY_AFTER_NAMESPACE();
#if HWY_ONCE
namespace jxl {
class RationalPolynomialTest : public hwy::TestWithParamTarget {};
HWY_TARGET_INSTANTIATE_TEST_SUITE_P(RationalPolynomialTest);
HWY_EXPORT_AND_TEST_P(RationalPolynomialTest, TestSimpleGamma);
HWY_EXPORT_AND_TEST_P(RationalPolynomialTest, TestLinearToSrgb8Direct);
HWY_EXPORT_AND_TEST_P(RationalPolynomialTest, TestExp);
HWY_EXPORT_AND_TEST_P(RationalPolynomialTest, TestNegExp);
HWY_EXPORT_AND_TEST_P(RationalPolynomialTest, TestSin);
HWY_EXPORT_AND_TEST_P(RationalPolynomialTest, TestLog);
} // namespace jxl
#endif // HWY_ONCE
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