// 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 #include #include #undef HWY_TARGET_INCLUDE #define HWY_TARGET_INCLUDE "lib/jxl/rational_polynomial_test.cc" #include #include #include #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::ShiftLeft; using hwy::HWY_NAMESPACE::ShiftRight; // Generic: only computes polynomial struct EvalPoly { template 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 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, Abs(x_bits) == x_bits)); // Range reduction to [-1/3, 1/3] - 3 integer, 2 float ops const auto exp_bits = 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, x_bits - ShiftLeft<23>(exp_shifted)); const auto exp_val = ConvertTo(d, exp_shifted); vx = mantissa - Set(d, 1.0f); const auto approx = 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 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(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