tor-browser

convolve_slow.cc (7480B)

---

// 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 <atomic>

#include "lib/jxl/base/rect.h"
#include "lib/jxl/base/status.h"
#include "lib/jxl/convolve-inl.h"
#include "lib/jxl/convolve.h"

namespace jxl {

//------------------------------------------------------------------------------
// Kernels

// 4 instances of a given literal value, useful as input to LoadDup128.
#define JXL_REP4(literal) literal, literal, literal, literal

// Concentrates energy in low-frequency components (e.g. for antialiasing).
const WeightsSymmetric3& WeightsSymmetric3Lowpass() {
 // Computed by research/convolve_weights.py's cubic spline approximations of
 // prolate spheroidal wave functions.
 constexpr float w0 = 0.36208932f;
 constexpr float w1 = 0.12820096f;
 constexpr float w2 = 0.03127668f;
 static constexpr WeightsSymmetric3 weights = {
     {JXL_REP4(w0)}, {JXL_REP4(w1)}, {JXL_REP4(w2)}};
 return weights;
}

const WeightsSeparable5& WeightsSeparable5Lowpass() {
 constexpr float w0 = 0.41714928f;
 constexpr float w1 = 0.25539268f;
 constexpr float w2 = 0.03603267f;
 static constexpr WeightsSeparable5 weights = {
     {JXL_REP4(w0), JXL_REP4(w1), JXL_REP4(w2)},
     {JXL_REP4(w0), JXL_REP4(w1), JXL_REP4(w2)}};
 return weights;
}

const WeightsSymmetric5& WeightsSymmetric5Lowpass() {
 static constexpr WeightsSymmetric5 weights = {
     {JXL_REP4(0.1740135f)}, {JXL_REP4(0.1065369f)}, {JXL_REP4(0.0150310f)},
     {JXL_REP4(0.0652254f)}, {JXL_REP4(0.0012984f)}, {JXL_REP4(0.0092025f)}};
 return weights;
}

const WeightsSeparable5& WeightsSeparable5Gaussian1() {
 constexpr float w0 = 0.38774f;
 constexpr float w1 = 0.24477f;
 constexpr float w2 = 0.06136f;
 static constexpr WeightsSeparable5 weights = {
     {JXL_REP4(w0), JXL_REP4(w1), JXL_REP4(w2)},
     {JXL_REP4(w0), JXL_REP4(w1), JXL_REP4(w2)}};
 return weights;
}

const WeightsSeparable5& WeightsSeparable5Gaussian2() {
 constexpr float w0 = 0.250301f;
 constexpr float w1 = 0.221461f;
 constexpr float w2 = 0.153388f;
 static constexpr WeightsSeparable5 weights = {
     {JXL_REP4(w0), JXL_REP4(w1), JXL_REP4(w2)},
     {JXL_REP4(w0), JXL_REP4(w1), JXL_REP4(w2)}};
 return weights;
}

#undef JXL_REP4

//------------------------------------------------------------------------------
// Slow

namespace {

template <class WrapX, class WrapY>
float SlowSymmetric3Pixel(const ImageF& in, const int64_t ix, const int64_t iy,
                         const int64_t xsize, const int64_t ysize,
                         const WeightsSymmetric3& weights) {
 float sum = 0.0f;

 // ix: image; kx: kernel
 for (int64_t ky = -1; ky <= 1; ky++) {
   const int64_t y = WrapY()(iy + ky, ysize);
   const float* JXL_RESTRICT row_in = in.ConstRow(static_cast<size_t>(y));

   const float wc = ky == 0 ? weights.c[0] : weights.r[0];
   const float wlr = ky == 0 ? weights.r[0] : weights.d[0];

   const int64_t xm1 = WrapX()(ix - 1, xsize);
   const int64_t xp1 = WrapX()(ix + 1, xsize);
   sum += row_in[ix] * wc + (row_in[xm1] + row_in[xp1]) * wlr;
 }
 return sum;
}

template <class WrapY>
void SlowSymmetric3Row(const ImageF& in, const int64_t iy, const int64_t xsize,
                      const int64_t ysize, const WeightsSymmetric3& weights,
                      float* JXL_RESTRICT row_out) {
 row_out[0] =
     SlowSymmetric3Pixel<WrapMirror, WrapY>(in, 0, iy, xsize, ysize, weights);
 for (int64_t ix = 1; ix < xsize - 1; ix++) {
   row_out[ix] = SlowSymmetric3Pixel<WrapUnchanged, WrapY>(in, ix, iy, xsize,
                                                           ysize, weights);
 }
 {
   const int64_t ix = xsize - 1;
   row_out[ix] = SlowSymmetric3Pixel<WrapMirror, WrapY>(in, ix, iy, xsize,
                                                        ysize, weights);
 }
}

}  // namespace

Status SlowSymmetric3(const ImageF& in, const Rect& rect,
                     const WeightsSymmetric3& weights, ThreadPool* pool,
                     ImageF* JXL_RESTRICT out) {
 const int64_t xsize = static_cast<int64_t>(rect.xsize());
 const int64_t ysize = static_cast<int64_t>(rect.ysize());
 const int64_t kRadius = 1;

 const auto process_row = [&](const uint32_t task,
                              size_t /*thread*/) -> Status {
   const int64_t iy = task;
   float* JXL_RESTRICT out_row = out->Row(static_cast<size_t>(iy));

   if (iy < kRadius || iy >= ysize - kRadius) {
     SlowSymmetric3Row<WrapMirror>(in, iy, xsize, ysize, weights, out_row);
   } else {
     SlowSymmetric3Row<WrapUnchanged>(in, iy, xsize, ysize, weights, out_row);
   }
   return true;
 };
 JXL_RETURN_IF_ERROR(RunOnPool(pool, 0, static_cast<uint32_t>(ysize),
                               ThreadPool::NoInit, process_row,
                               "SlowSymmetric3"));
 return true;
}

namespace {

// Separable kernels, any radius.
StatusOr<float> SlowSeparablePixel(const ImageF& in, const Rect& rect,
                                  const int64_t x, const int64_t y,
                                  const int64_t radius,
                                  const float* JXL_RESTRICT horz_weights,
                                  const float* JXL_RESTRICT vert_weights) {
 const size_t xsize = in.xsize();
 const size_t ysize = in.ysize();
 const WrapMirror wrap;

 float mul = 0.0f;
 for (int dy = -radius; dy <= radius; ++dy) {
   const float wy = vert_weights[std::abs(dy) * 4];
   const size_t sy = wrap(rect.y0() + y + dy, ysize);
   JXL_ENSURE(sy < ysize);
   const float* const JXL_RESTRICT row = in.ConstRow(sy);
   for (int dx = -radius; dx <= radius; ++dx) {
     const float wx = horz_weights[std::abs(dx) * 4];
     const size_t sx = wrap(rect.x0() + x + dx, xsize);
     JXL_ENSURE(sx < xsize);
     mul += row[sx] * wx * wy;
   }
 }
 return mul;
}

template <int R, typename Weights>
Status SlowSeparable(const ImageF& in, const Rect& in_rect,
                    const Weights& weights, ThreadPool* pool, ImageF* out,
                    const Rect& out_rect) {
 JXL_ENSURE(in_rect.xsize() == out_rect.xsize());
 JXL_ENSURE(in_rect.ysize() == out_rect.ysize());
 JXL_ENSURE(in_rect.IsInside(Rect(in)));
 JXL_ENSURE(out_rect.IsInside(Rect(*out)));
 const float* horz_weights = &weights.horz[0];
 const float* vert_weights = &weights.vert[0];

 const auto process_row = [&](const uint32_t task,
                              size_t /*thread*/) -> Status {
   const int64_t y = task;

   float* const JXL_RESTRICT row_out = out_rect.Row(out, y);
   for (size_t x = 0; x < in_rect.xsize(); ++x) {
     JXL_ASSIGN_OR_RETURN(row_out[x],
                          SlowSeparablePixel(in, in_rect, x, y, /*radius=*/R,
                                             horz_weights, vert_weights));
   }
   return true;
 };
 const size_t ysize = in_rect.ysize();
 JXL_RETURN_IF_ERROR(RunOnPool(pool, 0, static_cast<uint32_t>(ysize),
                               ThreadPool::NoInit, process_row,
                               "SlowSeparable"));
 return true;
}

}  // namespace

Status SlowSeparable5(const ImageF& in, const Rect& in_rect,
                     const WeightsSeparable5& weights, ThreadPool* pool,
                     ImageF* out, const Rect& out_rect) {
 return SlowSeparable<2>(in, in_rect, weights, pool, out, out_rect);
}

}  // namespace jxl