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Optimize blurhashes a bit more

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Nicolas Werner 2022-01-02 03:41:38 +01:00
parent e05720b5ca
commit 87ba5796bc
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1 changed files with 45 additions and 43 deletions
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88
third_party/blurhash/blurhash.cpp vendored
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@ -230,25 +230,17 @@ decodeAC(std::string_view value, float maximumValue)
return decodeAC(decode83(value), maximumValue); return decodeAC(decode83(value), maximumValue);
} }
Color std::vector<float>
multiplyBasisFunction(Components components, int width, int height, unsigned char *pixels) bases_for(size_t dimension, size_t components)
{ {
Color c{}; std::vector<float> bases(dimension * components, 0.f);
float normalisation = (components.x == 0 && components.y == 0) ? 1 : 2; auto scale = pi<float> / float(dimension);
for (size_t x = 0; x < dimension; x++) {
for (int y = 0; y < height; y++) { for (size_t nx = 0; nx < size_t(components); nx++) {
for (int x = 0; x < width; x++) { bases[x * components + nx] = std::cos(scale * float(nx * x));
float basis = std::cos(pi<float> * components.x * x / float(width)) *
std::cos(pi<float> * components.y * y / float(height));
c.r += basis * srgbToLinear(pixels[3 * x + 0 + y * width * 3]);
c.g += basis * srgbToLinear(pixels[3 * x + 1 + y * width * 3]);
c.b += basis * srgbToLinear(pixels[3 * x + 2 + y * width * 3]);
} }
} }
return bases;
float scale = normalisation / (width * height);
c *= scale;
return c;
} }
} }
@ -281,23 +273,10 @@ decode(std::string_view blurhash, size_t width, size_t height, size_t bytesPerPi
return {}; return {};
} }
i.image.reserve(height * width * bytesPerPixel); i.image = decltype(i.image)(height * width * bytesPerPixel, 255);
std::vector<float> basis_x(width * components.x, 0.f); std::vector<float> basis_x = bases_for(width, components.x);
std::vector<float> basis_y(height * components.y, 0.f); std::vector<float> basis_y = bases_for(height, components.y);
for (size_t x = 0; x < width; x++) {
for (size_t nx = 0; nx < size_t(components.x); nx++) {
basis_x[x * components.x + nx] =
std::cos(pi<float> * float(nx * x) / float(width));
}
}
for (size_t y = 0; y < height; y++) {
for (size_t ny = 0; ny < size_t(components.y); ny++) {
basis_y[y * components.y + ny] =
std::cos(pi<float> * float(ny * y) / float(height));
}
}
for (size_t y = 0; y < height; y++) { for (size_t y = 0; y < height; y++) {
for (size_t x = 0; x < width; x++) { for (size_t x = 0; x < width; x++) {
@ -311,12 +290,12 @@ decode(std::string_view blurhash, size_t width, size_t height, size_t bytesPerPi
} }
} }
i.image.push_back(static_cast<unsigned char>(linearToSrgb(c.r))); i.image[(y * width + x) * bytesPerPixel + 0] =
i.image.push_back(static_cast<unsigned char>(linearToSrgb(c.g))); static_cast<unsigned char>(linearToSrgb(c.r));
i.image.push_back(static_cast<unsigned char>(linearToSrgb(c.b))); i.image[(y * width + x) * bytesPerPixel + 1] =
static_cast<unsigned char>(linearToSrgb(c.g));
for (size_t p = 3; p < bytesPerPixel; p++) i.image[(y * width + x) * bytesPerPixel + 2] =
i.image.push_back(255); static_cast<unsigned char>(linearToSrgb(c.b));
} }
} }
@ -333,14 +312,37 @@ encode(unsigned char *image, size_t width, size_t height, int components_x, int
components_y > 9 || !image) components_y > 9 || !image)
return ""; return "";
std::vector<Color> factors; std::vector<float> basis_x = bases_for(width, components_x);
factors.reserve(components_x * components_y); std::vector<float> basis_y = bases_for(height, components_y);
for (int y = 0; y < components_y; y++) {
for (int x = 0; x < components_x; x++) { std::vector<Color> factors(components_x * components_y, Color{});
factors.push_back(multiplyBasisFunction({x, y}, width, height, image)); for (size_t y = 0; y < height; y++) {
for (size_t x = 0; x < width; x++) {
Color linear{srgbToLinear(image[3 * x + 0 + y * width * 3]),
srgbToLinear(image[3 * x + 1 + y * width * 3]),
srgbToLinear(image[3 * x + 2 + y * width * 3])};
// other half of normalization.
linear *= 1.f / width;
for (size_t ny = 0; ny < size_t(components_y); ny++) {
for (size_t nx = 0; nx < size_t(components_x); nx++) {
float basis = basis_x[x * size_t(components_x) + nx] *
basis_y[y * size_t(components_y) + ny];
factors[ny * components_x + nx] += linear * basis;
}
}
} }
} }
// scale by normalization. Half the scaling is done in the previous loop to prevent going
// too far outside the float range.
for (size_t i = 0; i < factors.size(); i++) {
float normalisation = (i == 0) ? 1 : 2;
float scale = normalisation / (height);
factors[i] *= scale;
}
assert(factors.size() > 0); assert(factors.size() > 0);
auto dc = factors.front(); auto dc = factors.front();