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So I broke the inverse predictor

pull/1552/head
James Jackson-South 6 years ago
parent
8538db75e4
commit
2e17ce1619
2 changed files with 464 additions and 405 deletions
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  1. 477
      src/ImageSharp/Formats/WebP/Lossless/LosslessUtils.cs
  2. 392
      src/ImageSharp/Formats/WebP/Lossless/PredictorEncoder.cs

477
src/ImageSharp/Formats/WebP/Lossless/LosslessUtils.cs
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@ -12,7 +12,7 @@ namespace SixLabors.ImageSharp.Formats.WebP.Lossless
/// <summary>
/// Utility functions for the lossless decoder.
/// </summary>
internal static class LosslessUtils
internal static unsafe class LosslessUtils
{
private const uint Predictor0 = WebPConstants.ArgbBlack;
@ -244,104 +244,117 @@ namespace SixLabors.ImageSharp.Formats.WebP.Lossless
/// </summary>
/// <param name="transform">The transform data.</param>
/// <param name="pixelData">The pixel data to apply the inverse transform.</param>
/// <param name="output">The resulting pixel data with the reversed transformation data.</param>
public static void PredictorInverseTransform(Vp8LTransform transform, Span<uint> pixelData, Span<uint> output)
{
int processedPixels = 0;
int width = transform.XSize;
Span<uint> transformData = transform.Data.GetSpan();
// First Row follows the L (mode=1) mode.
PredictorAdd0(pixelData, processedPixels, 1, output);
PredictorAdd1(pixelData, 1, width - 1, output);
processedPixels += width;
int y = 1;
int yEnd = transform.YSize;
int tileWidth = 1 << transform.Bits;
int mask = tileWidth - 1;
int tilesPerRow = SubSampleSize(width, transform.Bits);
int predictorModeIdxBase = (y >> transform.Bits) * tilesPerRow;
while (y < yEnd)
/// <param name="outputSpan">The resulting pixel data with the reversed transformation data.</param>
public static void PredictorInverseTransform(
Vp8LTransform transform,
Span<uint> pixelData,
Span<uint> outputSpan)
{
fixed (uint* inputFixed = pixelData)
fixed (uint* outputFixed = outputSpan)
{
int predictorModeIdx = predictorModeIdxBase;
int x = 1;
uint* input = inputFixed;
uint* output = outputFixed;
int width = transform.XSize;
Span<uint> transformData = transform.Data.GetSpan();
// First Row follows the L (mode=1) mode.
PredictorAdd0(input, null, 1, output);
PredictorAdd1(input + 1, null, width - 1, output);
input += width;
output += width;
int y = 1;
int yEnd = transform.YSize;
int tileWidth = 1 << transform.Bits;
int mask = tileWidth - 1;
int tilesPerRow = SubSampleSize(width, transform.Bits);
int predictorModeIdxBase = (y >> transform.Bits) * tilesPerRow;
while (y < yEnd)
{
int predictorModeIdx = predictorModeIdxBase;
int x = 1;
// First pixel follows the T (mode=2) mode.
PredictorAdd2(pixelData, processedPixels, 1, width, output);
// First pixel follows the T (mode=2) mode.
PredictorAdd2(input, output - width, 1, output);
while (x < width)
{
uint predictorMode = (transformData[predictorModeIdx++] >> 8) & 0xf;
int xEnd = (x & ~mask) + tileWidth;
if (xEnd > width)
// .. the rest:
while (x < width)
{
xEnd = width;
}
uint predictorMode = (transformData[predictorModeIdx++] >> 8) & 0xf;
int xEnd = (x & ~mask) + tileWidth;
if (xEnd > width)
{
xEnd = width;
}
// There are 14 different prediction modes.
// In each prediction mode, the current pixel value is predicted from one or more neighboring pixels whose values are already known.
int startIdx = processedPixels + x;
int numberOfPixels = xEnd - x;
switch (predictorMode)
{
case 0:
PredictorAdd0(pixelData, startIdx, numberOfPixels, output);
break;
case 1:
PredictorAdd1(pixelData, startIdx, numberOfPixels, output);
break;
case 2:
PredictorAdd2(pixelData, startIdx, numberOfPixels, width, output);
break;
case 3:
PredictorAdd3(pixelData, startIdx, numberOfPixels, width, output);
break;
case 4:
PredictorAdd4(pixelData, startIdx, numberOfPixels, width, output);
break;
case 5:
PredictorAdd5(pixelData, startIdx, numberOfPixels, width, output);
break;
case 6:
PredictorAdd6(pixelData, startIdx, numberOfPixels, width, output);
break;
case 7:
PredictorAdd7(pixelData, startIdx, numberOfPixels, width, output);
break;
case 8:
PredictorAdd8(pixelData, startIdx, numberOfPixels, width, output);
break;
case 9:
PredictorAdd9(pixelData, startIdx, numberOfPixels, width, output);
break;
case 10:
PredictorAdd10(pixelData, startIdx, numberOfPixels, width, output);
break;
case 11:
PredictorAdd11(pixelData, startIdx, numberOfPixels, width, output);
break;
case 12:
PredictorAdd12(pixelData, startIdx, numberOfPixels, width, output);
break;
case 13:
PredictorAdd13(pixelData, startIdx, numberOfPixels, width, output);
break;
// There are 14 different prediction modes.
// In each prediction mode, the current pixel value is predicted from one
// or more neighboring pixels whose values are already known.
switch (predictorMode)
{
case 0:
PredictorAdd0(input + x, output + x - width, xEnd - x, output + x);
break;
case 1:
PredictorAdd1(input + x, output + x - width, xEnd - x, output + x);
break;
case 2:
PredictorAdd2(input + x, output + x - width, xEnd - x, output + x);
break;
case 3:
PredictorAdd3(input + x, output + x - width, xEnd - x, output + x);
break;
case 4:
PredictorAdd4(input + x, output + x - width, xEnd - x, output + x);
break;
case 5:
PredictorAdd5(input + x, output + x - width, xEnd - x, output + x);
break;
case 6:
PredictorAdd6(input + x, output + x - width, xEnd - x, output + x);
break;
case 7:
PredictorAdd7(input + x, output + x - width, xEnd - x, output + x);
break;
case 8:
PredictorAdd8(input + x, output + x - width, xEnd - x, output + x);
break;
case 9:
PredictorAdd9(input + x, output + x - width, xEnd - x, output + x);
break;
case 10:
PredictorAdd10(input + x, output + x - width, xEnd - x, output + x);
break;
case 11:
PredictorAdd11(input + x, output + x - width, xEnd - x, output + x);
break;
case 12:
PredictorAdd12(input + x, output + x - width, xEnd - x, output + x);
break;
case 13:
PredictorAdd13(input + x, output + x - width, xEnd - x, output + x);
break;
}
x = xEnd;
}
x = xEnd;
}
input += width;
output += width;
++y;
processedPixels += width;
++y;
if ((y & mask) == 0)
{
// Use the same mask, since tiles are squares.
predictorModeIdxBase += tilesPerRow;
if ((y & mask) == 0)
{
// Use the same mask, since tiles are squares.
predictorModeIdxBase += tilesPerRow;
}
}
}
output.CopyTo(pixelData);
// TODO: I can't see the equivalent code in the source?
outputSpan.CopyTo(pixelData);
}
public static void ExpandColorMap(int numColors, Span<uint> transformData, Span<uint> newColorMap)
@ -582,374 +595,352 @@ namespace SixLabors.ImageSharp.Formats.WebP.Lossless
return code;
}
private static void PredictorAdd0(Span<uint> input, int startIdx, int numberOfPixels, Span<uint> output)
[MethodImpl(InliningOptions.ShortMethod)]
private static void PredictorAdd0(uint* input, uint* upper, int numberOfPixels, uint* output)
{
int endIdx = startIdx + numberOfPixels;
uint pred = Predictor0;
for (int x = startIdx; x < endIdx; ++x)
for (int x = 0; x < numberOfPixels; ++x)
{
output[x] = AddPixels(input[x], pred);
output[x] = AddPixels(input[x], WebPConstants.ArgbBlack);
}
}
private static void PredictorAdd1(Span<uint> input, int startIdx, int numberOfPixels, Span<uint> output)
[MethodImpl(InliningOptions.ShortMethod)]
private static void PredictorAdd1(uint* input, uint* upper, int numberOfPixels, uint* output)
{
int endIdx = startIdx + numberOfPixels;
uint left = output[startIdx - 1];
for (int x = startIdx; x < endIdx; ++x)
uint left = output[-1];
for (int x = 0; x < numberOfPixels; ++x)
{
output[x] = left = AddPixels(input[x], left);
}
}
private static void PredictorAdd2(Span<uint> input, int startIdx, int numberOfPixels, int width, Span<uint> output)
[MethodImpl(InliningOptions.ShortMethod)]
private static void PredictorAdd2(uint* input, uint* upper, int numberOfPixels, uint* output)
{
int endIdx = startIdx + numberOfPixels;
int offset = startIdx - width;
for (int x = startIdx; x < endIdx; ++x)
for (int x = 0; x < numberOfPixels; ++x)
{
uint pred = Predictor2(output, offset++);
uint pred = Predictor2(output[x - 1], upper + x);
output[x] = AddPixels(input[x], pred);
}
}
private static void PredictorAdd3(Span<uint> input, int startIdx, int numberOfPixels, int width, Span<uint> output)
[MethodImpl(InliningOptions.ShortMethod)]
private static void PredictorAdd3(uint* input, uint* upper, int numberOfPixels, uint* output)
{
int endIdx = startIdx + numberOfPixels;
int offset = startIdx - width;
for (int x = startIdx; x < endIdx; ++x)
for (int x = 0; x < numberOfPixels; ++x)
{
uint pred = Predictor3(output, offset++);
uint pred = Predictor3(output[x - 1], upper + x);
output[x] = AddPixels(input[x], pred);
}
}
private static void PredictorAdd4(Span<uint> input, int startIdx, int numberOfPixels, int width, Span<uint> output)
[MethodImpl(InliningOptions.ShortMethod)]
private static void PredictorAdd4(uint* input, uint* upper, int numberOfPixels, uint* output)
{
int endIdx = startIdx + numberOfPixels;
int offset = startIdx - width;
for (int x = startIdx; x < endIdx; ++x)
for (int x = 0; x < numberOfPixels; ++x)
{
uint pred = Predictor4(output, offset++);
uint pred = Predictor4(output[x - 1], upper + x);
output[x] = AddPixels(input[x], pred);
}
}
private static void PredictorAdd5(Span<uint> input, int startIdx, int numberOfPixels, int width, Span<uint> output)
[MethodImpl(InliningOptions.ShortMethod)]
private static void PredictorAdd5(uint* input, uint* upper, int numberOfPixels, uint* output)
{
int endIdx = startIdx + numberOfPixels;
int offset = startIdx - width;
for (int x = startIdx; x < endIdx; ++x)
for (int x = 0; x < numberOfPixels; ++x)
{
uint pred = Predictor5(output[x - 1], output, offset++);
uint pred = Predictor5(output[x - 1], upper + x);
output[x] = AddPixels(input[x], pred);
}
}
private static void PredictorAdd6(Span<uint> input, int startIdx, int numberOfPixels, int width, Span<uint> output)
[MethodImpl(InliningOptions.ShortMethod)]
private static void PredictorAdd6(uint* input, uint* upper, int numberOfPixels, uint* output)
{
int endIdx = startIdx + numberOfPixels;
int offset = startIdx - width;
for (int x = startIdx; x < endIdx; ++x)
for (int x = 0; x < numberOfPixels; ++x)
{
uint pred = Predictor6(output[x - 1], output, offset++);
uint pred = Predictor6(output[x - 1], upper + x);
output[x] = AddPixels(input[x], pred);
}
}
private static void PredictorAdd7(Span<uint> input, int startIdx, int numberOfPixels, int width, Span<uint> output)
[MethodImpl(InliningOptions.ShortMethod)]
private static void PredictorAdd7(uint* input, uint* upper, int numberOfPixels, uint* output)
{
int endIdx = startIdx + numberOfPixels;
int offset = startIdx - width;
for (int x = startIdx; x < endIdx; ++x)
for (int x = 0; x < numberOfPixels; ++x)
{
uint pred = Predictor7(output[x - 1], output, offset++);
uint pred = Predictor7(output[x - 1], upper + x);
output[x] = AddPixels(input[x], pred);
}
}
private static void PredictorAdd8(Span<uint> input, int startIdx, int numberOfPixels, int width, Span<uint> output)
[MethodImpl(InliningOptions.ShortMethod)]
private static void PredictorAdd8(uint* input, uint* upper, int numberOfPixels, uint* output)
{
int endIdx = startIdx + numberOfPixels;
int offset = startIdx - width;
for (int x = startIdx; x < endIdx; ++x)
for (int x = 0; x < numberOfPixels; ++x)
{
uint pred = Predictor8(output, offset++);
uint pred = Predictor8(output[x - 1], upper + x);
output[x] = AddPixels(input[x], pred);
}
}
private static void PredictorAdd9(Span<uint> input, int startIdx, int numberOfPixels, int width, Span<uint> output)
[MethodImpl(InliningOptions.ShortMethod)]
private static void PredictorAdd9(uint* input, uint* upper, int numberOfPixels, uint* output)
{
int endIdx = startIdx + numberOfPixels;
int offset = startIdx - width;
for (int x = startIdx; x < endIdx; ++x)
for (int x = 0; x < numberOfPixels; ++x)
{
uint pred = Predictor9(output, offset++);
uint pred = Predictor9(output[x - 1], upper + x);
output[x] = AddPixels(input[x], pred);
}
}
private static void PredictorAdd10(Span<uint> input, int startIdx, int numberOfPixels, int width, Span<uint> output)
[MethodImpl(InliningOptions.ShortMethod)]
private static void PredictorAdd10(uint* input, uint* upper, int numberOfPixels, uint* output)
{
int endIdx = startIdx + numberOfPixels;
int offset = startIdx - width;
for (int x = startIdx; x < endIdx; ++x)
for (int x = 0; x < numberOfPixels; ++x)
{
uint pred = Predictor10(output[x - 1], output, offset++);
uint pred = Predictor10(output[x - 1], upper + x);
output[x] = AddPixels(input[x], pred);
}
}
private static void PredictorAdd11(Span<uint> input, int startIdx, int numberOfPixels, int width, Span<uint> output)
[MethodImpl(InliningOptions.ShortMethod)]
private static void PredictorAdd11(uint* input, uint* upper, int numberOfPixels, uint* output)
{
int endIdx = startIdx + numberOfPixels;
int offset = startIdx - width;
for (int x = startIdx; x < endIdx; ++x)
for (int x = 0; x < numberOfPixels; ++x)
{
uint pred = Predictor11(output[x - 1], output, offset++);
uint pred = Predictor11(output[x - 1], upper + x);
output[x] = AddPixels(input[x], pred);
}
}
private static void PredictorAdd12(Span<uint> input, int startIdx, int numberOfPixels, int width, Span<uint> output)
[MethodImpl(InliningOptions.ShortMethod)]
private static void PredictorAdd12(uint* input, uint* upper, int numberOfPixels, uint* output)
{
int endIdx = startIdx + numberOfPixels;
int offset = startIdx - width;
for (int x = startIdx; x < endIdx; ++x)
for (int x = 0; x < numberOfPixels; ++x)
{
uint pred = Predictor12(output[x - 1], output, offset++);
uint pred = Predictor12(output[x - 1], upper + x);
output[x] = AddPixels(input[x], pred);
}
}
private static void PredictorAdd13(Span<uint> input, int startIdx, int numberOfPixels, int width, Span<uint> output)
[MethodImpl(InliningOptions.ShortMethod)]
private static void PredictorAdd13(uint* input, uint* upper, int numberOfPixels, uint* output)
{
int endIdx = startIdx + numberOfPixels;
int offset = startIdx - width;
for (int x = startIdx; x < endIdx; ++x)
for (int x = 0; x < numberOfPixels; ++x)
{
uint pred = Predictor13(output[x - 1], output, offset++);
uint pred = Predictor13(output[x - 1], upper + x);
output[x] = AddPixels(input[x], pred);
}
}
[MethodImpl(InliningOptions.ShortMethod)]
public static uint Predictor2(Span<uint> top, int idx)
public static uint Predictor2(uint left, uint* top)
{
return top[idx];
return top[0];
}
[MethodImpl(InliningOptions.ShortMethod)]
public static uint Predictor3(Span<uint> top, int idx)
public static uint Predictor3(uint left, uint* top)
{
return top[idx + 1];
return top[1];
}
[MethodImpl(InliningOptions.ShortMethod)]
public static uint Predictor4(Span<uint> top, int idx)
public static uint Predictor4(uint left, uint* top)
{
return top[idx - 1];
return top[-1];
}
[MethodImpl(InliningOptions.ShortMethod)]
public static uint Predictor5(uint left, Span<uint> top, int idx)
public static uint Predictor5(uint left, uint* top)
{
uint pred = Average3(left, top[idx], top[idx + 1]);
return pred;
return Average3(left, top[0], top[1]);
}
[MethodImpl(InliningOptions.ShortMethod)]
public static uint Predictor6(uint left, Span<uint> top, int idx)
public static uint Predictor6(uint left, uint* top)
{
uint pred = Average2(left, top[idx - 1]);
return pred;
return Average2(left, top[-1]);
}
[MethodImpl(InliningOptions.ShortMethod)]
public static uint Predictor7(uint left, Span<uint> top, int idx)
public static uint Predictor7(uint left, uint* top)
{
uint pred = Average2(left, top[idx]);
return pred;
return Average2(left, top[0]);
}
[MethodImpl(InliningOptions.ShortMethod)]
public static uint Predictor8(Span<uint> top, int idx)
public static uint Predictor8(uint left, uint* top)
{
uint pred = Average2(top[idx - 1], top[idx]);
return pred;
return Average2(top[-1], top[0]);
}
[MethodImpl(InliningOptions.ShortMethod)]
public static uint Predictor9(Span<uint> top, int idx)
public static uint Predictor9(uint left, uint* top)
{
uint pred = Average2(top[idx], top[idx + 1]);
return pred;
return Average2(top[0], top[1]);
}
[MethodImpl(InliningOptions.ShortMethod)]
public static uint Predictor10(uint left, Span<uint> top, int idx)
public static uint Predictor10(uint left, uint* top)
{
uint pred = Average4(left, top[idx - 1], top[idx], top[idx + 1]);
return pred;
return Average4(left, top[-1], top[0], top[1]);
}
[MethodImpl(InliningOptions.ShortMethod)]
public static uint Predictor11(uint left, Span<uint> top, int idx)
public static uint Predictor11(uint left, uint* top)
{
uint pred = Select(top[idx], left, top[idx - 1]);
return pred;
return Select(top[0], left, top[-1]);
}
[MethodImpl(InliningOptions.ShortMethod)]
public static uint Predictor12(uint left, Span<uint> top, int idx)
public static uint Predictor12(uint left, uint* top)
{
uint pred = ClampedAddSubtractFull(left, top[idx], top[idx - 1]);
return pred;
return ClampedAddSubtractFull(left, top[0], top[-1]);
}
[MethodImpl(InliningOptions.ShortMethod)]
public static uint Predictor13(uint left, Span<uint> top, int idx)
public static uint Predictor13(uint left, uint* top)
{
uint pred = ClampedAddSubtractHalf(left, top[idx], top[idx - 1]);
return pred;
return ClampedAddSubtractHalf(left, top[0], top[-1]);
}
[MethodImpl(InliningOptions.ShortMethod)]
public static void PredictorSub0(Span<uint> input, int numPixels, Span<uint> output)
public static void PredictorSub0(uint* input, int numPixels, uint* output)
{
for (int i = 0; i < numPixels; i++)
for (int i = 0; i < numPixels; ++i)
{
output[i] = SubPixels(input[i], WebPConstants.ArgbBlack);
}
}
[MethodImpl(InliningOptions.ShortMethod)]
public static void PredictorSub1(Span<uint> input, int idx, int numPixels, Span<uint> output)
public static void PredictorSub1(uint* input, int numPixels, uint* output)
{
for (int i = 0; i < numPixels; i++)
for (int i = 0; i < numPixels; ++i)
{
output[i] = SubPixels(input[idx + i], input[idx + i - 1]);
output[i] = SubPixels(input[i], input[i - 1]);
}
}
[MethodImpl(InliningOptions.ShortMethod)]
public static void PredictorSub2(Span<uint> input, int idx, Span<uint> upper, int numPixels, Span<uint> output)
public static void PredictorSub2(uint* input, uint* upper, int numPixels, uint* output)
{
for (int x = 0; x < numPixels; x++)
for (int x = 0; x < numPixels; ++x)
{
uint pred = Predictor2(upper, x);
output[x] = SubPixels(input[idx + x], pred);
uint pred = Predictor2(input[x - 1], upper + x);
output[x] = SubPixels(input[x], pred);
}
}
[MethodImpl(InliningOptions.ShortMethod)]
public static void PredictorSub3(Span<uint> input, int idx, Span<uint> upper, int numPixels, Span<uint> output)
public static void PredictorSub3(uint* input, uint* upper, int numPixels, uint* output)
{
for (int x = 0; x < numPixels; x++)
for (int x = 0; x < numPixels; ++x)
{
uint pred = Predictor3(upper, x);
output[x] = SubPixels(input[idx + x], pred);
uint pred = Predictor3(input[x - 1], upper + x);
output[x] = SubPixels(input[x], pred);
}
}
[MethodImpl(InliningOptions.ShortMethod)]
public static void PredictorSub4(Span<uint> input, int idx, Span<uint> upper, int numPixels, Span<uint> output)
public static void PredictorSub4(uint* input, uint* upper, int numPixels, uint* output)
{
for (int x = 0; x < numPixels; x++)
for (int x = 0; x < numPixels; ++x)
{
uint pred = Predictor4(upper, x);
output[x] = SubPixels(input[idx + x], pred);
uint pred = Predictor4(input[x - 1], upper + x);
output[x] = SubPixels(input[x], pred);
}
}
[MethodImpl(InliningOptions.ShortMethod)]
public static void PredictorSub5(Span<uint> input, int idx, Span<uint> upper, int numPixels, Span<uint> output)
public static void PredictorSub5(uint* input, uint* upper, int numPixels, uint* output)
{
for (int x = 0; x < numPixels; x++)
for (int x = 0; x < numPixels; ++x)
{
uint pred = Predictor5(input[idx - 1], upper, x);
output[x] = SubPixels(input[idx + x], pred);
uint pred = Predictor5(input[x - 1], upper + x);
output[x] = SubPixels(input[x], pred);
}
}
[MethodImpl(InliningOptions.ShortMethod)]
public static void PredictorSub6(Span<uint> input, int idx, Span<uint> upper, int numPixels, Span<uint> output)
public static void PredictorSub6(uint* input, uint* upper, int numPixels, uint* output)
{
for (int x = 0; x < numPixels; x++)
for (int x = 0; x < numPixels; ++x)
{
uint pred = Predictor6(input[idx - 1], upper, x);
output[x] = SubPixels(input[idx + x], pred);
uint pred = Predictor6(input[x - 1], upper + x);
output[x] = SubPixels(input[x], pred);
}
}
[MethodImpl(InliningOptions.ShortMethod)]
public static void PredictorSub7(Span<uint> input, int idx, Span<uint> upper, int numPixels, Span<uint> output)
public static void PredictorSub7(uint* input, uint* upper, int numPixels, uint* output)
{
for (int x = 0; x < numPixels; x++)
for (int x = 0; x < numPixels; ++x)
{
uint pred = Predictor7(input[idx - 1], upper, x);
output[x] = SubPixels(input[idx + x], pred);
uint pred = Predictor7(input[x - 1], upper + x);
output[x] = SubPixels(input[x], pred);
}
}
[MethodImpl(InliningOptions.ShortMethod)]
public static void PredictorSub8(Span<uint> input, int idx, Span<uint> upper, int numPixels, Span<uint> output)
public static void PredictorSub8(uint* input, uint* upper, int numPixels, uint* output)
{
for (int x = 0; x < numPixels; x++)
for (int x = 0; x < numPixels; ++x)
{
uint pred = Predictor8(upper, x);
output[x] = SubPixels(input[idx + x], pred);
uint pred = Predictor8(input[x - 1], upper + x);
output[x] = SubPixels(input[x], pred);
}
}
[MethodImpl(InliningOptions.ShortMethod)]
public static void PredictorSub9(Span<uint> input, int idx, Span<uint> upper, int numPixels, Span<uint> output)
public static void PredictorSub9(uint* input, uint* upper, int numPixels, uint* output)
{
for (int x = 0; x < numPixels; x++)
for (int x = 0; x < numPixels; ++x)
{
uint pred = Predictor9(upper, x);
output[x] = SubPixels(input[idx + x], pred);
uint pred = Predictor9(input[x - 1], upper + x);
output[x] = SubPixels(input[x], pred);
}
}
[MethodImpl(InliningOptions.ShortMethod)]
public static void PredictorSub10(Span<uint> input, int idx, Span<uint> upper, int numPixels, Span<uint> output)
public static void PredictorSub10(uint* input, uint* upper, int numPixels, uint* output)
{
for (int x = 0; x < numPixels; x++)
for (int x = 0; x < numPixels; ++x)
{
uint pred = Predictor10(input[idx - 1], upper, x);
output[x] = SubPixels(input[idx + x], pred);
uint pred = Predictor10(input[x - 1], upper + x);
output[x] = SubPixels(input[x], pred);
}
}
[MethodImpl(InliningOptions.ShortMethod)]
public static void PredictorSub11(Span<uint> input, int idx, Span<uint> upper, int numPixels, Span<uint> output)
public static void PredictorSub11(uint* input, uint* upper, int numPixels, uint* output)
{
for (int x = 0; x < numPixels; x++)
for (int x = 0; x < numPixels; ++x)
{
uint pred = Predictor11(input[idx - 1], upper, x);
output[x] = SubPixels(input[idx + x], pred);
uint pred = Predictor11(input[x - 1], upper + x);
output[x] = SubPixels(input[x], pred);
}
}
[MethodImpl(InliningOptions.ShortMethod)]
public static void PredictorSub12(Span<uint> input, int idx, Span<uint> upper, int numPixels, Span<uint> output)
public static void PredictorSub12(uint* input, uint* upper, int numPixels, uint* output)
{
for (int x = 0; x < numPixels; x++)
for (int x = 0; x < numPixels; ++x)
{
uint pred = Predictor12(input[idx - 1], upper, x);
output[x] = SubPixels(input[idx + x], pred);
uint pred = Predictor12(input[x - 1], upper + x);
output[x] = SubPixels(input[x], pred);
}
}
[MethodImpl(InliningOptions.ShortMethod)]
public static void PredictorSub13(Span<uint> input, int idx, Span<uint> upper, int numPixels, Span<uint> output)
public static void PredictorSub13(uint* input, uint* upper, int numPixels, uint* output)
{
for (int x = 0; x < numPixels; x++)
for (int x = 0; x < numPixels; ++x)
{
uint pred = Predictor13(input[idx - 1], upper, x);
output[x] = SubPixels(input[idx + x], pred);
uint pred = Predictor13(input[x - 1], upper + x);
output[x] = SubPixels(input[x], pred);
}
}

392
src/ImageSharp/Formats/WebP/Lossless/PredictorEncoder.cs
View File

@ -10,7 +10,7 @@ namespace SixLabors.ImageSharp.Formats.WebP.Lossless
/// <summary>
/// Image transform methods for the lossless webp encoder.
/// </summary>
internal static class PredictorEncoder
internal static unsafe class PredictorEncoder
{
private const int GreenRedToBlueNumAxis = 8;
@ -27,27 +27,61 @@ namespace SixLabors.ImageSharp.Formats.WebP.Lossless
/// with respect to predictions. If nearLosslessQuality &lt; 100, applies
/// near lossless processing, shaving off more bits of residuals for lower qualities.
/// </summary>
public static void ResidualImage(int width, int height, int bits, Span<uint> argb, Span<uint> argbScratch, Span<uint> image, int nearLosslessQuality, bool exact, bool usedSubtractGreen)
public static void ResidualImage(
int width,
int height,
int bits,
Span<uint> argb,
Span<uint> argbScratch,
Span<uint> image,
int nearLosslessQuality,
bool exact,
bool usedSubtractGreen)
{
int tilesPerRow = LosslessUtils.SubSampleSize(width, bits);
int tilesPerCol = LosslessUtils.SubSampleSize(height, bits);
int maxQuantization = 1 << LosslessUtils.NearLosslessBits(nearLosslessQuality);
// TODO: Can we optimize this?
var histo = new int[4][];
for (int i = 0; i < 4; i++)
{
histo[i] = new int[256];
}
for (int tileY = 0; tileY < tilesPerCol; tileY++)
// TODO: Low Effort
for (int tileY = 0; tileY < tilesPerCol; ++tileY)
{
for (int tileX = 0; tileX < tilesPerRow; tileX++)
for (int tileX = 0; tileX < tilesPerRow; ++tileX)
{
int pred = GetBestPredictorForTile(width, height, tileX, tileY, bits, histo, argbScratch, argb, maxQuantization, exact, usedSubtractGreen, image);
int pred = GetBestPredictorForTile(
width,
height,
tileX,
tileY,
bits,
histo,
argbScratch,
argb,
maxQuantization,
exact,
usedSubtractGreen,
image);
image[(tileY * tilesPerRow) + tileX] = (uint)(WebPConstants.ArgbBlack | (pred << 8));
}
}
CopyImageWithPrediction(width, height, bits, image, argbScratch, argb, maxQuantization, exact, usedSubtractGreen);
CopyImageWithPrediction(
width,
height,
bits,
image,
argbScratch,
argb,
maxQuantization,
exact,
usedSubtractGreen);
}
public static void ColorSpaceTransform(int width, int height, int bits, int quality, Span<uint> argb, Span<uint> image)
@ -261,113 +295,130 @@ namespace SixLabors.ImageSharp.Formats.WebP.Lossless
/// the deviation further to pixels which depend on the current pixel for their
/// predictions.
/// </summary>
private static void GetResidual(int width, int height, Span<uint> upperRow, Span<uint> currentRow, Span<byte> maxDiffs, int mode, int xStart, int xEnd, int y, int maxQuantization, bool exact, bool usedSubtractGreen, Span<uint> output)
private static void GetResidual(
int width,
int height,
Span<uint> upperRowSpan,
Span<uint> currentRowSpan,
Span<byte> maxDiffs,
int mode,
int xStart,
int xEnd,
int y,
int maxQuantization,
bool exact,
bool usedSubtractGreen,
Span<uint> output)
{
if (exact)
{
PredictBatch(mode, xStart, y, xEnd - xStart, currentRow, upperRow, output);
PredictBatch(mode, xStart, y, xEnd - xStart, currentRowSpan, upperRowSpan, output);
}
else
{
for (int x = xStart; x < xEnd; x++)
fixed (uint* currentRow = currentRowSpan)
fixed (uint* upperRow = upperRowSpan)
{
uint predict = 0;
uint residual;
if (y == 0)
{
predict = (x == 0) ? WebPConstants.ArgbBlack : currentRow[x - 1]; // Left.
}
else if (x == 0)
{
predict = upperRow[x]; // Top.
}
else
for (int x = xStart; x < xEnd; ++x)
{
switch (mode)
uint predict = 0;
uint residual;
if (y == 0)
{
case 0:
predict = WebPConstants.ArgbBlack;
break;
case 1:
predict = currentRow[x - 1];
break;
case 2:
predict = LosslessUtils.Predictor2(upperRow, x);
break;
case 3:
predict = LosslessUtils.Predictor3(upperRow, x);
break;
case 4:
predict = LosslessUtils.Predictor4(upperRow, x);
break;
case 5:
predict = LosslessUtils.Predictor5(currentRow[x - 1], upperRow, x);
break;
case 6:
predict = LosslessUtils.Predictor6(currentRow[x - 1], upperRow, x);
break;
case 7:
predict = LosslessUtils.Predictor7(currentRow[x - 1], upperRow, x);
break;
case 8:
predict = LosslessUtils.Predictor8(upperRow, x);
break;
case 9:
predict = LosslessUtils.Predictor9(upperRow, x);
break;
case 10:
predict = LosslessUtils.Predictor10(currentRow[x - 1], upperRow, x);
break;
case 11:
predict = LosslessUtils.Predictor11(currentRow[x - 1], upperRow, x);
break;
case 12:
predict = LosslessUtils.Predictor12(currentRow[x - 1], upperRow, x);
break;
case 13:
predict = LosslessUtils.Predictor13(currentRow[x - 1], upperRow, x);
break;
predict = (x == 0) ? WebPConstants.ArgbBlack : currentRow[x - 1]; // Left.
}
else if (x == 0)
{
predict = upperRow[x]; // Top.
}
else
{
switch (mode)
{
case 0:
predict = WebPConstants.ArgbBlack;
break;
case 1:
predict = currentRow[x - 1];
break;
case 2:
predict = LosslessUtils.Predictor2(currentRow[x - 1], upperRow + x);
break;
case 3:
predict = LosslessUtils.Predictor3(currentRow[x - 1], upperRow + x);
break;
case 4:
predict = LosslessUtils.Predictor4(currentRow[x - 1], upperRow + x);
break;
case 5:
predict = LosslessUtils.Predictor5(currentRow[x - 1], upperRow + x);
break;
case 6:
predict = LosslessUtils.Predictor6(currentRow[x - 1], upperRow + x);
break;
case 7:
predict = LosslessUtils.Predictor7(currentRow[x - 1], upperRow + x);
break;
case 8:
predict = LosslessUtils.Predictor8(currentRow[x - 1], upperRow + x);
break;
case 9:
predict = LosslessUtils.Predictor9(currentRow[x - 1], upperRow + x);
break;
case 10:
predict = LosslessUtils.Predictor10(currentRow[x - 1], upperRow + x);
break;
case 11:
predict = LosslessUtils.Predictor11(currentRow[x - 1], upperRow + x);
break;
case 12:
predict = LosslessUtils.Predictor12(currentRow[x - 1], upperRow + x);
break;
case 13:
predict = LosslessUtils.Predictor13(currentRow[x - 1], upperRow + x);
break;
}
}
}
if (maxQuantization == 1 || mode == 0 || y == 0 || y == height - 1 || x == 0 || x == width - 1)
{
residual = LosslessUtils.SubPixels(currentRow[x], predict);
}
else
{
residual = NearLossless(currentRow[x], predict, maxQuantization, maxDiffs[x], usedSubtractGreen);
if (maxQuantization == 1 || mode == 0 || y == 0 || y == height - 1 || x == 0 || x == width - 1)
{
residual = LosslessUtils.SubPixels(currentRow[x], predict);
}
else
{
residual = NearLossless(currentRow[x], predict, maxQuantization, maxDiffs[x], usedSubtractGreen);
// Update the source image.
currentRow[x] = LosslessUtils.AddPixels(predict, residual);
// Update the source image.
currentRow[x] = LosslessUtils.AddPixels(predict, residual);
// x is never 0 here so we do not need to update upperRow like below.
}
// x is never 0 here so we do not need to update upperRow like below.
}
if ((currentRow[x] & MaskAlpha) == 0)
{
// If alpha is 0, cleanup RGB. We can choose the RGB values of the
// residual for best compression. The prediction of alpha itself can be
// non-zero and must be kept though. We choose RGB of the residual to be
// 0.
residual &= MaskAlpha;
// Update the source image.
currentRow[x] = predict & ~MaskAlpha;
// The prediction for the rightmost pixel in a row uses the leftmost
// pixel
// in that row as its top-right context pixel. Hence if we change the
// leftmost pixel of current_row, the corresponding change must be
// applied
// to upperRow as well where top-right context is being read from.
if (x == 0 && y != 0)
if ((currentRow[x] & MaskAlpha) == 0)
{
upperRow[width] = currentRow[0];
// If alpha is 0, cleanup RGB. We can choose the RGB values of the
// residual for best compression. The prediction of alpha itself can be
// non-zero and must be kept though. We choose RGB of the residual to be
// 0.
residual &= MaskAlpha;
// Update the source image.
currentRow[x] = predict & ~MaskAlpha;
// The prediction for the rightmost pixel in a row uses the leftmost
// pixel
// in that row as its top-right context pixel. Hence if we change the
// leftmost pixel of current_row, the corresponding change must be
// applied
// to upperRow as well where top-right context is being read from.
if (x == 0 && y != 0)
{
upperRow[width] = currentRow[0];
}
}
}
output[x - xStart] = residual;
output[x - xStart] = residual;
}
}
}
}
@ -486,14 +537,18 @@ namespace SixLabors.ImageSharp.Formats.WebP.Lossless
Span<uint> upperRow = argbScratch;
Span<uint> currentRow = upperRow.Slice(width + 1);
Span<byte> currentMaxDiffs = MemoryMarshal.Cast<uint, byte>(currentRow.Slice(width + 1));
// TODO: This should be wrapped in a condition.
Span<byte> lowerMaxDiffs = currentMaxDiffs.Slice(width);
for (int y = 0; y < height; y++)
for (int y = 0; y < height; ++y)
{
Span<uint> tmp32 = upperRow;
upperRow = currentRow;
currentRow = tmp32;
Span<uint> src = argb.Slice(y * width, width + ((y + 1) < height ? 1 : 0));
src.CopyTo(currentRow);
// TODO: Near lossless conditional.
if (maxQuantization > 1)
{
// Compute max_diffs for the lower row now, because that needs the
@ -537,77 +592,90 @@ namespace SixLabors.ImageSharp.Formats.WebP.Lossless
}
}
private static void PredictBatch(int mode, int xStart, int y, int numPixels, Span<uint> current, Span<uint> upper, Span<uint> output)
private static void PredictBatch(
int mode,
int xStart,
int y,
int numPixels,
Span<uint> currentSpan,
Span<uint> upperSpan,
Span<uint> outputSpan)
{
if (xStart == 0)
fixed (uint* current = currentSpan)
fixed (uint* upper = upperSpan)
fixed (uint* outputFixed = outputSpan)
{
if (y == 0)
uint* output = outputFixed;
if (xStart == 0)
{
// ARGB_BLACK.
LosslessUtils.PredictorSub0(current, 1, output);
if (y == 0)
{
// ARGB_BLACK.
LosslessUtils.PredictorSub0(current, 1, output);
}
else
{
// Top one.
LosslessUtils.PredictorSub2(current, upper, 1, output);
}
++xStart;
++output;
--numPixels;
}
else
if (y == 0)
{
// Top one.
LosslessUtils.PredictorSub2(current, 0, upper, 1, output);
// Left one.
LosslessUtils.PredictorSub1(current + xStart, numPixels, output);
}
xStart++;
output = output.Slice(1);
numPixels--;
}
if (y == 0)
{
// Left one.
LosslessUtils.PredictorSub1(current, xStart, numPixels, output);
}
else
{
switch (mode)
else
{
case 0:
LosslessUtils.PredictorSub0(current, numPixels, output);
break;
case 1:
LosslessUtils.PredictorSub1(current, xStart, numPixels, output);
break;
case 2:
LosslessUtils.PredictorSub2(current, xStart, upper.Slice(xStart), numPixels, output);
break;
case 3:
LosslessUtils.PredictorSub3(current, xStart, upper.Slice(xStart), numPixels, output);
break;
case 4:
LosslessUtils.PredictorSub4(current, xStart, upper.Slice(xStart), numPixels, output);
break;
case 5:
LosslessUtils.PredictorSub5(current, xStart, upper.Slice(xStart), numPixels, output);
break;
case 6:
LosslessUtils.PredictorSub6(current, xStart, upper.Slice(xStart), numPixels, output);
break;
case 7:
LosslessUtils.PredictorSub7(current, xStart, upper.Slice(xStart), numPixels, output);
break;
case 8:
LosslessUtils.PredictorSub8(current, xStart, upper.Slice(xStart), numPixels, output);
break;
case 9:
LosslessUtils.PredictorSub9(current, xStart, upper.Slice(xStart), numPixels, output);
break;
case 10:
LosslessUtils.PredictorSub10(current, xStart, upper.Slice(xStart), numPixels, output);
break;
case 11:
LosslessUtils.PredictorSub11(current, xStart, upper.Slice(xStart), numPixels, output);
break;
case 12:
LosslessUtils.PredictorSub12(current, xStart, upper.Slice(xStart), numPixels, output);
break;
case 13:
LosslessUtils.PredictorSub13(current, xStart, upper.Slice(xStart), numPixels, output);
break;
switch (mode)
{
case 0:
LosslessUtils.PredictorSub0(current, numPixels, output);
break;
case 1:
LosslessUtils.PredictorSub1(current + xStart, numPixels, output);
break;
case 2:
LosslessUtils.PredictorSub2(current + xStart, upper + xStart, numPixels, output);
break;
case 3:
LosslessUtils.PredictorSub3(current + xStart, upper + xStart, numPixels, output);
break;
case 4:
LosslessUtils.PredictorSub4(current + xStart, upper + xStart, numPixels, output);
break;
case 5:
LosslessUtils.PredictorSub5(current + xStart, upper + xStart, numPixels, output);
break;
case 6:
LosslessUtils.PredictorSub6(current + xStart, upper + xStart, numPixels, output);
break;
case 7:
LosslessUtils.PredictorSub7(current + xStart, upper + xStart, numPixels, output);
break;
case 8:
LosslessUtils.PredictorSub8(current + xStart, upper + xStart, numPixels, output);
break;
case 9:
LosslessUtils.PredictorSub9(current + xStart, upper + xStart, numPixels, output);
break;
case 10:
LosslessUtils.PredictorSub10(current + xStart, upper + xStart, numPixels, output);
break;
case 11:
LosslessUtils.PredictorSub11(current + xStart, upper + xStart, numPixels, output);
break;
case 12:
LosslessUtils.PredictorSub12(current + xStart, upper + xStart, numPixels, output);
break;
case 13:
LosslessUtils.PredictorSub13(current + xStart, upper + xStart, numPixels, output);
break;
}
}
}
}
@ -627,7 +695,7 @@ namespace SixLabors.ImageSharp.Formats.WebP.Lossless
right = AddGreenToBlueAndRed(right);
}
for (int x = 1; x < width - 1; x++)
for (int x = 1; x < width - 1; ++x)
{
uint up = argb[-stride + x];
uint down = argb[stride + x];

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