C# predictive coding for image compression
I've been playing with Huffman Compression on images to reduce size while maintaining a lossless image, but I've also read that you can use predictive coding to further compress image data by reducing entropy.
From what I understand, in the lossless JPEG standard, each pixel is predicted as the weighted average of the adjacent 4 pixels already encountered in raster order (three above and one to the left). e.g., trying to predict the value of a pixel a based on preceding pixels, x, to the left as well as above a :
x x x
x a
Then calculate and encode the residual (difference between predicted and actual value).
But what I don't get is if the average 4 neighbor pixels aren't a multiple of 4, you'd get a fraction right? Should that fraction be ignored? If so, would the proper encoding of an 8 bit image (saved in a byte[]
) be something like:
public static void Encode(byte[] buffer, int width, int height)
{
var tempBuff = new byte[buffer.Length];
for (int i = 0; i < buffer.Length; i++)
{
tempBuff[i] = buffer[i];
}
for (int i = 1; i < height; i++)
{
for (int j = 1; j < width - 1; j++)
{
int offsetUp = ((i - 1) * width) + (j - 1);
int offset = (i * width) + (j - 1);
int a = tempBuff[offsetUp];
int b = tempBuff[offsetUp + 1];
int c = tempBuff[offsetUp + 2];
int d = tempBuff[offset];
int pixel = tempBuff[offset + 1];
var ave = (a + b + c + d) / 4;
var val = (byte)(ave - pixel);
buffer[offset + 1] = val;
}
}
}
public static void Decode(byte[] buffer, int width, int height)
{
for (int i = 1; i < height; i++)
{
for (int j = 1; j < width - 1; j++)
{
int offsetUp = ((i - 1) * width) + (j - 1);
int offset = (i * width) + (j - 1);
int a = buffer[offsetUp];
int b = buffer[offsetUp + 1];
int c = buffer[offsetUp + 2];
int d = buffer[offset];
int pixel = buffer[offset + 1];
var ave = (a + b + c + d) / 4;
var val = (byte)(ave - pixel);
buffer[offset + 1] = val;
}
}
}
I don't see how this really will reduce entropy? How will this help compress my images furth开发者_StackOverflow中文版er while still being lossless?
Thanks for any enlightenment
EDIT:
So after playing with the predictive coding images, I noticed that the histogram data shows a lot of +-1's of the varous pixels. This reduces entropy quite a bit in some cases. Here is a screenshot:
Yes, just truncate. Doesn't matter because you store the difference. It reduces entropy because you only store small values, a lot of them will be -1, 0 or 1. There are a couple of off-by-one bugs in your snippet btw.
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