# How does a predictive coding aid in lossless compression?

I'm working on this lab where we need to apply a lossless predictive coding to an image before compressing it (with Huffman, or some other lossless compression algorithm).

From the example seen below, it's pretty clear that by pre-processing the image with predictive coding, we've modified its histogram and concentrated all of its grey levels around 0. But why exactly does this aid compression?

Is there maybe a formula to determine the compression rate of Huffman, knowing the standard deviation and entropy of the original image? Otherwise, why would the compression ratio be any different; it's not like the range of values has changed between the original image and pre-processed image. Huffman coding, as usually applied, only considers the distribution of singletons. If $$X$$ is the distribution of a random singleton, then Huffman coding uses between $$H(X)$$ and $$H(X)+1$$ bits per singleton, where $$H(\cdot)$$ is the (log 2) entropy function.
In contrast, predictive coding can take into account correlations across data points. As a simple example, consider the following sequence: $$0,1,2,\ldots,255,0,1,2,\ldots,255,\ldots$$ Huffman coding would use 8 bits per unit of data, whereas with predictive coding we could get potentially to $$O(\log n)$$ bits for the entire sequence.