Lossless Compression of Color Sequences Using Optimal Linear Prediction Theory

In this paper, we present a novel technique that uses the optimal linear prediction theory to exploit all the existing redundancies in a color video sequence for lossless compression purposes. The main idea is to introduce the spatial, the spectral, and the temporal correlations in the autocorrelation matrix estimate. In this way, we calculate the cross correlations between adjacent frames and adjacent color components to improve the prediction, i.e., reduce the prediction error energy. The residual image is then coded using a context-based Golomb-Rice coder, where the error modeling is provided by a quantized version of the local prediction error variance. Experimental results show that the proposed algorithm achieves good compression ratios and it is robust against the scene change problem.