Reversible compression of a video sequence

In addition to spatial redundancies, a sequence of video images contain spectral and temporal redundancies, which standard lossless compression techniques fail to take into account. In this paper we propose and investigate lossless compression schemes for a video sequence. Prediction schemes are presented that exploit temporal correlations and spectral correlation as well as spatial correlations. These schemes are based on the notion of a scan model which we have defined in our earlier work. A scan model effectively captures the inherent structure in an image and by using optimal scan models from spectrally and temporally adjacent frames to perform prediction in the current frame provides an effective means of utilizing spectral and temporal correlations. We also present a simpler approximation to this technique that selects an appropriate predictor from a set, by making use of information in neighboring frames. Besides effective prediction techniques, we also include a simple error modeling step that takes into account prediction errors made in spectrally and/or temporally adjacent pixels in order to efficiently encode the prediction residual. Implementation results on standard test sequences indicate that significant improvements can be obtained by the proposed techniques.