A generic context model for uniform-reconstruction based SNR-scalable representations of residual texture signals

SNR scalability involves refinement of residual texture information. The entropy coding of texture refinement information in the scalable video coding (SVC) extension of H.264/AVC relies on a simple statistical model that is tuned to an encoder-specific way of quantization for generating a single SNR layer on top of the backward compatible base layer. For SNR layers above the first layer, we demonstrate how and why the current model fails to properly reflect the statistics of texture refinement information. By analyzing the specific properties of the typical quantization process in SNR scalable coding of SVC, we are able to derive a generic modeling approach for coding of refinement symbols, independent of the specific choice of dead-zone parameters and classification rules. Experimental results show bit rate savings of around 5% relative to the total bit rate and averaged over a representative set of video sequences in a test scenario including up to three SNR layers.