An estimation-theoretic approach to spatially scalable video coding
This paper focuses on prediction optimality in spatially scalable video coding. It is inspired by the earlier estimation-theoretic prediction framework developed by our group for quality (SNR) scalability, which achieved optimality by fully accounting for relevant information from the current base layer (e.g., quantization intervals) and the enhancement layer, to efficiently calculate the conditional expectation that forms the optimal predictor. It was central to that approach that all layers reconstruct approximations to the same original transform coefficient. In spatial scalability, however, the layers encode different resolution versions of the signal. To approach optimality in enhancement layer prediction, the current work departs from existing spatially scalable codecs that employ pixel-domain resampling to perform inter-layer prediction. Instead, it incorporates a transform-domain resampling technique that ensures that the base layer quantization intervals are accessible and usable at the enhancement layer, which in conjunction with prior enhancement layer information, enable optimal prediction. Simulations provide experimental evidence that the proposed approach achieves substantial enhancement layer coding gains over the standard.