We extend a stochastic model of hierarchical dependencies between wavelet coefficients of still images to the spatiotemporal decomposition of video sequences, obtained by a motion-compensated wavelet decomposition. We propose new estimators for the parameters of this model which provide better statistical performances. Based on this model, we deduce an optimal predictor of missing samples in the spatiotemporal wavelet domain and use it in two applications: quality enhancement and error concealment of scalable video transmitted over packet networks. Simulation results show significant quality improvement achieved by this technique with different packetization strategies for a scalable video bit stream.
A scalable video coder consisting of motion-compensated temporal filtering coupled with structured vector quantization plus a linear mapping of quantizer indexes that minimizes simultaneously source and channel distortions is presented. The linear index assignment takes the form of either a direct, uncoded mapping or a coded mapping via Reed-Muller codes. Experimental results compare the proposed system to a similar scheme using unstructured vector quantization as well as to a prominent scalable video coder protected by more traditional convolutional codes. The proposed system consistently outperforms the other two schemes by a significant margin for very noisy channel conditions. Index Terms-Joint source-channel coding (JSCC), scalable video coding.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.