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This paper focuses on prediction optimality in spatially scalable video coding. It draws inspiration from an estimation-theoretic prediction framework for quality (SNR) scalability earlier developed by our group, 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...
A novel scalable coding approach is proposed for video transmission over lossy networks, which builds on two estimation-theoretic (ET) paradigms previously developed by our group: (1) an ET approach to enhancement layer prediction in scalable video coding (ET-SVC) that optimally combines all available information from both the current base layer and prior enhancement layer frames, and (2) the spectral...
End-to-end distortion estimation is critical to effective error-resilient video coding. The recursive optimal per-pixel estimate (ROPE) is a known approach to compute up to second moments of decoder-reconstructed pixels, and thereby optimally estimate the distortion. ROPE accurately accounts for encoding/decoding operations that are recursive in the pixel domain, and their interaction with packet...
Current video coding schemes employ motion compensation to exploit the fact that the signal forms an auto-regressive process along the motion trajectory, and remove temporal redundancies with prior reconstructed samples via prediction. However, the decoder may, in principle, also exploit correlations with received encoding information of future frames. In contrast to current decoders that reconstruct...
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