A Unified Estimation-Theoretic Framework for Error-Resilient Scalable Video Coding

Abstract

A novel scalable video coding (SVC) scheme is proposed for video transmission over loss networks, which builds on an estimation-theoretic (ET) framework for optimal prediction and error concealment, given all available information from both the current base layer and prior enhancement layer frames. It incorporates a recursive end-to-end distortion estimation technique, namely, the spectral coefficient-wise optimal recursive estimate (SCORE), which accounts for all ET operations and tracks the first and second moments of decoder reconstructed transform coefficients. The overall framework enables optimization of ET-SVC systems for transmission over lossy networks, while accounting for all relevant conditions including the effects of quantization, channel loss, concealment, and error propagation. It thus resolves longstanding difficulties in combining truly optimal prediction and concealment with optimal end-to-end distortion and error-resilient SVC coding decisions. Experiments demonstrate that the proposed scheme offers substantial performance gains over existing error-resilient SVC systems, under a wide range of packet loss and bit rates.