An online SVM based side information creation for efficient distributed scalable video coding

With the significant increase of the network heterogeneity and the wide use of emerging video applications such as wireless sensor networks, video surveillance systems or remote sensing, the Distributed Scalable Video Coding (DSVC) is a potential solution for efficiently transmitting and storing video data due to its high compression efficiency and low encoding complexity capabilities. In DSVC framework, Side Information (SI), created at the decoder side by exploiting the temporal and inter-layer correlations between decoded frames, plays an important role as it directly affects to the final DSVC coding performance. Therefore, this paper proposes a novel SI creation solution which explicitly formulates the SI creation as a classification problem and employs an online learning Support Vector Machine (SVM) engine to fuse several SI candidates. Experiments conducted for a rich set of test sequences show that the proposed SI creation solution significantly outperforms the previous DSVC SI creation methods in terms of SI quality while slightly introducing the computational complexity.