Robust Video Region-of-Interest Coding Based on Leaky Prediction


A video region-of-interest (ROI) scalable coding scheme can ensure the priority of ROI. Error protection schemes can be used to guarantee the correct receipt of the ROI stream when transporting ROI scalable video over an error-prone network. However, we find that the correct receipt of ROI bitstreams cannot ensure the correct decoding of ROI due to the unique issue of the cross error propagation between ROI and background in ROI scalable coding. In this letter, we propose an ROI scalable coding framework based on leaky prediction (LP) for robustly transporting video over an error-prone network. Although several LP approaches have been proposed to improve layered coding, they cannot be applied to ROI scalable coding straightforwardly due to the cross error propagation issue. We deploy a leaky factor to weigh the two predictions: one from the constrained motion estimation (ME) within the ROI layer of the reference frame, and the other from the unrestricted ME in the overall reference frame. Simulation results show that the proposed scheme enhances the robustness of ROI scalability while maintaining coding efficiency.

IEEE Transactions on Circuits and Systems for Video Technology
Li Song
Li Song
Professor, IEEE Senior Member

Professor, Doctoral Supervisor, the Deputy Director of the Institute of Image Communication and Network Engineering of Shanghai Jiao Tong University, the Double-Appointed Professor of the Institute of Artificial Intelligence and the Collaborative Innovation Center of Future Media Network, the Deputy Secretary-General of the China Video User Experience Alliance and head of the standards group.