Important macroblock distinction model for multi-view plus depth video transmission over error-prone network

Multi-view plus depth (MVD) video is an efficient three dimensional (3D) video representation format that allows the sender only transmit two pairs of texture and depth videos, arbitrary virtual view can be synthesized at the receiver. However, constrained by the limited-bandwidth, inevitable packet loss will induce transmission distortion, which will propagate to virtual views thereby affects user’s stereoscopic perception. In order to relieve the virtual view’s quality degradation induced by packet loss, an important macroblock (MB) distinction model for both texture and depth videos is proposed. MBs with low important level will be actively discarded once congestion occurs. The model includes two main parts: Firstly, by considering temporal and spatial correlation of the coding structure and the distortion diffusion due to lost packets, a transmission distortion model is proposed. Secondly, a gradient based synthesis distortion model is adopted to analyze the distortion induced by depth-error. Finally, a low-complexity important MB distinction model is proposed for MVD video transmission. Experiment results show that, compared with random packet loss condition, Peak Signal-to-Noise Ratio (PSNR) of the virtual view increase by up to 15.65 dB at 20% packet loss rate, both objective and subjective quality of the virtual view are close to error-free transmission.