Low-complexity frame-level joint source-channel distortion optimal, adaptive intra refresh

Error resilient, low latency video coding for interactive video applications requires progressive intra coding of macroblocks (MBs) to contain the error propagation. Both refresh MB selection (RMS) and refresh rate selection (RRS) impact the subjective video quality in the presence of packet losses. Joint source-channel rate distortion optimization methods attempt to find the best trade-off between compression efficiency and end-to-end distortion at an MB-level and are typically computationally expensive in addition to not being optimal at a picture level. While probabilistic error propagation tracking is used for refresh MB selection in previous work, these picture-level optimal RRS methods model source-channel distortion by mimicking the effect of periodic intra frame coding which does not match well with content adaptive refresh MB selection. In this paper, we propose a frame-level approach to RRS that aligns the joint source-channel rate-distortion trade-off modeling with an enhanced RMS process to achieve an optimal end-to-end distortion that is content, bit-rate and channel adaptive. For typical videoconferencing content, the proposed approach is quite low in complexity, works on par with off-line multi-pass identification of the optimal fixed refresh rate and is quite competitive when compared to the H.264 joint modelpsilas lossy rate-distortion optimization technique.