Key-frame reference selection for non-feedback video communication

摘要

This article addresses the problem of reference picture optimization in video communication over error prone networks. A novel estimation model for transmission distortion is proposed. This model is capable of recursively estimating the overall end-to-end distortion caused by quantization, error propagation, and error concealment. Simulation results show that this model can accurately estimate channel distortion. Then, based on the distortion estimation model, a new non-feedback key-frame reference picture selection (KRPS) algorithm is developed. The optimum reference picture minimizes the transmission distortion under the rate-distortion optimization framework. Extensive experiment results demonstrate that the proposed KRPS algorithm substantially achieves more peak signal to noise ratio (PSNR) gain over traditional prediction, especially in low bit-rate transmission.