Recursive end-to-end distortion estimation for error-resilient adaptive predictive compression systems

Linear prediction is widely used in speech, audio and video coding systems. Predictive coders often operate over unreliable channels or networks prone to packet loss, wherein errors propagate through the prediction loop and may catastrophically degrade the reconstructed signal at the decoder. To mitigate this problem, end-to-end distortion (EED) estimation, accounting for error propagation and concealment at the decoder, has been developed for video coding, and enables optimal rate-distortion (RD) decisions at the encoder. However, this approach was limited to the video coder's simple setting of a single tap constant coefficient temporal predictor. This paper considerably generalizes the framework to account for: i) high order prediction filters, and ii) filter adaptation to local signal statistics. Specifically, we propose to simultaneously track the decoder statistics of the reconstructed signal and the prediction parameters, which enable effective estimation of the overall EED. We first demonstrate the accuracy of the EED estimate in comparison to extensive simulation of transmission through a lossy network. Finally, experimental results demonstrate how this EED estimate can be leveraged, by an encoder with short and long term linear prediction, to improve RD decisions and achieve major performance gains.