Asymptotic closed-loop design of error resilient predictive compression systems
Prediction is used in virtually all compression systems. When such a compressed signal is transmitted over unreliable networks, packet losses can lead to significant error propagation through the prediction loop. Despite this, the conventional design technique completely ignores the effect of packet losses, and estimates the prediction parameters to minimize the mean squared prediction error, and optimizes the quantizer to minimize the reconstruction error at the encoder. While some design techniques have been proposed to accurately estimate and minimize the end-to-end distortion at the decoder that accounts for packet losses, they operate in a closed-loop, which introduces a mismatch between statistics used for design and statistics used in operation, causing a negative impact on convergence and stability of the design procedure. Instead, we propose in this paper an effective technique for predictive compression system design that accounts for the instability caused by error propagation due to packet losses, and enjoys stable statistics during design by employing open-loop iterations that on convergence mimic closed-loop operation. Simulation results for a compression system with a first order linear predictor demonstrate the utility of the proposed approach, which offers significant performance improvements over existing design techniques.