Deterministic annealing based design of error resilient predictive compression systems
This paper considers near optimal design of predictive compression system that accounts for packet loss over unreliable networks. Major challenges to address include, propagation of errors due to packet loss through the prediction loop, mismatch between statistics used for design and during operation, and above all a cost function that is fraught with poor local minima. Accurately estimating and minimizing the end-to-end distortion (EED), in combination with asymptotic closed-loop (ACL) design that employs open-loop iterations, but mimics closed-loop operation on convergence, was proposed to address the first two challenges. However the severe non-convexity of the cost function, especially due to the piece-wise linear nature of the quantizer function, makes this a particularly challenging optimization problem. We propose to tackle this via a new design approach in the deterministic annealing framework to avoid poor local minima, coupled with the ACL approach to minimize EED estimate. This effectively addresses all the major challenges, and leads to a near optimal design of error-resilient predictive compression system. Substantial performance improvement obtained in experimental evaluations demonstrates the efficacy of the proposed approach.