A Deterministic Annealing Approach to Switched Predictor Design for Adaptive Compression Systems

Abstract

Adaptive prediction is important in the compression of non-stationary signals, and a common remedy is to switch between appropriately designed prediction modes. This paper presents a near optimal procedure to design prediction modes for an adaptive compression system. The main challenges include: instability and mismatched statistics during closed loop design; and the severe non-convexity of the cost function trapping the system in poor local minima. The statistical mismatch is circumvented through a largely open loop (hence stable) design that is devised to asymptotically optimize the prediction modes for closed loop operation. The non-convexity of the cost function is handled by the deterministic annealing paradigm, a powerful non-convex optimization framework devised to avoid poor local minima. Experimental results provide substantial gains validating the efficacy of the proposed design technique.