A new multiple description coding paradigm is proposed by combining the time-domain lapped transform, block level source splitting, linear prediction, and prediction residual encoding. The method provides effective redundancy control and fully utilizes the source correlation. The joint optimization of all system components and the asymptotic performance analysis are presented. Image coding results demonstrate the superior performance of the proposed method, especially at low redundancies.

A new multiple description image coding paradigm is presented in this paper by combining the lapped transform, block level source splitting, inter-description prediction, and coding of the prediction residual. Jointly optimal designs of all system components are discussed. Compared with the best multiple description image coding algorithm in the literature, the new method can achieve significant improvement when one description is lost, given the same bit rate and the same central distortion.

This paper presents the design of two-dimensional Wiener filters for error resilient time domain lapped transform. Two solutions are discussed, and a multi-pass approach is also proposed to make the algorithm adaptive to input statistics. Design examples and image coding experiments show that the adaptive 2-D Wiener filters provide significant improvement over the existing 1-D Wiener filtering method.