Multiple Description Coding : proposed methods and video application

Abstract

Multiple description coding (MDC) has received a lot of attention recently, and has been studied widely and extended to many demanding applications such as speech and video. MDC is a coding technique that generates correlated descriptions of the source stream for transmitting over a diversity system with several channels. The objective of this diversity system is to overcome channel impairments and provide more reliability. In the context of lossy source coding and quantization, a multiple description quantization system usually consists of multiple channels, side encoders to quantize the source samples and send over different channels, and side and central decoders to reconstruct the source.

We propose two multiple description quantization schemes in order to design the codebooks and partitions of side and central quantizers of a multiple description system with two channels. The applied framework originated in the multiple description quantization via Gram-Schmidt orthogonalization approach. The basic idea of our proposed schemes is to minimize a Lagrangian cost function by an iterative technique which jointly designs side codebooks and partitions. Our proposed methods perform very closely to the optimum MD quantizer with considerably less complexity.

We also propose a multiple description video coding technique motivated by human visual perception. We employ two simple parameters as a measure of the perceptual tolerance of discrete cosine transform (DCT) blocks against visual distortion. We duplicate the essential information such as motion vectors and some low-frequency DCT coefficients of prediction errors into each description, and split the remaining high-frequency DCT coefficients according to the calculated perceptual tolerance parameter. Our proposed technique has very low complexity and achieves superior performance compared to other similar techniques which do not consider perceptual distortion in the design problem.