Diversity Image Coding Using Irregular Interpolation

Diversity “multiple description” (MD) source coding promises graceful degradation in the presence of a priori unknown number of erased packets in the channel. A simple coding scheme for the case of two packets consists of oversampling the source by a factor of two and delta-sigma quantization. This approach was applied successfully to JPEG-based image coding over a lossy packet network, where the interpolation and splitting into two descriptions are done in the discrete cosine transform (DCT) domain. Moreover, unlike the classical source-channel separation approach - which is designed for a predetermined number of erasures (say, K out of N), hence its distortion does not improve when the channel behaves better than expected - an MD coding scheme aims to achieve a better reconstruction quality when more or all the N descriptions are received at the decoder side. The extension to a larger number of descriptions, however, suffers from noise amplification whenever the received descriptions form a non-uniform sampling pattern. In this work, we examine inter- and intra-block interpolation methods, and show how noise amplification can be reduced by redesigning the interpolation filter at the encoder. Specifically, for a given total coding rate, we demonstrate that an “irregular” interpolation filter is robust to the pattern of received packets over all ( K out of N) patterns, with some degradation relative to low-pass (LP) interpolation in the case where all N packets arrived. We provide experimental results comparing LP and irregular interpolation filters, and examine the effect of noise shaping on the trade-off between the central distortion (receiving all packets) and side distortion (receiving K packets).