1 Multimedia Information and Signal Processing, The Technical Faculty of IT and Design, Aalborg University, VBN2 Department of Electronic Systems, The Technical Faculty of IT and Design, Aalborg University, VBN3 The Faculty of Engineering and Science (TECH), Aalborg University, VBN4 Dept. Electrical Engineering-Systems, Tel Aviv University, Tel Aviv5 Dept. Electrical Engineering-Systems,Tel Aviv University, Tel Aviv
Random binning is an efficient, yet complex, coding technique for the symmetric L-description source coding problem. We propose an alternative approach, that uses the quantized samples of a bandlimited source as "descriptions". By the Nyquist condition, the source can be reconstructed if enough samples are received. We examine a coding scheme that combines sampling and noise-shaped quantization for a scenario in which only K < L descriptions or all L descriptions are received. Some of the received K-sets of descriptions correspond to uniform sampling while others to non-uniform sampling. This scheme achieves the optimum rate-distortion performance for uniform-sampling K-sets, but suffers noise amplification for nonuniform-sampling K-sets. We then show that by increasing the sampling rate and adding a random-binning stage, the optimal operation point is achieved for any K-set.