Parameter Selection for Wyner–Ziv Coding of Laplacian Sources with Additive Laplacian or Gaussian Innovation
A large number of practical coding scenarios deal with sources such as transform coefficients that can be well modeled as Laplacians. For regular coding of such sources, samples are often quantized by a family of uniform quantizers possibly with a deadzone, and then entropy coded. For the Wyner-Ziv coding problem when correlated side-information is available at the decoder, the side-information can be modeled as obtained by independent additive Laplacian or Gaussian innovation on the source. This paper deals with optimal choice of parameters for practical Wyner-Ziv coding in such scenarios, using the same quantizer family as in the regular codec to cover a range of rate-distortion tradeoffs, given the variances of the source and additive noise. We propose and analyze a general encoding model based on multilevel coset codes that combines source coding and channel coding and show that at practical block lengths and code complexities, not pure channel coding but a hybrid combination of source coding and channel coding with right parameters provide optimal rate-distortion performance. We also provide a framework for on-the-fly parameter choice based on nonparametric representation of a set of seed functions, for use in scenarios where variances are estimated during encoding. A good insight in the optimal parameter selection mechanism is essential for building practical distributed codecs.