We introduce Xampling, a design methodology for analog compressed sensing in which we sample analog bandlimited signals at rates far lower than Nyquist, without loss of informatio...
Motivated by the Markov chain Monte Carlo (MCMC) relaxation method of Jalali and Weissman, we propose a lossy compression algorithm for continuous amplitude sources that relies on...
The multiple description coding of an independent and identically distributed Gaussian source over bit error channels is considered. A novel practical multiple description coding ...
Inspired by syndrome source coding using linear error-correcting codes, we explore a new form of measurement matrix for compressed sensing. The proposed matrix is constructed in t...
In this work we describe a sequence compression method based on combining a Bayesian nonparametric sequence model with entropy encoding. The model, a hierarchy of Pitman-Yor proce...
It is known that modeling an information source via a symbolic dynamical system evolving over the unit interval, leads to a natural lossless compression scheme attaining the entro...
—We consider Slepian-Wolf coding of multiple sources and extend the packing bound and the notion of perfect code from conventional channel coding to SW coding with more than two ...
Block-based random image sampling is coupled with a projectiondriven compressed-sensing recovery that encourages sparsity in the domain of directional transforms simultaneously wi...
We introduce an alternative Lempel-Ziv text parsing, LZ-End, that converges to the entropy and in practice gets very close to LZ77. LZ-End forces sources to finish at the end of ...
In lifting-based directional wavelet transforms, different subsampling patterns may show significant difference for directional signals in image coding. This paper investigates t...