The use of compression algorithms in machine learning tasks such as clustering and classification has appeared in a variety of fields, sometimes with the promise of reducing probl...
This paper considers the design of efficient quantizers for a distributed source coding system. The information is encoded at independent terminals and transmitted across separate...
We are interested in how to best communicate a real valued source to a number of destinations (sinks) over a network with capacity constraints in a collective fidelity metric over...
This paper proposes a framework for joint source-channel decoding of Markov sequences that are coded by a fixed-rate multiple description quantizer (MDQ), and transmitted via a lo...
We describe a low-complexity scheme for lossless compression of short text messages. The method uses arithmetic coding and a specific statistical context model for prediction of s...
In scientific computing environments, large amounts of floating-point data often need to be transferred between computers as well as to and from storage devices. Compression can r...
We present a simple and efficient entropy coder that combines run-length and Golomb-Rice encoders. The encoder automatically switches between the two modes according to simple rul...
We propose a new sequential, adaptive, quadratic-time algorithm for variable-rate lossy compression of memoryless sources at a fixed distortion. The algorithm uses approximate pat...
Abstract. The Compressed Delta Encoding paradigm is introduced, i.e., delta encoding directly in two given compressed files without decompressing. Here we explore the case where th...
In this paper we present the B-coder, an efficient binary arithmetic coder that performs extremely well on a wide range of data. The B-coder should be classed as an `approximate...