Publication

Lossy Source Coding with Gaussian or Erased Side-Information

Emre Telatar, Suhas Diggavi, Etienne Perron
2009
Conference Papers

Abstract

In this paper we find properties that are shared between two seemingly unrelated lossy source coding setups with side-information. The first setup is when the source and side-information are jointly Gaussian and the distortion measure is quadratic. The second setup is when the side-information is an erased version of the source. We begin with the observation that in both these cases the Wyner-Ziv and conditional rate-distortion functions are equal. We further find that there is a continuum of optimal strategies for the conditional rate distortion problem in both these setups. Next, we consider the case when there are two decoders with access to different side-information sources. For the case when the encoder has access to the side-information we establish bounds on the rate-distortion function and a sufficient condition for tightness. Under this condition, we find a characterization of the rate-distortion function for physically degraded side-information. This characterization holds for both the Gaussian and erasure setups.

Official source

https://infoscience.epfl.ch/entities/publication/7f911c63-3e62-48c8-9569-582fe13a7e9d

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Emre Telatar, Suhas Diggavi, Etienne Perron
2009
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/7f911c63-3e62-48c8-9569-582fe13a7e9d

About this result

Related concepts (17)

Rate–distortion theory

Rate–distortion theory is a major branch of information theory which provides the theoretical foundations for lossy data compression; it addresses the problem of determining the minimal number of bits per symbol, as measured by the rate R, that should be communicated over a channel, so that the source (input signal) can be approximately reconstructed at the receiver (output signal) without exceeding an expected distortion D. Rate–distortion theory gives an analytical expression for how much compression can be achieved using lossy compression methods.

Quantization (signal processing)

Quantization, in mathematics and digital signal processing, is the process of mapping input values from a large set (often a continuous set) to output values in a (countable) smaller set, often with a finite number of elements. Rounding and truncation are typical examples of quantization processes. Quantization is involved to some degree in nearly all digital signal processing, as the process of representing a signal in digital form ordinarily involves rounding. Quantization also forms the core of essentially all lossy compression algorithms.

Information theory

Information theory is the mathematical study of the quantification, storage, and communication of information. The field was originally established by the works of Harry Nyquist and Ralph Hartley, in the 1920s, and Claude Shannon in the 1940s. The field, in applied mathematics, is at the intersection of probability theory, statistics, computer science, statistical mechanics, information engineering, and electrical engineering. A key measure in information theory is entropy.

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