Coding Theorems for a Discrete Source With a Fidelity Criterion-Claude

By Claude E. Shannon. 2009

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Autoregressive Image Generation using Residual Quantization (2022, background): “However, previous studies have a limitation to reducing the sequence length of images in terms of the rate-distortion trade-off [42].”
Polar Codes for Channel and Source Coding (2009, methodology): “1 (Shannon’s Rate-Distortion Theorem [2]).”
An information-theoretic approach to privacy (2010, background): “We can show this by arguing that if such a code exists then we can always find an equivalent source coding problem for which the code would violate Shannon’s source coding theorem [25].”
Information Theoretic Lower Bounds on Negative Log Likelihood (2018, unknown): “…the prior to improve the log likelihood (see the Blahut-Arimoto algorithm (Blahut, 1972)): Lindsay (1983) derived his results with no apparent reference to earlier published work on rate-distortion theory, which is how information theorists study the problem of lossy compression Shannon (1959).”
Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance (2017, background): “This problem has wide applications in information theory, under the name rate distortion [13,48]; machine learning [10, 38]; and numerical methods [12].”
Lossy Compression for Lossless Prediction (2021, background): “This is accomplished in Shannon’s [17] rate-distortion (RD) theory by mapping X into a r.v.”
Minimizing Additive Distortion in Steganography Using Syndrome-Trellis Codes (2011, background): “Both embedding problems described above bear relationship to the problem of source coding with a fidelity criterion as described by Shannon [21] and the problem of source coding with side information available at the transmitter, the socalled Gel’fand-Pinsker problem [19].”
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff (2019, background): “The theoretical foundations of lossy compression are rooted in Shannon’s seminal work on rate-distortion theory (Shannon, 1959), which analyzes the fundamental tradeoff between the rate (bits per sample) used for representing data, and the distortion incurred when reconstructing the data from its…”
Trading Value and Information in MDPs (2012, background): “Information processing and control are known to be related, at least since Shannon’s 1959 Rate-Distortion theory paper [7].”
Polar codes are optimal for lossy source coding (2009, background): “It is well known that, in order to compress Y with average distortionD, the rateR at which we describe the source has to be at leastR(D) = 1 − h2(D), whereh2(·) is the binary entropy function [1].”