Coding Theorems for a Discrete Source With a Fidelity CriterionInstitute of Radio Engineers, International Convention Record, vol. 7, 1959.

By N. Sloane, A. Wyner. 1993

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Citations in the corpus

A Survey of Quantization Methods for Efficient Neural Network Inference (2021, neutral): “1959 [206], Shannon introduced distortion-rate functions (which provide a lower bound on the signal distortion”
The Necessity and Sufficiency of Anytime Capacity for Stabilization of a Linear System Over a Noisy Communication Link—Part I: Scalar Systems (2006, background): “This provides a justification forthe layered architecture that lets engineers isolate the problem of reliable communication from that of using the communicated information.”
An information-theoretic approach to privacy (2010, unknown): “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].”
Universal distortion function for steganography in an arbitrary domain (2014, background): “Instead, message embedding is formulated as source coding with a fidelity constraint [2] - the sender hides her message while minimizing an embedding distortion.”
Video Compression - From Concepts to the H.264/AVC Standard (2005, methodology): “Digital Object Identifier 10.1109/JPROC.2004.839617 source data using the lowest bit rate possible while maintaining a specified reproduction fidelity [7].”
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications (2022, unknown): “This was observed by Shannon in his 1959 paper [11].”
On the Interdependence of Routing and Data Compression in Multi-Hop Sensor Networks (2002, background): “Rate Distortion is based on the seminal contribution of Claude Shannon who tried to provided a theoretical framework for the representation of a continuous source through discrete symbols [13].”
Information Bottleneck for Gaussian Variables (2003, background): “As is well acknowledged now source coding with distortion and channel coding with cost are dual problems (see for example Shannon, 1959; Pradhan et al., 2003).”
Neural Network Approaches to Image Compression (1995, background): “e) Entropy and Perceptual Entropy:: Shannon [77] introduced the concept of entropy as a measure of the average information content of a source.”
Introduction to Data Compression (2005, background): “In his 1959 paper on source coding [100], Shannon showed that the minimum rate for a given distortion is given by R D = min P yj xi ∈ I X Y (8.”
High-Fidelity GAN Inversion for Image Attribute Editing (2021, methodology): “This implies a contradiction with [29, 32], which shows the low-rate latent codes are insufficient for faithful reconstruction and some information is inevitably lost.”
Multiple description coding: compression meets the network (2001, background): “This theorem equates the rate distortion function to the result of a constrained minimization problem for a scalar conditional density [6], [90].”
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff (2019, methodology): “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…”
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 so-called Gel'fand–Pinsker problem [19].”
Autoregressive Image Generation using Residual Quantization (2022, background): “Note that the best achievable reconstruction error depends on the number of bits in terms of the rate-distortion theory [38].”