Polar codes are optimal for lossy source coding

By Satish Babu Korada, Rüdiger L. Urbanke. 2009 IEEE Information Theory Workshop, 2009

320 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3418666b85e8d0c58c364900c7beedada91fbbe4

How researchers see this paper

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

In Computer Networks and Communications

1. Jessica J. Fridrich
2. Tomáš Filler
3. Vojtech Holub
4. Jan Kodovský
5. Bin Li
6. Jiwu Huang

In Electrical and Electronic Engineering

1. Jessica J. Fridrich
2. Jan Judas
3. Tomáš Filler

Citations in the corpus

• A Semi-Parallel Successive-Cancellation Decoder for Polar Codes (2013, unknown): “Polar codes were also shown to be good at solving other information theoretic problems in an efficient manner [6], [7], [8].”
• Minimizing Additive Distortion in Steganography Using Syndrome-Trellis Codes (2011, background): “Moreover, most of the capacity-achieving properties of random linear codes are retained even for other information-theoretic problems and thus polar codes are known to be optimal for the PLS problem [36] (at least for the uniform profile).”