Introducing the Perception-Distortion Tradeoff into the Rate-Distortion Theory of General Information Sources

By Ryutaroh Matsumoto. 2018

44 citations recorded by the source corpus.

Blau and Michaeli recently introduced a novel concept for inverse problems of signal processing, that is, the perception-distortion tradeoff. We introduce their tradeoff into the rate distortion theory of lossy source coding in information theory, and clarify the tradeoff among information rate, distortion and perception for general information sources.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

How researchers see this paper

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

In Artificial Intelligence

1. Tomer Michaeli
2. Yochai Blau

In Computer Vision and Pattern Recognition

1. Tomer Michaeli
2. Yochai Blau
3. Chen Change Loy
4. Radu Timofte
5. Chao Dong
6. Luc Van Gool
7. Lihi Zelnik-Manor
8. Roey Mechrez

In Electrical and Electronic Engineering

1. Tomer Michaeli
2. Yochai Blau

Citations in the corpus

• The Perception-Distortion Tradeoff (2017, background): “Theoretically, its effect can be studied through the rate-distortion-perception function [55], [56], [57], which is an extension of the ratedistortion function (15) and the perception-distortion function (10), characterizing the triple tradeoff between rate, distortion, and perceptual quality.”
• Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff (2019, background): “A preliminary attempt to incorporate perceptual quality into rate-distortion theory was briefly reported in (Matsumoto, 2018a;b).”