Visual entropy: A new framework for quantifying visual information based on human perception

Sewoong Ahn, Kwanghyun Lee, Sanghoon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years, how to quantify visualizations of an object and surface displayed in 3D space is now more prominent with a rapid increase in the demand for three-dimensional (3D) content. In order to measure the content information in terms of human visual perception, it is necessary to quantify the visual information in accordance with the human visual system. In this paper, we propose a framework for expressing visual information in bits termed visual entropy based on information theory. The visual entropy of 2D content (2DVE) is composed of texture entropy on the 2D surface and depth entropy based on the monocular cue. In addition to 2DVE, the visual entropy of 3D content (3DVE) includes the depth entropy based on the binocular cue. A series of simulations are conducted to demonstrate the effectiveness of visual entropy, including a performance trade-off between 2D and 3D visualizations measured according to the bitrate.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Pages3485-3489
Number of pages5
ISBN (Electronic)9781509021758
DOIs
Publication statusPublished - 2018 Feb 20
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: 2017 Sep 172017 Sep 20

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2017-September
ISSN (Print)1522-4880

Other

Other24th IEEE International Conference on Image Processing, ICIP 2017
CountryChina
CityBeijing
Period17/9/1717/9/20

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Ahn, S., Lee, K., & Lee, S. (2018). Visual entropy: A new framework for quantifying visual information based on human perception. In 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings (pp. 3485-3489). (Proceedings - International Conference on Image Processing, ICIP; Vol. 2017-September). IEEE Computer Society. https://doi.org/10.1109/ICIP.2017.8296930