Perception-Optimized Encoding for Visually Lossy Image Compression

Yuzhang Lin, Feng Liu, Miguel Hernandez-Cabronero, Eze Ahanonu, Michael W Marcellin, Ali Bilgin, Amit Ashok

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

Abstract

We propose a compression encoding method to perceptually optimize the image quality based on a novel quality metric, which emulates how the human visual system form opinion of a compressed image. Compared to the existing perceptual-optimized compression methods, which usually aim to minimize the detectability of compression artifacts and are sub-optimal in visually lossless regime, the proposed encoder aims to operate in the visually lossy regime. We implement the proposed encoder within the JPEG 2000 standard, and demonstrate its advantage over both detectability-based and conventional MSE encoders.

Original languageEnglish (US)
Title of host publicationProceedings - DCC 2019
Subtitle of host publication2019 Data Compression Conference
EditorsAli Bilgin, James A. Storer, Joan Serra-Sagrista, Michael W. Marcellin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages1
ISBN (Electronic)9781728106571
DOIs
Publication statusPublished - May 10 2019
Event2019 Data Compression Conference, DCC 2019 - Snowbird, United States
Duration: Mar 26 2019Mar 29 2019

Publication series

NameData Compression Conference Proceedings
Volume2019-March
ISSN (Print)1068-0314

Conference

Conference2019 Data Compression Conference, DCC 2019
CountryUnited States
CitySnowbird
Period3/26/193/29/19

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Keywords

  • Image compression
  • Image Quality
  • JPEG 2000
  • Perceptual Compression

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Lin, Y., Liu, F., Hernandez-Cabronero, M., Ahanonu, E., Marcellin, M. W., Bilgin, A., & Ashok, A. (2019). Perception-Optimized Encoding for Visually Lossy Image Compression. In A. Bilgin, J. A. Storer, J. Serra-Sagrista, & M. W. Marcellin (Eds.), Proceedings - DCC 2019: 2019 Data Compression Conference [8712740] (Data Compression Conference Proceedings; Vol. 2019-March). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DCC.2019.00104