Predicting and optimizing image compression

Oleksandr Murashko, John Donald Thomson, Hugh Leather

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

3 Citations (Scopus)

Abstract

Image compression is a core task for mobile devices, social media and cloud storage backend services. Key evaluation criteria for compression are: the quality of the output, the compression ratio achieved and the computational time (and energy) expended. Predicting the effectiveness of standard compression implementations like libjpeg and WebP on a novel image is challenging, and often leads to non-optimal compression.

This paper presents a machine learning-based technique to accurately model the outcome of image compression for arbitrary new images in terms of quality and compression ratio, without requiring significant additional computational time and energy. Using this model, we can actively adapt the aggressiveness of compression on a per image basis to accurately fit user requirements, leading to a more optimal compression.
Original languageEnglish
Title of host publication Proceedings of the 24th ACM International Conference on Multimedia
PublisherACM
Pages665-669
ISBN (Electronic)9781450336031
ISBN (Print)9781450336031
DOIs
Publication statusPublished - 1 Oct 2016
Event24th ACM International Conference on Multimedia (MM) - Amsterdam, Netherlands
Duration: 15 Oct 201619 Oct 2016
http://www.acmmm.org/2016/

Conference

Conference24th ACM International Conference on Multimedia (MM)
Country/TerritoryNetherlands
CityAmsterdam
Period15/10/1619/10/16
Internet address

Keywords

  • Image Processing
  • Compression
  • Machine Learning
  • JPEG
  • WebP

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