Predicting and Optimizing Image Compression

Oleksandr Murashko, John Thomson, Hugh Leather

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

Abstract / Description of output

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 publicationProceedings of the 2016 ACM on Multimedia Conference
Place of PublicationNew York, NY, USA
PublisherACM
Pages665-669
Number of pages5
ISBN (Electronic)978-1-4503-3603-1
DOIs
Publication statusPublished - 1 Oct 2016
EventACM MULTIMEDIA CONFERENCE 2016 - Amsterdam, Netherlands
Duration: 15 Oct 201619 Oct 2016
http://www.acmmm.org/2016/

Conference

ConferenceACM MULTIMEDIA CONFERENCE 2016
Country/TerritoryNetherlands
CityAmsterdam
Period15/10/1619/10/16
Internet address

Fingerprint

Dive into the research topics of 'Predicting and Optimizing Image Compression'. Together they form a unique fingerprint.

Cite this