St Andrews Research Repository

St Andrews University Home
View Item 
  •   St Andrews Research Repository
  • Computer Science (School of)
  • Computer Science
  • Computer Science Theses
  • View Item
  •   St Andrews Research Repository
  • Computer Science (School of)
  • Computer Science
  • Computer Science Theses
  • View Item
  •   St Andrews Research Repository
  • Computer Science (School of)
  • Computer Science
  • Computer Science Theses
  • View Item
  • Login
JavaScript is disabled for your browser. Some features of this site may not work without it.

Using machine learning to select and optimise multiple objectives in media compression

Thumbnail
View/Open
OleksandrMurashkoPhDThesis.pdf (6.263Mb)
Date
2018
Author
Murashko, Oleksandr
Supervisor
Thomson, John
Metadata
Show full item record
Altmetrics Handle Statistics
Abstract
The growing complexity of emerging image and video compression standards means additional demands on computational time and energy resources in a variety of environments. Additionally, the steady increase in sensor resolution, display resolution, and the demand for increasingly high-quality media in consumer and professional applications also mean that there is an increasing quantity of media being compressed. This work focuses on a methodology for improving and understanding the quality of media compression algorithms using an empirical approach. Consequently, the outcomes of this research can be deployed on existing standard compression algorithms, but are also likely to be applicable to future standards without substantial redevelopment, increasing productivity and decreasing time-to-market. Using machine learning techniques, this thesis proposes a means of using past information about how images and videos are compressed in terms of content, and leveraging this information to guide and improve industry standard media compressors in order to achieve the desired outcome in a time and energy e cient way. The methodology is implemented and evaluated on JPEG, WebP and x265 codecs, allowing the system to automatically target multiple performance characteristics like le size, image quality, compression time and e ciency, based on user preferences. Compared to previous work, this system is able to achieve a prediction error three times smaller for quality and size for JPEG, and a speed up of compression of four times for WebP, targeting the same objectives. For x265 video compression, the system allows multiple objectives to be considered simultaneously, allowing speedier encoding for similar levels of quality.
Type
Thesis, PhD Doctor of Philosophy
Collections
  • Computer Science Theses
URI
http://hdl.handle.net/10023/15657

Items in the St Andrews Research Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

Advanced Search

Browse

All of RepositoryCommunities & CollectionsBy Issue DateNamesTitlesSubjectsClassificationTypeFunderThis CollectionBy Issue DateNamesTitlesSubjectsClassificationTypeFunder

My Account

Login

Open Access

To find out how you can benefit from open access to research, see our library web pages and Open Access blog. For open access help contact: openaccess@st-andrews.ac.uk.

Accessibility

Read our Accessibility statement.

How to submit research papers

The full text of research papers can be submitted to the repository via Pure, the University's research information system. For help see our guide: How to deposit in Pure.

Electronic thesis deposit

Help with deposit.

Repository help

For repository help contact: Digital-Repository@st-andrews.ac.uk.

Give Feedback

Cookie policy

This site may use cookies. Please see Terms and Conditions.

Usage statistics

COUNTER-compliant statistics on downloads from the repository are available from the IRUS-UK Service. Contact us for information.

© University of St Andrews Library

University of St Andrews is a charity registered in Scotland, No SC013532.

  • Facebook
  • Twitter