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Predicting and optimizing image compression

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acmmm2016_paper_camera_submitted.pdf (566.7Kb)
Date
01/10/2016
Author
Murashko, Oleksandr
Thomson, John Donald
Leather, Hugh
Keywords
Image Processing
Compression
Machine Learning
JPEG
WebP
Computer Science Applications
Computer Graphics and Computer-Aided Design
Artificial Intelligence
NDAS
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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.
Citation
Murashko , O , Thomson , J D & Leather , H 2016 , Predicting and optimizing image compression . in Proceedings of the 24th ACM International Conference on Multimedia . ACM , pp. 665-669 , 24th ACM International Conference on Multimedia (MM) , Amsterdam , Netherlands , 15/10/16 . https://doi.org/10.1145/2964284.2967305
 
conference
 
Publication
Proceedings of the 24th ACM International Conference on Multimedia
DOI
https://doi.org/10.1145/2964284.2967305
Type
Conference item
Rights
© 2016, the Author(s). This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at dl.acm.org / https://dx.doi.org/10.1145/2964284.2967305
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  • Computer Science Research
  • University of St Andrews Research
URI
http://hdl.handle.net/10023/9668

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