Predicting and optimizing image compression
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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.
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 , 15-19 October . DOI: 10.1145/2964284.2967305conference
Proceedings of the 24th ACM International Conference on Multimedia
© 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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