Predicting and Optimizing Image Compression

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.

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Index Terms

• Computing methodologies

  • Computer graphics

    • Image compression

  • Machine learning

    • Learning paradigms

      • Supervised learning

        • Supervised learning by regression

• Human-centered computing

  • Ubiquitous and mobile computing

    • Ubiquitous and mobile computing systems and tools