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.
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 language | English |
|---|---|
| Title of host publication | Proceedings of the 24th ACM International Conference on Multimedia |
| Publisher | ACM |
| Pages | 665-669 |
| ISBN (Electronic) | 9781450336031 |
| ISBN (Print) | 9781450336031 |
| DOIs | |
| Publication status | Published - 1 Oct 2016 |
| Event | 24th ACM International Conference on Multimedia (MM) - Amsterdam, Netherlands Duration: 15 Oct 2016 → 19 Oct 2016 http://www.acmmm.org/2016/ |
Conference
| Conference | 24th ACM International Conference on Multimedia (MM) |
|---|---|
| Country/Territory | Netherlands |
| City | Amsterdam |
| Period | 15/10/16 → 19/10/16 |
| Internet address |
Keywords
- Image Processing
- Compression
- Machine Learning
- JPEG
- WebP