Ye, N;
Perez-Ortiz, M;
Mantiuk, RK;
(2020)
Visibility Metric for Visually Lossless Image Compression.
In:
Proceedings of the 2019 Picture Coding Symposium (PCS).
IEEE
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Abstract
Encoding images in a visually lossless manner helps to achieve the best trade-off between image compression performance and quality and so that compression artifacts are invisible to the majority of users. Visually lossless encoding can often be achieved by manually adjusting compression quality parameters of existing lossy compression methods, such as JPEG or WebP. But the required compression quality parameter can also be determined automatically using visibility metrics. However, creating an accurate visibility metric is challenging because of the complexity of the human visual system and the effort needed to collect the required data. In this paper, we investigate how to train an accurate visibility metric for visually lossless compression from a relatively small dataset. Our experiments show that prediction error can be reduced by 40% compared with the state-of-theart, and that our proposed method can save between 25%-75% of storage space compared with the default quality parameter used in commercial software. We demonstrate how the visibility metric can be used for visually lossless image compression and for benchmarking image compression encoders
| Type: | Proceedings paper |
|---|---|
| Title: | Visibility Metric for Visually Lossless Image Compression |
| Event: | 2019 Picture Coding Symposium (PCS) |
| Location: | Ningbo, China |
| Dates: | 12th-15th November 2019 |
| ISBN-13: | 978-1-7281-4704-8 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/PCS48520.2019.8954560 |
| Publisher version: | https://doi.org/10.1109/PCS48520.2019.8954560 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Visually lossless image compression, visibility metric, deep learning, transfer learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10093841 |
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