Dataset and metrics for predicting local visible differences
- Submitting institution
-
University of Cambridge
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9076
- Type
- D - Journal article
- DOI
-
10.1145/3196493
- Title of journal
- ACM Transactions on Graphics
- Article number
- ARTN 172
- First page
- 1
- Volume
- 37
- Issue
- 5
- ISSN
- 0730-0301
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
8
- Research group(s)
-
-
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work is an important stepping stone to modelling visible differences (detection and discrimination) using deep convolutional networks. It opened many applications, such as visually lossless compression and adaptive texture resolution selection. This work was crucial in publishing a series of three follow-up, application-oriented papers at CVPR, Computer Graphics Forum and the Picture Coding Symposium in 2019.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -