Abstract
Modern displays and contents support more than 8bits image and video. However, bit-starving situations such as compression codecs make low bit-depth (LBD) images (<8bits), occurring banding and blurry artifacts. Previous bit depth expansion (BDE) methods still produce unsatisfactory high bit-depth (HBD) images. To this end, we propose an implicit neural function with a bit query to recover de-quantized images from arbitrarily quantized inputs. We develop a phasor estimator to exploit the information of the nearest pixels. Our method shows superior performance against prior BDE methods on natural and animation images. We also demonstrate our model on YouTube UGC datasets for de-banding. Our source code is available at https://github.com/WooKyoungHan/ABCD
Original language | English |
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Title of host publication | Proceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 |
Publisher | IEEE Computer Society |
Pages | 5876-5885 |
Number of pages | 10 |
ISBN (Electronic) | 9798350301298 |
DOIs | |
State | Published - 2023 |
Event | 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 - Vancouver, Canada Duration: 18 Jun 2023 → 22 Jun 2023 |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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Volume | 2023-June |
ISSN (Print) | 1063-6919 |
Conference
Conference | 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 |
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Country/Territory | Canada |
City | Vancouver |
Period | 18/06/23 → 22/06/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- Low-level vision