ABCD: Arbitrary Bitwise Coefficient for De-Quantization
Woo Kyoung Han, Byeonghun Lee, Sang Hyun Park, Kyong Hwan Jin
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
BibTeX
@inproceedings{cvpr2023_abcdarbitrarybit,
title = {ABCD: Arbitrary Bitwise Coefficient for De-Quantization},
author = {Woo Kyoung Han and Byeonghun Lee and Sang Hyun Park and Kyong Hwan Jin},
booktitle = {CVPR 2023},
year = {2023}
}