@inproceedings{d5ac07973eed424c858117ba6764a148,
title = "Training compression artifacts reduction network with domain adaptation",
abstract = "Compression artifact removal is imperative for more visually pleasing contents after image and video compression. Recent works on compression artifact reduction network (CARN) assume that the same or similar quality of images would be employed for both training and testing, and, accordingly, a model needs a quality factor as a prior to accomplish the task successfully. However, the possible discrepancy will degrade performance substantially in a target if the model confronts a different level of distortion from the training phase. To solve the problem, we propose a novel training scheme of CARN to take an advantage of domain adaptation (DA). Specifically, we assign an image encoded with a different quality factor as a different domain and train a CARN using DA to perform robustly in another domain of a different level of distortion. Experimental results demonstrate that the proposed method achieves superior performance on DIV2K, BSD68, and Set12. ",
keywords = "Compression Artifact Reduction, Deep Learning, Domain Adaptation, Video/Image Coding",
author = "Ham, {Yu Jin} and Chaehwa Yoo and Kang, {Je Won}",
note = "Funding Information: This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP–2021–2020–0–01460) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation) Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; null ; Conference date: 01-08-2021 Through 05-08-2021",
year = "2021",
doi = "10.1117/12.2597094",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Tescher, {Andrew G.} and Touradj Ebrahimi",
booktitle = "Applications of Digital Image Processing XLIV",
}