Artifact Filtering versus Artifact-Compensated Training in JPEG to JPEG AI Recompression
Igor Podtsepko, Yuriy Kim, Karam Mahfod, Evgeny Belyaev, Kristian Kirs, Jianping Lin, Ilya Papiev
原文摘要
In this paper, we consider the problem of recompression of images from JPEG to JPEG AI format. We show that direct recompression from JPEG to JPEG~AI trained on uncompressed images is accompanied by the preservation of the JPEG image artifacts in the resulting JPEG~AI image. In order to avoid it, we evaluate an approach where an artifacts removal neural network is first applied to the input JPEG image as an external pre-filter. However, in this case, the processing time is increased by 29 times. Therefore, we propose an artifact-compensated training for JPEG AI and show that it is able to combine pre-filtering against target distortions with efficient compression without increase in the processing time.
eess.IV · cs.MM