Compressed Video Quality Assessment with Fine-Grained Artifact Perception and Evaluation

Videos are generally compressed to save storage and transmission bandwidth. Popular lossy video compression inevitably leads to Perceivable Encoding Artifacts (PEAs) that affect user's visual experience. Thus, Compression Artifact Removal (CAR) methods have emerged to eliminate perceivable encoding artifacts after video coding. However, there still lacks of an efficient artifact discrimination and evaluation method to guide the optimization of CAR methods. To solve this problem, we make the first attempt to propose an Artifact Perception and Evaluation Network (APE-Net) that can accurately locate artifacts and evaluate their impacts on user experience. First, we propose an Artifact Perception Module (APM) that captures various types and long-tailed-distributed PEAs with attention learning and data re-weighting, thus greatly improving the perception capability for video compression artifacts. Second, we design an Artifact Evaluation Module (AEM) to fuse all recognized PEAs with visual saliency and random forest regression, which assists the artifact perception model to be in line with human visual characteristics in video quality assessment tasks. Experimental results demonstrate that our proposed APE-Net is superior to the state-of-the-art algorithms on compressed video quality assessment. Our codes will be made publicly available after the peer review process.

© 2026 IEEE. All rights reserved, including rights for text and data mining and training of artificial intelligence and similar technologies. Personal use is permitted, but republication/redistribution requires IEEE permission.