Compressed Screen Content Image Enhancement with B-Spline Based Distortion Estimation

Junru Li, Yue Li, Chaoyi Lin, Kai Zhang, Li Zhang

Abstract: Screen content has emerged as a prominent medium in our increasingly connected world. However, compressed screen content images often suffer from unpleasant artifacts, significantly obstructing the comprehension of text and graphic regions. In this paper, we introduce a quality enhancement framework specifically designed for compressed screen content images. We first propose a dataset for enhancing the quality of screen content images affected by various levels of compression distortion, using state-of-the-art Versatile Video Coding with screen content coding techniques enabled. Given the unique characteristics of screen content images, our enhancement framework incorporates B-spline representation to mitigate the quality degradation caused by compression. Additionally, we focus on recovering distorted text by detecting text regions within the degraded image and generating a pristine textual map to guide the recovery process. Experimental results demonstrate that our proposed method effectively enhances the quality of reconstructed screen content images across different compression distortion levels, leading to the quantitative and qualitative improvement.

External IDs:dblp:conf/dcc/LiLLZZ25