DSCIC: Deep Screen Content Image Compression
Feifeng Wang, Liquan Shen, Qi Teng, Zhaoyi Tian · IEEE Transactions on Circuits and Systems for Video Technology · 2024
Existing deep learning-based image compression methods overlook the unique properties of screen content images (SCIs), like limited color values and abundant repetitive patterns, leading to limited compression performance on SCIs. Therefore, a specialized framework, deep screen content image compression (DSCIC) is proposed, which contains a color context generator (CCG) and a region-based block aggregation (RBA) module. The CCG is designed to generate compression-friendly color contexts based on main color components, embedded in the encoder-decoder to remove color representation redundancy. Furthermore, to effectively reduce repetitive block redundancy in SCIs, the RBA captures repetitive patterns and enables adaptive aggregation in the latent space. It leverages region-based block matching and block content-aware aggregation to utilize repetitive features for further improving compression performance. Extensive experimental results demonstrate that the proposed DSCIC outperforms the most advanced traditional codec VVC-SCC, and is significantly superior to other learning-based image compression methods. Using VVC as the anchor, DSCIC exhibits further BD-Rate savings of 12.185% and 4.889% compared to VVC-SCC and the SOTA deep learning-based method, respectively.