OMR-Net+: A Frequency-Aware Feature Refinement and Entropy Modeling Method for Efficient Screen Content Image Compression

Screen content image (SCI) compression faces challenges due to distinct characteristics such as sharp edges and repetitive structures. Existing learned image compression methods encounter two key issues: 1) insufficient frequency-aware processing, and 2) suboptimal entropy modeling for mixed-frequency components. To this end, we propose OMR-Net+, a novel SCI compression method that incorporates frequency-aware feature characteristics, including a frequency-aware refinement network (FARN) and a frequency-aware entropy model (FAEM). The proposed FARN uses an invertible neural network to preserve critical high-frequency details and a transformer-based model to reduce redundancy in low-frequency features. Additionally, the proposed FAEM provides tailored conditional probability estimation based on a parallel context model for high- and low-frequency features, respectively, to improve both coding performance and computational efficiency. Experimental results on the SCID and SIQAD datasets show that OMR-Net+ significantly outperforms the previous OMR-Net and other state-of-the-art methods in rate-distortion performance, demonstrating its potential for efficient SCI compression.