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

doi:10.1109/LSP.2025.3596872

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

2025-01-01

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DOI：10.1109/LSP.2025.3596872

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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.

Keywords:

Screen content

image compression

frequency-aware

entropy model

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

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