تقرير
OMR-NET: a two-stage octave multi-scale residual network for screen content image compression
| العنوان: | OMR-NET: a two-stage octave multi-scale residual network for screen content image compression |
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| المؤلفون: | Jiang, Shiqi, Ren, Ting, Fu, Congrui, Li, Shuai, Yuan, Hui |
| المصدر: | IEEE Signal Processing Letters, 2024 |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | Screen content (SC) differs from natural scene (NS) with unique characteristics such as noise-free, repetitive patterns, and high contrast. Aiming at addressing the inadequacies of current learned image compression (LIC) methods for SC, we propose an improved two-stage octave convolutional residual blocks (IToRB) for high and low-frequency feature extraction and a cascaded two-stage multi-scale residual blocks (CTMSRB) for improved multi-scale learning and nonlinearity in SC. Additionally, we employ a window-based attention module (WAM) to capture pixel correlations, especially for high contrast regions in the image. We also construct a diverse SC image compression dataset (SDU-SCICD2K) for training, including text, charts, graphics, animation, movie, game and mixture of SC images and NS images. Experimental results show our method, more suited for SC than NS data, outperforms existing LIC methods in rate-distortion performance on SC images. The code is publicly available at https://github.com/SunshineSki/OMR Net.git. Comment: 7 figures, 2 tables |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1109/LSP.2024.3411917 |
| URL الوصول: | http://arxiv.org/abs/2407.08545 |
| رقم الأكسشن: | edsarx.2407.08545 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1109/LSP.2024.3411917 |
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