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.

屏幕内容与自然场景不同，有噪音少、重复模式和高对比度等独特特征。本文提出了改进的两阶段八度卷积残差块(IToRB)用于高低频特征提取，以及级联的两阶段多尺度残差块(CTMSRB)用于改进屏幕内容的多尺度学习和非线性优化。此外，文章还引入了基于窗口的注意模块(WAM)，用于捕捉图像中高对比度区域的像素关联。我们还构建了一个多样的屏幕内容图像压缩数据集(SDU-SCICD2K)用于训练，包括文本、图表、图形、动画、电影、游戏和屏幕内容与自然场景混合的图像。实验结果表明，我们的方法在屏幕内容图像的速率-失真性能上优于现有的图像压缩方法。代码公开可在此URL获取 Net.git。

OMR-NET：基于两阶段的八度多尺度残差网络进行屏幕内容图像压缩