OMR-NET：一种用于屏幕内容图像压缩的两阶段八度多尺度残差网络
OMR-NET: a two-stage octave multi-scale residual network for screen content image compression
AI总结：
针对现有学习型图像压缩方法在屏幕内容上的不足，提出含改进两阶段八度卷积残差块、级联多尺度残差块与窗口注意力模块的OMR-NET，构建SDU-SCICD2K数据集，其屏幕内容压缩率失真性能优于现有方法。
摘要：
屏幕内容（SC）与自然场景（NS）不同，具有无噪声、模式重复、高对比度等独特特征。针对当前学习型图像压缩（LIC）方法在SC上的不足，我们提出了改进型两阶段八度卷积残差块（IToRB）用于高低频特征提取，以及级联两阶段多尺度残差块（CTMSRB）以增强SC的多尺度学习能力和非线性表达。此外，我们采用基于窗口的注意力模块（WAM）捕捉像素相关性，尤其针对图像中的高对比度区域。我们还构建了一个多样化的SC图像压缩数据集SDU-SCICD2K用于训练，包含文本、图表、图形、动画、电影、游戏以及SC图像与NS图像的混合类内容。实验结果表明，我们的方法相比NS数据更适配SC，在SC图像的率失真性能上优于现有LIC方法。代码已公开于https://github.com/SunshineSki/OMR Net.git。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
