  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.
