  Recently, the deep learning technology has been successfully applied in the
field of image compression, leading to superior rate-distortion performance.
However, a challenge of many learning-based approaches is that they often
achieve better performance via sacrificing complexity, which making practical
deployment difficult. To alleviate this issue, in this paper, we propose an
effective and efficient learned image compression framework based on an
enhanced residual Swinv2 transformer. To enhance the nonlinear representation
of images in our framework, we use a feature enhancement module that consists
of three consecutive convolutional layers. In the subsequent coding and hyper
coding steps, we utilize a SwinV2 transformer-based attention mechanism to
process the input image. The SwinV2 model can help to reduce model complexity
while maintaining high performance. Experimental results show that the proposed
method achieves comparable performance compared to some recent learned image
compression methods on Kodak and Tecnick datasets, and outperforms some
traditional codecs including VVC. In particular, our method achieves comparable
results while reducing model complexity by 56% compared to these recent
methods.
