S2LIC: Learned image compression with the SwinV2 block, Adaptive Channel-wise and Global-inter attention Context

Recently, deep learning technology has been successfully applied in the field of image compression, leading to superior rate-distortion performance. It is crucial to design an effective and efficient entropy model to estimate the probability distribution of the latent representation. However, the majority of entropy models primarily focus on one-dimensional correlation processing between channel and spatial information. In this paper, we propose an Adaptive Channel-wise and Global-inter attention Context (ACGC) entropy model, which can efficiently achieve dual feature aggregation in both inter-slice and intra-slice contexts. Specifically, we divide the latent representation into different slices and then apply the ACGC model in a parallel checkerboard context to achieve faster decoding speed and higher rate-distortion performance. We utilize deformable attention in adaptive global-inter slices context to dynamically refine the attention weights based on the actual spatial correlation and context. Furthermore, in the main transformation structure, we introduce the Residual SwinV2 Transformer model to capture global feature information and utilize a dense block network as the feature enhancement module to improve the nonlinear representation of the image within the transformation structure. Experimental results demonstrate that our method achieves faster encoding and decoding speeds, with only 0.31 and 0.38 s, respectively. Additionally, our approach outperforms VTM-17.1 and some recent learned image compression methods in terms of PSNR metrics, reducing BD-Rate by 8.87%, 10.15% and 7.48% on three different datasets (i.e., Kodak, Tecnick and CLIC Pro). Our code will be available at https://github.com/wyq2021/S2LIC.git.