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With the ever-increasing amount of 3D data being captured and processed, multi-view image compression is essential to various applications, including virtual reality and 3D modeling. Despite the considerable success of learning-based compression models on single images, limited progress has been made in multi-view image compression. In this paper, we propose an efficient approach to multi-view image compression by leveraging the redundant information across different viewpoints without explicitly using warping operations or camera parameters. Our method builds upon the recent advancements in Multi-Reference Entropy Models (MEM), which were initially proposed to capture correlations within an image. We extend the MEM models to employ cross-view correlations in addition to within-image correlations. Specifically, we generate latent representations for each view independently and integrate a cross-view context module within the entropy model. The estimation of entropy parameters for each view follows an autoregressive technique, leveraging correlations with the previous views. We show that adding this view context module further enhances the compression performance when jointly trained with the autoencoder. Experimental results demonstrate superior performance compared to both traditional and learning-based multi-view compression methods.