In this paper, we present ECSIC, a novel learned method for stereo image compression. Our proposed method compresses the left and right images in a joint manner by exploiting the mutual information between the images of the stereo image pair using a novel stereo cross attention (SCA) module and two stereo context modules. The SCA module performs cross-attention restricted to the corresponding epipolar lines of the two images and processes them in parallel. The stereo context modules improve the entropy estimation of the second encoded image by using the first image as a context. We conduct an extensive ablation study demonstrating the effectiveness of the proposed modules and a comprehensive quantitative and qualitative comparison with existing methods. ECSIC achieves state-of-the-art performance among stereo image compression models on the two popular stereo image datasets Cityscapes and InStereo2k while allowing for fast encoding and decoding, making it highly practical for real-time applications.

本文提出了一种新颖的学习方法ECSIC，用于立体图像压缩。通过利用立体图像对之间的相互信息，我们的方法以联合的方式压缩左右图像，采用新颖的立体交互注意力模块（SCA）和两个立体上下文模块。SCA模块在两个图像的对应极线上进行跨注意力限制处理，并且并行处理。立体上下文模块通过使用第一个图像作为上下文来改进第二个编码图像的熵估计。我们进行了大量的消融研究，验证了所提出模块的有效性，并与现有方法进行了全面的定量和定性比较。ECSIC在两个流行的立体图像数据集Cityscapes和InStereo2k上实现了最先进的性能，在保证快速编解码的同时，非常适用于实时应用。

ECSIC: 立体图像压缩的极线交叉关注