  With the rapid advancement of stereo vision technologies, stereo image
compression has emerged as a crucial field that continues to draw significant
attention. Previous approaches have primarily employed a unidirectional
paradigm, where the compression of one view is dependent on the other,
resulting in imbalanced compression. To address this issue, we introduce a
symmetric bidirectional stereo image compression architecture, named BiSIC.
Specifically, we propose a 3D convolution based codec backbone to capture local
features and incorporate bidirectional attention blocks to exploit global
features. Moreover, we design a novel cross-dimensional entropy model that
integrates various conditioning factors, including the spatial context, channel
context, and stereo dependency, to effectively estimate the distribution of
latent representations for entropy coding. Extensive experiments demonstrate
that our proposed BiSIC outperforms conventional image/video compression
standards, as well as state-of-the-art learning-based methods, in terms of both
PSNR and MS-SSIM.
