  Multi-view compression technology, especially Stereo Image Compression (SIC),
plays a crucial role in car-mounted cameras and 3D-related applications.
Interestingly, the Distributed Source Coding (DSC) theory suggests that
efficient data compression of correlated sources can be achieved through
independent encoding and joint decoding. This motivates the rapidly developed
deep-distributed SIC methods in recent years. However, these approaches neglect
the unique characteristics of stereo-imaging tasks and incur high decoding
latency. To address this limitation, we propose a Feature-based Fast Cascade
Alignment network (FFCA-Net) to fully leverage the side information on the
decoder. FFCA adopts a coarse-to-fine cascaded alignment approach. In the
initial stage, FFCA utilizes a feature domain patch-matching module based on
stereo priors. This module reduces redundancy in the search space of trivial
matching methods and further mitigates the introduction of noise. In the
subsequent stage, we utilize an hourglass-based sparse stereo refinement
network to further align inter-image features with a reduced computational
cost. Furthermore, we have devised a lightweight yet high-performance feature
fusion network, called a Fast Feature Fusion network (FFF), to decode the
aligned features. Experimental results on InStereo2K, KITTI, and Cityscapes
datasets demonstrate the significant superiority of our approach over
traditional and learning-based SIC methods. In particular, our approach
achieves significant gains in terms of 3 to 10-fold faster decoding speed than
other methods.
