DIC-DDA: Learned Asymmetric Distributed Image Compression via Dual Domain Alignment

Multi-view or stereo image compression is an essential technology in 3D related applications. Due to the overlap between different views, exploring their correlations can help improve the compression rate. However, the computing complexity of joint encoding at the encoding side is a heavy burden for terminal encoders. To solve this problem, the learned Distributed Image Coding (DIC), which only uses the correlated view (namely the side image, SI) in the decoder side, has gained much attention in recent years. In this work, we explore asymmetric DIC where one view is selected as the SI and is losslessly compressed. The key problem in learned asymmetric DIC is alignment between the transmitted low-quality target image and high-quality SI. Previous methods usually adopt patch-level alignment with the offset index obtained from degraded (via re-encoded and decoded) SI and the decoded target image, which hinders the alignment accuracy. In this work, we propose a dual domain alignment strategy, which includes degraded domain and fused domain pixel-wise offset estimation. For the degraded domain alignment, we estimate the offset between the degraded SI feature and the degraded target image feature, which eliminates the difficulties in cross-domain matching. For the fused-domain alignment, we observe that the fusion result of degraded target feature and aligned side image feature implicitly contains fine-scale disparity information. Therefore, we estimate the fine-scale offset from the fusion result, which helps refine the degraded domain offsets. We further propose a selective enhancement module to repair the mismatched region in the aligned feature. Extensive experiments on three datasets demonstrate the superiority of our proposed method, outperforming the second-best method by 16% in terms of average BD-rate reduction on the KITTI Stereo dataset. Our code will be released after the acceptance of this work.