SASIC: Stereo Image Compression With Latent Shifts and Stereo Attention

Matthias Wödlinger, Jan Kotera, Jan Xu, Robert Sablatnig; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 661-670

Abstract


We propose a learned method for stereo image compression that leverages the similarity of the left and right images in a stereo pair due to overlapping fields of view. The left image is compressed by a learned compression method based on an autoencoder with a hyperprior entropy model. The right image uses this information from the previously encoded left image in both the encoding and decoding stages. In particular, for the right image, we encode only the residual of its latent representation to the optimally shifted latent of the left image. On top of that, we also employ a stereo attention module to connect left and right images during decoding. The performance of the proposed method is evaluated on two benchmark stereo image datasets (Cityscapes and InStereo2K) and outperforms previous stereo image compression methods while being significantly smaller in model size.

Related Material


[pdf] [supp]
[bibtex]
@InProceedings{Wodlinger_2022_CVPR, author = {W\"odlinger, Matthias and Kotera, Jan and Xu, Jan and Sablatnig, Robert}, title = {SASIC: Stereo Image Compression With Latent Shifts and Stereo Attention}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {661-670} }