SASIC: Stereo Image Compression with Latent Shifts and Stereo Attention
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.
Cite
Text
Wödlinger et al. "SASIC: Stereo Image Compression with Latent Shifts and Stereo Attention." Conference on Computer Vision and Pattern Recognition, 2022. doi:10.1109/CVPR52688.2022.00074Markdown
[Wödlinger et al. "SASIC: Stereo Image Compression with Latent Shifts and Stereo Attention." Conference on Computer Vision and Pattern Recognition, 2022.](https://mlanthology.org/cvpr/2022/wodlinger2022cvpr-sasic/) doi:10.1109/CVPR52688.2022.00074BibTeX
@inproceedings{wodlinger2022cvpr-sasic,
title = {{SASIC: Stereo Image Compression with Latent Shifts and Stereo Attention}},
author = {Wödlinger, Matthias and Kotera, Jan and Xu, Jan and Sablatnig, Robert},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2022},
pages = {661-670},
doi = {10.1109/CVPR52688.2022.00074},
url = {https://mlanthology.org/cvpr/2022/wodlinger2022cvpr-sasic/}
}