@inproceedings{797c8079b4094e14a2639469bc7bdea8,
title = "DSIC: Deep Stereo Image Compression",
abstract = "In this paper we tackle the problem of stereo image compression, and leverage the fact that the two images have overlapping fields of view to further compress the representations. Our approach leverages state-of-the-art single-image compression autoencoders and enhances the compression with novel parametric skip functions to feed fully differentiable, disparity-warped features at all levels to the encoder/decoder of the second image. Moreover, we model the probabilistic dependence between the image codes using a conditional entropy model. Our experiments show an impressive 30 - 50% reduction in the second image bitrate at low bitrates compared to deep single-image compression, and a 10 - 20% reduction at higher bitrates.",
author = "Jerry Liu and Shenlong Wang and Raquel Urtasun",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 ; Conference date: 27-10-2019 Through 02-11-2019",
year = "2019",
month = oct,
doi = "10.1109/ICCV.2019.00323",
language = "English (US)",
series = "Proceedings of the IEEE International Conference on Computer Vision",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3136--3145",
booktitle = "Proceedings - 2019 International Conference on Computer Vision, ICCV 2019",
address = "United States",
}