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.
@article{arxiv.1908.03631,
title = {DSIC: Deep Stereo Image Compression},
author = {Jerry Liu and Shenlong Wang and Raquel Urtasun},
journal= {arXiv preprint arXiv:1908.03631},
year = {2019}
}
Comments
Accepted at International Conference on Computer Vision 2019