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.

    Authors

    Jerry Liu, Shenlong Wang, Raquel Urtasun

    Conference

    ICCV 2019

    Full Paper

    ‘DSIC: Deep Stereo Image Compression’ (PDF)

    Uber ATG

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    Jerry Liu
    Jerry Liu is an AI Resident at Uber ATG Toronto under Prof. Raquel Urtasun. Before joining ATG, he completed a B.S.E in Computer Science at Princeton and worked as a Machine Learning Engineer at Quora. His current research interests involve image/video compression, generative models, and attention mechanisms.
    Shenlong Wang
    Shenlong Wang is research scientist at Uber ATG Toronto working on the development of self-driving cars. He is also a PhD student at University of Toronto. His advisor is Prof. Raquel Urtasun. He has a broad interest in computer vision, machine learning and robotics. He is particularly interested in 3D vision and deep structured models.
    Raquel Urtasun
    Raquel Urtasun is the Chief Scientist for Uber ATG and the Head of Uber ATG Toronto. She is also a Professor at the University of Toronto, a Canada Research Chair in Machine Learning and Computer Vision and a co-founder of the Vector Institute for AI. She is a recipient of an NSERC EWR Steacie Award, an NVIDIA Pioneers of AI Award, a Ministry of Education and Innovation Early Researcher Award, three Google Faculty Research Awards, an Amazon Faculty Research Award, a Connaught New Researcher Award, a Fallona Family Research Award and two Best Paper Runner up Prize awarded CVPR in 2013 and 2017. She was also named Chatelaine 2018 Woman of the year, and 2018 Toronto’s top influencers by Adweek magazine