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SASIC: Stereo Image Compression With Latent Shifts and Stereo Attention

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F22%3A00569938" target="_blank" >RIV/67985556:_____/22:00569938 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1109/CVPR52688.2022.00074" target="_blank" >http://dx.doi.org/10.1109/CVPR52688.2022.00074</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/CVPR52688.2022.00074" target="_blank" >10.1109/CVPR52688.2022.00074</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    SASIC: Stereo Image Compression With Latent Shifts and Stereo Attention

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022)

  • ISBN

    978-1-6654-6946-3

  • ISSN

    1063-6919

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

    661-670

  • Publisher name

    IEEE

  • Place of publication

    Piscataway

  • Event location

    New Orleans

  • Event date

    Jun 19, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000867754200066