DPICT: Deep Progressive Image Compression Using Trit-Planes

Jae Han Lee, Seungmin Jeon, Kwang Pyo Choi, Youngo Park, Chang Su Kim

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    24 Citations (Scopus)

    Abstract

    We propose the deep progressive image compression using trit-planes (DPICT) algorithm, which is the first learning-based codec supporting fine granular scalability (FGS). First, we transform an image into a latent tensor using an analysis network. Then, we represent the latent tensor in ternary digits (trits) and encode it into a compressed bitstream trit-plane by trit-plane in the decreasing order of significance. Moreover, within each trit-plane, we sort the trits according to their rate-distortion priorities and transmit more important information first. Since the compression network is less optimized for the cases of using fewer tritplanes, we develop a postprocessing network for refining reconstructed images at low rates. Experimental results show that DPICT outperforms conventional progressive codecs significantly, while enabling FGS transmission. Codes are available at https://github.com/jaehanlee-mcl/DPICT.

    Original languageEnglish
    Title of host publicationProceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
    PublisherIEEE Computer Society
    Pages16092-16101
    Number of pages10
    ISBN (Electronic)9781665469463
    DOIs
    Publication statusPublished - 2022
    Event2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 - New Orleans, United States
    Duration: 2022 Jun 192022 Jun 24

    Publication series

    NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    Volume2022-June
    ISSN (Print)1063-6919

    Conference

    Conference2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022
    Country/TerritoryUnited States
    CityNew Orleans
    Period22/6/1922/6/24

    Bibliographical note

    Publisher Copyright:
    © 2022 IEEE.

    Keywords

    • Low-level vision
    • Representation learning

    ASJC Scopus subject areas

    • Software
    • Computer Vision and Pattern Recognition

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