Lossless compression of subaperture images using context modeling

Ionut Schiopu, Moncef Gabbouj, Atanas Gotchev, Miska M. Hannuksela

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    9 Citations (Scopus)


    The paper proposes a method for lossless compression of sub-aperture image stacks obtained by rectifying light-field images captured by a plenoptic camera. We exploit the similarities between two subaperture images using a predictive coding algorithm, where the current view is predicted from one reference view. Context modeling is the main technique used to reduce the image file size. A suitable image segmentation and a template context are used by the context tree algorithm for encoding up to the smallest detail in each subaperture image. Entropy coding is configured by a residual analysis module. The results show improved performance compared to the state-of-the-art encoders.
    Original languageEnglish
    Title of host publication2017 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON)
    ISBN (Electronic)978-1-5386-1635-2
    Publication statusPublished - 2018
    Publication typeA4 Article in conference proceedings
    Event3DTV Conference -
    Duration: 1 Jan 1900 → …

    Publication series

    ISSN (Electronic)2161-203X


    Conference3DTV Conference
    Abbreviated title3DTV-CON
    Period1/01/00 → …


    • lossless compression
    • plenoptic image
    • context modeling
    • Image segmentation
    • predictive coding

    Publication forum classification

    • Publication forum level 1


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