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Subaperture image segmentation for lossless compression

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


Original languageEnglish
Title of host publicationProceedings of the 7th International Conference on Image Processing Theory, Tools and Applications, IPTA 2017
Number of pages6
ISBN (Electronic)9781538618417
Publication statusPublished - 8 Mar 2018
Publication typeA4 Article in a conference publication
EventInternational Conference on Image Processing Theory, Tools and Applications - Montreal, Canada
Duration: 28 Nov 20171 Dec 2017


ConferenceInternational Conference on Image Processing Theory, Tools and Applications


The paper proposes an image segmentation method for lossless compression of plenoptic images. Each light-field image captured by the plenoptic camera is processed to obtain a stack of subaperture images. Each subaperture image is encoded by using a gradient-base detector which classifies the image edges and designs refined contexts for an improved prediction and segmentation. The paper's main contribution is a new segmentation method which generates a preliminary segmentation, either by scaling the intensity differences or by using a quantum cut based algorithm, and merges it with an edge ranking-based segmentation. The results show around 2% improved performance compared to the state-of-the-art for a dataset of 118 plenoptic images.


  • image segmentation, Lossless compression, plenoptic image, quantum cut segmentation

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