@inproceedings{6088ea1dd1c2465f9b7bf34eb9204483,
title = "Lossless compression of subaperture images using context modeling",
abstract = "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.",
keywords = "lossless compression, plenoptic image, context modeling, Image segmentation, predictive coding",
author = "Ionut Schiopu and Moncef Gabbouj and Atanas Gotchev and Hannuksela, {Miska M.}",
note = "jufoid=50006; 3DTV Conference, 3DTV-CON ; Conference date: 01-01-1900",
year = "2018",
doi = "10.1109/3DTV.2017.8280403",
language = "English",
publisher = "IEEE",
booktitle = "2017 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON)",
}