Tampere University of Technology

TUTCRIS Research Portal

WaSP: Hierarchical Warping, Merging, and Sparse Prediction for Light Field Image Compression

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

Standard

WaSP: Hierarchical Warping, Merging, and Sparse Prediction for Light Field Image Compression. / Astola, Pekka; Tabus, Ioan.

2018 7th European Workshop on Visual Information Processing (EUVIP). IEEE, 2018.

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

Harvard

Astola, P & Tabus, I 2018, WaSP: Hierarchical Warping, Merging, and Sparse Prediction for Light Field Image Compression. in 2018 7th European Workshop on Visual Information Processing (EUVIP). IEEE, European Workshop on Visual Information Processing, 1/01/00. https://doi.org/10.1109/EUVIP.2018.8611756

APA

Vancouver

Author

Astola, Pekka ; Tabus, Ioan. / WaSP: Hierarchical Warping, Merging, and Sparse Prediction for Light Field Image Compression. 2018 7th European Workshop on Visual Information Processing (EUVIP). IEEE, 2018.

Bibtex - Download

@inproceedings{e2a89137df8a4917926bc7f873f154b4,
title = "WaSP: Hierarchical Warping, Merging, and Sparse Prediction for Light Field Image Compression",
abstract = "We propose a versatile light field compression scheme that is organized on hierarchical levels, where all views belonging to a particular level are encoded using several views already encoded in the previous hierarchical levels. The new scheme builds on an earlier version of our codec, and provides a more generalized functionality with improved view merging. The operations needed when one view is encoded conditional on its reference views are: first warping its reference views to the location of the current view and partitioning the pixels according to their state of occlusion in various warped versions; then merging the warped references using one optimal LS merger for each class of occluded pixels; finally, adjustment of the overall merged image to the original view by using a sparse predictor. The new scheme is applied to both plenoptic camera images and high density camera array data, and is evaluated in accordance with the JPEG Pleno test conditions. We compare the performance of the proposed codec to that of the HEVC anchors defined in the JPEG Pleno test conditions. We also make comparisons to the performance achieved by our earlier scheme. The proposed codec is publicly available on GitHub and it was accepted as the Verification Model (VM) 1.0 software for JPEG Pleno Light Field coding standard.",
keywords = "Encoding, Cameras, Image color analysis, Image coding, Transform coding, Codecs, Merging",
author = "Pekka Astola and Ioan Tabus",
year = "2018",
month = "11",
doi = "10.1109/EUVIP.2018.8611756",
language = "English",
isbn = "978-1-5386-6898-6",
publisher = "IEEE",
booktitle = "2018 7th European Workshop on Visual Information Processing (EUVIP)",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - WaSP: Hierarchical Warping, Merging, and Sparse Prediction for Light Field Image Compression

AU - Astola, Pekka

AU - Tabus, Ioan

PY - 2018/11

Y1 - 2018/11

N2 - We propose a versatile light field compression scheme that is organized on hierarchical levels, where all views belonging to a particular level are encoded using several views already encoded in the previous hierarchical levels. The new scheme builds on an earlier version of our codec, and provides a more generalized functionality with improved view merging. The operations needed when one view is encoded conditional on its reference views are: first warping its reference views to the location of the current view and partitioning the pixels according to their state of occlusion in various warped versions; then merging the warped references using one optimal LS merger for each class of occluded pixels; finally, adjustment of the overall merged image to the original view by using a sparse predictor. The new scheme is applied to both plenoptic camera images and high density camera array data, and is evaluated in accordance with the JPEG Pleno test conditions. We compare the performance of the proposed codec to that of the HEVC anchors defined in the JPEG Pleno test conditions. We also make comparisons to the performance achieved by our earlier scheme. The proposed codec is publicly available on GitHub and it was accepted as the Verification Model (VM) 1.0 software for JPEG Pleno Light Field coding standard.

AB - We propose a versatile light field compression scheme that is organized on hierarchical levels, where all views belonging to a particular level are encoded using several views already encoded in the previous hierarchical levels. The new scheme builds on an earlier version of our codec, and provides a more generalized functionality with improved view merging. The operations needed when one view is encoded conditional on its reference views are: first warping its reference views to the location of the current view and partitioning the pixels according to their state of occlusion in various warped versions; then merging the warped references using one optimal LS merger for each class of occluded pixels; finally, adjustment of the overall merged image to the original view by using a sparse predictor. The new scheme is applied to both plenoptic camera images and high density camera array data, and is evaluated in accordance with the JPEG Pleno test conditions. We compare the performance of the proposed codec to that of the HEVC anchors defined in the JPEG Pleno test conditions. We also make comparisons to the performance achieved by our earlier scheme. The proposed codec is publicly available on GitHub and it was accepted as the Verification Model (VM) 1.0 software for JPEG Pleno Light Field coding standard.

KW - Encoding

KW - Cameras

KW - Image color analysis

KW - Image coding

KW - Transform coding

KW - Codecs

KW - Merging

U2 - 10.1109/EUVIP.2018.8611756

DO - 10.1109/EUVIP.2018.8611756

M3 - Conference contribution

SN - 978-1-5386-6898-6

BT - 2018 7th European Workshop on Visual Information Processing (EUVIP)

PB - IEEE

ER -