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.
Year
DOI
Venue
2018
10.1109/EUVIP.2018.8611756
2018 7th European Workshop on Visual Information Processing (EUVIP)
Keywords
Field
DocType
warped versions,warped references,optimal LS merger,occluded pixels,merged image,original view,sparse predictor,plenoptic camera images,high density camera array data,JPEG Pleno test conditions,earlier scheme,JPEG Pleno Light Field coding standard,current view,reference views,improved view merging,generalized functionality,codec,earlier version,previous hierarchical levels,particular level,versatile light field compression scheme,Light Field image compression,sparse prediction,hierarchical warping
Computer vision,Image warping,Computer science,Transform coding,JPEG,Software,Artificial intelligence,Pixel,Image compression,Codec,Encoding (memory)
Conference
ISSN
ISBN
Citations 
2164-974X
978-1-5386-6898-6
5
PageRank 
References 
Authors
0.72
6
2
Name
Order
Citations
PageRank
Pekka Astola1204.98
Ioan Tabus227638.23