Paper Info
Title
Lossy Compression Of Lenslet Images From Plenoptic Cameras Combining Sparse Predictive Coding And Jpeg 2000
Abstract
This paper proposes a lenslet image compression method scalable from low bitrates to fully lossless. The subaperture images are split into two sets: a set of reference views, encoded by a standard lossy or lossless compressor, and the set of dependent views, which are reconstructed by sparse prediction from the reference set using the geometrical information from the depth map. The set of reference views may contain all views and all views may also be dependent views, in which case the sparse predictive stage does not reconstruct from scratch the views, but it refines in a sequential order all views by combining in an optimal way the information about the same region existing in neighbor views. The encoder transmits to the decoder a segmented version of the scene depthmap, the encoded versions of the reference views, displacements for each region from the central view to each of the dependent views, and finally the sparse predictors for each region and each dependent view. The scheme can be configured to ensure random access to the dependent views, while the reference views are compressed in a backward compatible way, e.g., using JPEG 2000. The experimental results show performance better than that of the baseline standard compressor used, JPEG 2000.
Year
Venue
Keywords
2017
2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
JPEG-PLENO, lenslet image compression, sparse prediction, JPEG 2000, depthmap compression, image warping
Field
DocType
ISSN
Computer vision,Lenslet,Pattern recognition,Lossy compression,Computer science,Transform coding,Image segmentation,Artificial intelligence,Depth map,JPEG 2000,Image compression,Lossless compression
Conference
1522-4880
Citations 
PageRank 
References 
3
0.38
6
Authors
3
Name
Order
Citations
PageRank
Ioan Tabus127638.23
Petri Helin2163.06
Pekka Astola3204.98