Paper Info
Title
Efficient Directional And L1-Optimized Intra-Prediction For Light Field Image Compression
Abstract
Light field images can be conveniently captured by consumer-level plenoptic cameras. However, as the resulting data rates are very high, providing efficient compression for this type of data is of critical importance. This remains an open problem which has recently attracted a lot of attention from the coding community. State-of-the-art compression systems prove to be inefficient when directly applied on this type of data due to the inherent spatial discontinuities in light field images. In this paper, a novel intra-prediction method for disk-shaped pixel clusters is proposed. An L1 minimization of the prediction residuals is performed followed by clustering of the predictors, leading to an optimized set of predictors for the macro-pixels. Furthermore, directional intra-prediction modes based on HEVC are devised for the macro-pixels. Experimental results obtained on the EPFL light field image dataset demonstrate that the proposed coding scheme yields an average of 3.22 dB and 1.45 dB gain in PSNR, and 59.6% and 30.88% average rate savings compared to HEVC and the state-of-the-art in light field image coding respectively.
Year
Venue
Keywords
2017
2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
light field images, intra prediction, directional mode, L1 optimization, image compression
Field
DocType
ISSN
Computer vision,Compression (physics),Classification of discontinuities,Open problem,Pattern recognition,Computer science,Coding (social sciences),Light field,Pixel,Artificial intelligence,Cluster analysis,Image compression
Conference
1522-4880
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Rui Zhong152.83
Shi-Zheng Wang2778.39
Bruno Cornelis34811.06
Yuanjin Zheng432872.86
Junsong Yuan53703187.68
Adrian Munteanu666480.29