Paper Info
Title
Light Field Image Compression Based On Convolutional Neural Networks And Linear Approximation
Abstract
Computer vision applications such as refocusing, segmentation and classification become one of the most advanced imaging services. Light Field (LF) imaging systems provide a rich semantic information of the scene. Using a dense set of cameras and microlens arrays (Plenoptic camera), the direction of each ray coming from the scene toward the LF capture system can be extracted and represented by spatial and angular coordinates. However, such imaging system induces many drawbacks including the large amount of data produced and complexity increase for scene representation. In this paper, we propose an efficient LF image coding scheme. This scheme first encodes a sparse set of views using the latest hybrid video encoder (JEM). Then, it estimates a second sparse set of views using a linear approximation. At the decoder side, we use a Deep Learning (DL) approach to estimate the whole LF image from the reconstructed sparse sets of views. Experimental results show that the proposed scheme provides higher visual quality and overcomes the state of the art LF image compression solution by 30 % bitrate gain.
Year
Venue
Keywords
2018
2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
Light Field, Machine Learning, Linear approximation, CNN, future video coding
Field
DocType
ISSN
Linear approximation,Computer vision,Pattern recognition,Convolutional neural network,Computer science,Segmentation,Light field,Encoder,Artificial intelligence,Deep learning,Decoding methods,Image compression
Conference
1522-4880
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Nader Bakir100.68
Wassim Hamidouche211533.01
Olivier Déforges317641.52
Khouloud Samrouth473.18
Mohamad Khalil531.43