Efficient Light Field Image Coding with Depth Estimation and View Synthesis

2018 ◽  
Author(s):  
Takanori Senoh ◽  
Kenji Yamamoto ◽  
Nobuji Tetsutani ◽  
Hiroshi Yasuda
Keyword(s):  
Image Coding ◽  
Light Field ◽  
Depth Estimation ◽  
View Synthesis ◽  
Light Field Image

scholarly journals Light Field Image Coding Using VVC standard and View Synthesis based on Dual Discriminator GAN

2021 ◽  
pp. 1-1
Author(s):  
Nader Bakir ◽  
Wassim Hamidouche ◽  
Sid Ahmed Fezza ◽  
Khouloud Samrout ◽  
Olivier Deforges
Keyword(s):  
Image Coding ◽  
Light Field ◽  
View Synthesis ◽  
Light Field Image

scholarly journals Light Field Image Coding Using High-Order Intrablock Prediction

2017 ◽  
Vol 11 (7) ◽  
pp. 1120-1131 ◽  
Author(s):  
Ricardo J. S. Monteiro ◽  
Paulo J. L. Nunes ◽  
Nuno M. M. Rodrigues ◽  
Sergio M. M. Faria
Keyword(s):  
Image Coding ◽  
Light Field ◽  
High Order ◽  
Light Field Image

scholarly journals Attention-Based View Selection Networks for Light-Field Disparity Estimation

2020 ◽  
Vol 34 (07) ◽  
pp. 12095-12103
Author(s):  
Yu-Ju Tsai ◽  
Yu-Lun Liu ◽  
Ming Ouhyoung ◽  
Yung-Yu Chuang
Keyword(s):  
Light Field ◽  
State Of The Art ◽  
Depth Estimation ◽  
Disparity Estimation ◽  
View Selection ◽  
Deep Network ◽  
Depth Maps ◽  
Improve Accuracy ◽  
Light Field Image ◽  
Symmetric Property

This paper introduces a novel deep network for estimating depth maps from a light field image. For utilizing the views more effectively and reducing redundancy within views, we propose a view selection module that generates an attention map indicating the importance of each view and its potential for contributing to accurate depth estimation. By exploring the symmetric property of light field views, we enforce symmetry in the attention map and further improve accuracy. With the attention map, our architecture utilizes all views more effectively and efficiently. Experiments show that the proposed method achieves state-of-the-art performance in terms of accuracy and ranks the first on a popular benchmark for disparity estimation for light field images.

scholarly journals Light Field Image Coding using High Order Prediction Training

2018 ◽  
Author(s):  
Ricardo J. S. Monteiro ◽  
Paulo J. L. Nunes ◽  
Sergio M. M. Faria ◽  
Nuno M. M. Rodrigues
Keyword(s):  
Image Coding ◽  
Light Field ◽  
High Order ◽  
Light Field Image

scholarly journals Light Field Image Coding with Image Prediction in Redundancy

2020 ◽  
Vol 2 (3) ◽  
pp. 160-167
Author(s):  
Prof. Sathish
Keyword(s):  
Image Coding ◽  
Light Field ◽  
Video Sequence ◽  
Data Representation ◽  
Experimental Results ◽  
Prediction Methods ◽  
Image Prediction ◽  
Full Capacity ◽  
Hybrid Data ◽  
Light Field Image

The proposed work involves a hybrid data representation using efficient light field coding. The existing light field coding solution are implemented using sub-aperture or micro-images. However, the full capacity in terms of intrinsic redundancy in light field images is not completely explored. This paper represents a hybrid data representation which explores four major redundancy types. Using coding block, the most predominant redundancy is exploited to find the optimum coding solution that provides maximum flexibility. To show how efficient the hybrid representation works, we have proposed a combination of pseudo-video sequence coding approach with pixel prediction methods. The observed experimental results shows a positive bit rate saving when compared to other similar methods. Similarly, the proposed method is also said to outperform other coding algorithms such as WaSP and MuLE when compared on a HEVC-based benchmark.

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