Graph-based light fields representation and coding using geometry information

This paper describes a graph-based coding scheme for light fields (LF). It first adapts graph-based representations (GBR) to describe color and geometry information of LF. Graph connections describing scene geometry capture inter-view dependencies. They are used as the support of a weighted Graph Fourier Transform (wGFT) to encode disoccluded pixels. The quality of the LF reconstructed from the graph is enhanced by adding extra color information to the representation for a sub-set of sub-aperture images. Experiments show that the proposed scheme yields rate-distortion gains compared with HEVC based compression (directly compressing the LF as a video sequence by HEVC).

[1]  Thomas Maugey,et al.  Graph-based representation for multiview images with complex camera configurations , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[2]  Antonio Ortega,et al.  Graph-based transforms for inter predicted video coding , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[3]  Gary J. Sullivan,et al.  Overview of the High Efficiency Video Coding (HEVC) Standard , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Marcus A. Magnor,et al.  Data compression for light-field rendering , 2000, IEEE Trans. Circuits Syst. Video Technol..

[5]  Bernd Girod,et al.  Light field compression using disparity-compensated lifting and shape adaptation , 2006, IEEE Transactions on Image Processing.

[6]  Marc Levoy,et al.  High performance imaging using large camera arrays , 2005, SIGGRAPH 2005.

[7]  Thomas Maugey,et al.  Graph-based representation for multiview images with complex camera configurations , 2016, ICIP.

[8]  Pascal Frossard,et al.  The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains , 2012, IEEE Signal Processing Magazine.

[9]  Jitendra Malik,et al.  Depth from Combining Defocus and Correspondence Using Light-Field Cameras , 2013, 2013 IEEE International Conference on Computer Vision.

[10]  In-So Kweon,et al.  Accurate depth map estimation from a lenslet light field camera , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Jesse D. Kornblum.  Using JPEG quantization tables to identify imagery processed by software , 2008, Digit. Investig..

[12]  Christine Guillemot,et al.  Bit-rate allocation for multi-view video plus depth , 2011, 2011 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video (3DTV-CON).

[13]  Gavin S. P. Miller,et al.  Lazy Decompression of Surface Light Fields for Precomputed Global Illumination , 1998, Rendering Techniques.

[14]  Thomas Maugey,et al.  Graph-Based Representation for Multiview Image Geometry , 2015, IEEE Transactions on Image Processing.

[15]  Jaejoon Lee,et al.  Edge-adaptive transforms for efficient depth map coding , 2010, 28th Picture Coding Symposium.

[16]  Yun Li,et al.  Efficient intra prediction scheme for light field image compression , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[17]  Reuben A. Farrugia,et al.  Homography-based low rank approximation of light fields for compression , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[18]  Sven Wanner,et al.  Datasets and Benchmarks for Densely Sampled 4D Light Fields , 2013, VMV.

[19]  Andrew Lumsdaine,et al.  Superresolution with the focused plenoptic camera , 2011, Electronic Imaging.