Light Field Compression with Homography-based Low Rank Approximation

Xiaoran Jiang (1) , Mikaël Le Pendu (1) , Reuben A Farrugia (2) , Christine Guillemot (1)

1 Sirocco - Analysis representation, compression and communication of visual data
2 University of Malta [Malta]

This paper describes a light field compression scheme based on a novel homography-based low rank approximation method called HLRA. The HLRA method jointly searches for the set of homographies best aligning the light field views and for the low rank approximation matrices. The light field views are aligned using either one global homography or multiple homographies depending on how much the disparity across views varies from one depth plane to the other. The light field low-rank representation is then compressed using HEVC. The best pair of rank and QP parameters of the coding scheme, for a given target bit-rate, is predicted with a model defined as a function of light field disparity and texture features. The results are compared with those obtained by directly applying HEVC on the light field views restructured as a pseudo-video sequence. The experiments using different data sets show substantial PSNR-rate gain of our compression algorithm, as well as the accuracy of the proposed parameter prediction model, especially for real light fields. A scalable extension of the coding scheme is finally proposed.

Mots clés: Homography; Light field coding; Low rank approximation; Light fields; Compression

Domaines: Traitement des images [eess.IV]; Traitement du signal et de l'image [eess.SP]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Xiaoran Jiang, Mikaël Le Pendu, Reuben A Farrugia, Christine Guillemot. Light Field Compression with Homography-based Low Rank Approximation. IEEE Journal of Selected Topics in Signal Processing, 2017, ⟨10.1109/JSTSP.2017.2747078⟩. ⟨hal-01591349⟩