Homography-Based Low Rank Approximation of Light Fields for Compression

Xiaoran Jiang (1) , Mikaël Le Pendu (1) , Reuben A Farrugia (2) , Sheila S Hemami (3) , Christine Guillemot (1)

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

This paper studies the problem of low rank approximation of light fields for compression. A homography-based approximation method is proposed which jointly searches for homographies to align the different views of the light field together with the low rank approximation matrices. We first consider a global homography per view and show that depending on the variance of the disparity across views, the global homography is not sufficient to well-align the entire images. In a second step, we thus consider multiple homographies, one per region, the region being extracted using depth information. We first show the benefit of the joint optimization of the homographies together with the low-rank approximation. The resulting compact representation is then compressed using HEVC and the results are compared with those obtained by directly applying HEVC on the light field views restructured as a video sequence. The experiments using different data sets show substantial PSNR-rate gain of our method, especially for real light fields.

Mots clés: Light fields; Low rank approximation; Homograph; Compression; Light field Coding

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, Sheila S Hemami, Christine Guillemot. Homography-Based Low Rank Approximation of Light Fields for Compression. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Mar 2017, New Orleans, United States. ⟨10.1109/ICASSP.2017.7952369⟩. ⟨hal-01591315⟩