Light Field Compression via a Variational Graph Auto-Encoder

Wenjun TENG, Yong LI, Sam KWONG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

1 Citation (Scopus)

Abstract

Massive light field (LF) data bring tremendous storage and transmission challenges, making the LF compression scheme highly demanded. This paper proposes a novel LF compression method via a variational graph auto-encoder (VGAE), aiming to exploit better the structural information of edges and vertices of the graph LF image. More specifically, the graph adjacency ma-trix and feature matrix are derived from the original graph data in the encoder. Subsequently, a graph convolutional network (GCN) is utilized to determine a multi-dimensional Gaussian distribution, from which the latent representation can be derived by sampling. Finally, the graph LF image can be reconstructed by the inner product of the latent variable in the decoder. The distinct charac-teristics of the proposed scheme lie in that VGAE encoder applies GCN as a function, which can better alleviate the loss of compression. Moreover, the divergence between the original and the reconstructed signals is evaluated using KL divergence to ensure that the estimator is unbiased, leading to better adaptability. The ex-perimental results demonstrate that the proposed method achieves better performance than the state-of-the-art methods.
Original languageEnglish
Title of host publicationProceedings of the 2021 International Conference on Wavelet Analysis and Pattern Recognition
PublisherIEEE
Number of pages6
ISBN (Electronic)9781665466110
ISBN (Print)9781665466127
DOIs
Publication statusPublished - Dec 2021
Externally publishedYes
Event2021 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) - Adelaide, Australia
Duration: 4 Dec 20215 Dec 2021

Conference

Conference2021 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)
Country/TerritoryAustralia
CityAdelaide
Period4/12/215/12/21

Keywords

  • Graph convolutional network
  • Light field compression
  • Variational graph auto-encoder

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