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Novel Projection Schemes for Graph-Based Light Field Coding.
Bach, Nguyen Gia; Tran, Chanh Minh; Duc, Tho Nguyen; Tan, Phan Xuan; Kamioka, Eiji.
Afiliação
  • Bach NG; Graduate School of Engineering and Science, Shibaura Institute of Technology, Tokyo 135-8548, Japan.
  • Tran CM; Graduate School of Engineering and Science, Shibaura Institute of Technology, Tokyo 135-8548, Japan.
  • Duc TN; Graduate School of Engineering and Science, Shibaura Institute of Technology, Tokyo 135-8548, Japan.
  • Tan PX; Department of Information and Communications Engineering, Shibaura Institute of Technology, Tokyo 135-8548, Japan.
  • Kamioka E; Graduate School of Engineering and Science, Shibaura Institute of Technology, Tokyo 135-8548, Japan.
Sensors (Basel) ; 22(13)2022 Jun 30.
Article em En | MEDLINE | ID: mdl-35808447
ABSTRACT
In light field compression, graph-based coding is powerful to exploit signal redundancy along irregular shapes and obtains good energy compaction. However, apart from high time complexity to process high dimensional graphs, their graph construction method is highly sensitive to the accuracy of disparity information between viewpoints. In real-world light field or synthetic light field generated by computer software, the use of disparity information for super-rays projection might suffer from inaccuracy due to vignetting effect and large disparity between views in the two types of light fields, respectively. This paper introduces two novel projection schemes resulting in less error in disparity information, in which one projection scheme can also significantly reduce computation time for both encoder and decoder. Experimental results show projection quality of super-pixels across views can be considerably enhanced using the proposals, along with rate-distortion performance when compared against original projection scheme and HEVC-based or JPEG Pleno-based coding approaches.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2022 Tipo de documento: Article