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Light field (LF) data are widely used in the immersive representations of the 3D world. Due to the vast amount of information they contain, light field data pose significant challenges for compression. The Fourier Disparity Layer (FDL) representation offers an effective method for light field compact representation. However, the high redundancy of information across viewpoints makes using all viewpoints as input for constructing the FDL inefficient. Therefore, it is crucial to construct FDL representation from sparse viewpoints. Given the varying characteristics of different scene contents, a content adaptive low-rank algorithm is proposed to optimize the FDL representation based on principal component analysis. In this way, a sparse set of viewpoints containing sufficient scene information is selected as the input for FDL representation. The proposed method demonstrates substantial robustness across diverse scene contents and highlights the significant benefits of flexible sampling in enhancing the efficiency of FDL representation.

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Content Adaptive Light Field Representation Using Fourier Disparity Layers

  • 会议论文

  • 发表日期：

    2025年

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  • Wenjing Ling,
  • Xinpeng Huang,
  • Yaqian Luo,
  • Ping An,
  • Chao Yang

摘要

Light field (LF) data are widely used in the immersive representations of the 3D world. Due to the vast amount of information they contain, light field data pose significant challenges for compression. The Fourier Disparity Layer (FDL) representation offers an effective method for light field compact representation. However, the high redundancy of information across viewpoints makes using all viewpoints as input for constructing the FDL inefficient. Therefore, it is crucial to construct FDL representation from sparse viewpoints. Given the varying characteristics of different scene contents, a content adaptive low-rank algorithm is proposed to optimize the FDL representation based on principal component analysis. In this way, a sparse set of viewpoints containing sufficient scene information is selected as the input for FDL representation. The proposed method demonstrates substantial robustness across diverse scene contents and highlights the significant benefits of flexible sampling in enhancing the efficiency of FDL representation.