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Graph-based representation for multiview image geometry.

Thomas Maugey, Antonio Ortega, Pascal Frossard

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |February 13, 2015
    PubMed
    Summary
    This summary is machine-generated.

    We introduce a novel graph-based representation (GBR) for multiview images, offering compact and controllable geometry information. GBR achieves significant gains in geometry coding rate over traditional depth compression methods.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Computer Graphics

    Background:

    • Multiview image sets require efficient geometry representation for coding and reconstruction.
    • Existing depth compression methods often struggle with quantifying coding errors' impact on interpolation.

    Purpose of the Study:

    • To propose a new, compact, and controllable geometry representation for multiview images.
    • To develop an efficient coding algorithm for this novel representation.
    • To compare the proposed method against classical depth compression techniques.

    Main Methods:

    • Utilizing graphs to encode multiview geometry information, where links represent pixel proximity in 3D space.
    • Developing a graph-based representation (GBR) that adaptively selects geometry information based on decoder prediction complexity.
    • Implementing an efficient coding algorithm for the GBR.

    Main Results:

    • The graph-based representation (GBR) provides a compact and controllable way to describe multiview geometry.
    • GBR achieves significant gains in geometry coding rate compared to depth-based schemes at similar quality.
    • Experimental results demonstrate the effectiveness and potential of the GBR approach.

    Conclusions:

    • The proposed graph-based representation (GBR) offers a promising alternative for multiview geometry coding.
    • GBR enables more efficient and accurate view synthesis by providing precise geometry information.
    • This method adapts to scene complexity, improving coding efficiency.