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    Area of Science:

    • Computer Vision
    • Image Processing
    • Multimedia Systems

    Background:

    • Multi-view depth data is essential for 3D applications like virtual reality and free viewpoint video.
    • Lossy compression of depth videos introduces artifacts, degrading visual quality and hindering interactive applications.

    Purpose of the Study:

    • To propose a cross-view multi-lateral filtering scheme to enhance the quality of compressed depth maps and videos.
    • To address artifacts caused by quantization errors in lossy compression for multi-view depth data.

    Main Methods:

    • Developed a cross-view multi-lateral filtering scheme within asymmetric multi-view video with depth compression.
    • Enhanced distorted depth maps using non-local candidates from neighboring viewpoints and time-slots.
    • Clustered candidates into macro super pixels representing cross-view, spatial, and temporal relationships.

    Main Results:

    • Achieved significant quality gains in static depth maps (PSNR) and dynamic depth videos (SSIM).
    • Successfully recovered object contours in compressed depth videos, as shown in subjective evaluations.
    • Demonstrated effective artifact management for interactive video and restoration of surfaces for 3D modeling.

    Conclusions:

    • The proposed filtering scheme effectively improves the quality of compressed multi-view depth data.
    • Outperforms existing state-of-the-art filters, making it suitable for advanced interactive and remote applications.
    • Successfully manages artifacts and restores surfaces, enhancing usability in virtual reality and 3D modeling.