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Measuring Sensitivity to Viewpoint Change with and without Stereoscopic Cues
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Adaptive Effective Wiener Filter- and Regression-based Upsampling for Asymmetric Resolution Stereoscopic Video

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    This study introduces an adaptive upsampling method for asymmetric resolution stereoscopic video coding (ARSVC). The novel RTCWF-LRIPRC approach significantly enhances upsampled image quality for 3D video display.

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    Area of Science:

    • Computer Vision
    • Signal Processing
    • Multimedia Systems

    Background:

    • Asymmetric Resolution Stereoscopic Video Coding (ARSVC) reduces bitrate by using a full-sized left view and a quarter-sized right view.
    • Upsampling the downsampled right-view video is crucial for displaying 3D scenes on client devices.

    Purpose of the Study:

    • To develop an effective adaptive upsampling method for ARSVC.
    • To improve the visual quality of upsampled stereoscopic video sequences.

    Main Methods:

    • Proposed Resolution- and Texture-Consistency Wiener Filter (RTCWF) for enhanced spatial domain upsampling accuracy.
    • Developed Linear Regression-based Interview Prediction with Residual Compensation (LRIPRC) for improved interview domain accuracy.
    • Introduced an adaptive fusion approach (RTCWFLRIPRC) integrating RTCWF and LRIPRC to maximize upsampled image quality.

    Main Results:

    • Experimental results in 3D-HEVC demonstrated superior performance of the RTCWF-LRIPRC method.
    • The proposed method outperformed state-of-the-art upsampling techniques across six quality metrics.
    • The method met execution time requirements for practical applications.

    Conclusions:

    • The RTCWF-LRIPRC method offers significant quality improvements for ARSVC upsampling.
    • This approach effectively enhances the visual experience of stereoscopic video.
    • The adaptive fusion strategy successfully integrates spatial and interview domain predictions.