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End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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Cross-Modal Multivariate Pattern Analysis
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Joint Local and Nonlocal Progressive Prediction for Versatile Video Coding.

Meng Lei, Falei Luo, Xinfeng Zhang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 29, 2022
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    Summary
    This summary is machine-generated.

    This study introduces a novel video coding prediction scheme for Versatile Video Coding (VVC). The method enhances prediction accuracy for complex content, achieving significant bit-rate reductions without extra signaling.

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    Area of Science:

    • Video Coding
    • Digital Signal Processing
    • Computer Vision

    Background:

    • Versatile Video Coding (VVC) utilizes directional intra modes and reference lines for prediction efficiency.
    • Complex content remains challenging for accurate prediction using only adjacent samples.
    • Existing nonlocal prediction methods face limitations due to explicit signaling or matching errors.

    Purpose of the Study:

    • To improve nonlocal prediction accuracy in VVC without additional signaling.
    • To enhance the coding efficiency of complex video content.
    • To develop a joint local and nonlocal progressive prediction scheme.

    Main Methods:

    • A two-stage prediction process is proposed: initial nonlocal prediction via template matching (TMP).
    • A local template, incorporating inner textures and reconstruction data, is designed for the second prediction stage.
    • The nonlocal matching process is progressively refined using the local template, enabling implicit derivation of accurate predictors.

    Main Results:

    • The proposed scheme achieves 1.02% BD-Rate reduction for natural video sequences.
    • A 2.31% BD-Rate reduction is observed for screen content videos under the all-intra (AI) configuration.
    • Experimental results were validated using VVC Test Model (VTM-9.0).

    Conclusions:

    • The joint local and nonlocal progressive prediction scheme effectively enhances prediction accuracy.
    • Implicit derivation of nonlocal predictors using local information improves coding efficiency.
    • The method offers a promising approach for optimizing VVC performance, particularly for complex visual data.