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A Completely Blind Video Integrity Oracle.

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    A new blind video quality assessment (VQA) model, VIIDEO, predicts video quality without reference or human scores. This novel approach offers a significant step toward real-time monitoring of distorted video content.

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    Area of Science:

    • Computer Vision
    • Signal Processing
    • Multimedia Systems

    Background:

    • Current still image quality assessment models lack reference images or human training.
    • Completely blind video quality assessment (VQA) models are still underdeveloped.
    • Assessing video quality without prior information presents a significant challenge.

    Purpose of the Study:

    • To develop a novel, completely blind video quality assessment (VQA) model.
    • To create a VQA model that does not require reference videos or human opinion scores.
    • To enable accurate quality prediction using only the distorted video content.

    Main Methods:

    • Developed the video intrinsic integrity and distortion evaluation oracle (VIIDEO) model.
    • Utilized models of intrinsic statistical regularities in natural videos.
    • Quantified disturbances introduced by distortions without external information.

    Main Results:

    • The VIIDEO algorithm predicts distorted video quality without source, distortion, or human judgment data.
    • VIIDEO outperformed the Mean Squared Error (MSE) on the LIVE VQA database.
    • VIIDEO achieved performance comparable to leading human-trained blind VQA models.

    Conclusions:

    • VIIDEO represents a significant advancement in completely blind video quality assessment.
    • The model's ability to predict quality without external data is a key innovation.
    • VIIDEO paves the way for real-time monitoring of video quality.