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    We introduce the Fusion of Unified Quality Evaluators (FUNQUE) framework to improve video quality assessment. FUNQUE enhances accuracy and computational efficiency in video compression, addressing limitations of the Visual Multimethod Assessment Fusion (VMAF) algorithm.

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    Area of Science:

    • Computer Vision
    • Signal Processing
    • Multimedia Engineering

    Background:

    • The Visual Multimethod Assessment Fusion (VMAF) algorithm is a leading video quality prediction tool widely adopted in streaming and social media.
    • VMAF's computational expense, due to its heterogeneous quality models, presents a bottleneck in modern video compression pipelines, especially with hardware-accelerated encoding.
    • Efficient video quality assessment is crucial for optimizing video compression and delivery.

    Purpose of the Study:

    • To develop a novel framework, Fusion of Unified Quality Evaluators (FUNQUE), that enhances the accuracy and computational efficiency of video quality assessment.
    • To address the computational burden of existing state-of-the-art methods like VMAF.
    • To propose low-complexity fused-feature models that improve video quality prediction performance.

    Main Methods:

    • Developed the Fusion of Unified Quality Evaluators (FUNQUE) framework, incorporating computation sharing mechanisms.
    • Utilized a novel transform sensitive to visual perception to enhance prediction accuracy.
    • Expanded the FUNQUE framework to create a suite of improved, low-complexity fused-feature models.

    Main Results:

    • Achieved state-of-the-art performance in video quality assessment.
    • Improved accuracy by 4.2% to 5.3% compared to existing methods.
    • Increased computational efficiency by factors of 3.8 to 11 times, significantly reducing processing bottlenecks.

    Conclusions:

    • The FUNQUE framework and its derived models offer a substantial advancement in video quality assessment.
    • FUNQUE effectively balances high accuracy with significant computational efficiency, making it suitable for demanding video compression pipelines.
    • This research alleviates the quality assessment bottleneck, paving the way for more efficient video processing and delivery.