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    This study introduces a versatile framework unifying full reference and no reference image quality assessment (IQA). The novel model achieves state-of-the-art performance, enhancing both IQA tasks with a single training process.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Processing

    Background:

    • Current image quality assessment (IQA) models are specialized for either full reference or no reference tasks.
    • Human assessment capabilities are more versatile, seamlessly handling both evaluation types.

    Purpose of the Study:

    • To develop a unified framework capable of performing both full reference and no reference IQA.
    • To enhance model versatility and performance by integrating these two assessment types.

    Main Methods:

    • A novel framework employing an encoder for multi-level feature extraction.
    • A Hierarchical Attention module to adaptively manage spatial distortions in both reference types.
    • A Semantic Distortion Aware module to analyze inter-layer feature correlations.

    Main Results:

    • The framework achieves state-of-the-art performance on both full-reference and no-reference IQA tasks when trained separately.
    • Joint training enhances no-reference IQA performance while maintaining competitive full-reference IQA results.
    • The unified approach demonstrates significant advancements in model versatility and efficiency.

    Conclusions:

    • A single, versatile model can effectively address both full reference and no reference IQA tasks.
    • The proposed framework offers improved performance and efficiency compared to task-specific models.
    • This integrated approach represents a significant step forward in the field of image quality assessment.