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    A new no-reference image quality assessment (NR-IQA) model, KSIQA, uses knowledge sharing to improve performance. It effectively mimics human perception without needing a reference image, outperforming existing NR-IQA methods.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • No-reference image quality assessment (NR-IQA) aims to evaluate image quality without a reference image.
    • Current NR-IQA methods struggle with diverse degradations and lack the reliability of full-reference IQA (FR-IQA).
    • A key challenge is enabling NR-IQA to emulate human and FR-IQA reference-aware reasoning.

    Purpose of the Study:

    • To propose a novel NR-IQA model that effectively simulates reference-aware reasoning.
    • To enhance the generalisability and reliability of NR-IQA across complex image degradations.
    • To achieve performance comparable to or exceeding FR-IQA methods.

    Main Methods:

    • A knowledge-sharing (KS) strategy is employed, designating an FR-IQA model as teacher and an NR-IQA model as student.
    • The student and teacher models share a decoder, unlike traditional knowledge distillation.
    • A Mental Imagery Generation (MIG) module is incorporated for learning mental imagery as a reference.
    • Feature extraction utilizes both Vision Transformer (ViT) and Convolutional Neural Network (CNN) branches.
    • A quality-aware regressor (QAR) with deep ordinal regression infers the final quality score.

    Main Results:

    • The proposed NR-IQA model, KSIQA, demonstrates class-leading performance.
    • KSIQA outperforms current no-reference (NR) techniques on benchmark datasets.
    • The knowledge-sharing strategy and MIG module contribute to improved perceptual cue capture.

    Conclusions:

    • The developed KSIQA model effectively addresses limitations in current NR-IQA approaches.
    • The KS strategy and MIG module enable NR-IQA models to better emulate human perception.
    • KSIQA represents a significant advancement in reliable and generalisable no-reference image quality assessment.