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Perception-Oriented Bidirectional Attention Network for Image Super-Resolution Quality Assessment.

Yixiao Li, Xiaoyuan Yang, Guanghui Yue

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |November 20, 2025
    PubMed
    Summary
    This summary is machine-generated.

    A new Perception-oriented Bidirectional Attention Network (PBAN) enhances full-reference image quality assessment for super-resolution (SR) algorithms. This novel method improves the evaluation of SR image quality by mimicking human visual perception.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Super-resolution (SR) algorithms aim to enhance image resolution.
    • Existing full-reference (FR) image quality assessment (IQA) metrics are limited for evaluating SR algorithms.

    Purpose of the Study:

    • To propose a novel FR-IQA metric for SR algorithms.
    • To develop a network that accurately assesses perceived quality of SR images.

    Main Methods:

    • Introduced the Perception-oriented Bidirectional Attention Network (PBAN) with three modules: image encoder, perception-oriented bidirectional attention (PBA), and quality prediction.
    • Constructed PBA module inspired by human visual system characteristics, incorporating Bidirectional Attention.
    • Utilized Grouped Multi-scale Deformable Convolution and Sub-information Excitation Convolution for adaptive distortion perception.

    Main Results:

    • PBAN effectively encodes images for feature representation.
    • The PBA module bidirectionally constructs visual attention to distortion.
    • Experiments show PBAN outperforms existing state-of-the-art quality assessment methods.

    Conclusions:

    • PBAN provides a robust solution for SR image FR-IQA.
    • The proposed method aligns quality assessment with human perception of distortions.
    • PBAN demonstrates superior performance in evaluating SR algorithm outputs.