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Weakly Supervised Complets Ranking for Deep Image Quality Modeling.

Luming Zhang, Mingliang Xu, Jianwei Yin

    IEEE Transactions on Neural Networks and Learning Systems
    |March 14, 2020
    PubMed
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    This study introduces a new image quality assessment framework using "complets" (sets of image segments) and a Spatially-Aware Dual Aggregation Network (SDA-Net). This approach improves region-level quality interpretation and models complex image compositions effectively.

    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Current deep image quality models struggle with region-level quality interpretation and incorporating complex compositional features.
    • Existing models often use rectangular patches, which do not effectively represent arbitrarily shaped objects or regions.

    Purpose of the Study:

    • To propose a novel image quality modeling framework that addresses limitations in region-level interpretation and compositional feature incorporation.
    • To develop a method that handles arbitrarily shaped image segments for more accurate quality assessment.

    Main Methods:

    • Introduced 'complets,' sets of image segments capturing spatial/geometric distributions of visual elements.
    • Developed a weakly supervised complet ranking algorithm to quantify complet quality based on image-level discrimination, constraints, and geometry.

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  • Designed a Spatially-Aware Dual Aggregation Network (SDA-Net) with a dual-aggregation mechanism for fusing intra- and inter-complet features.

    • Main Results:

    • The proposed framework effectively interprets and quantifies region-level quality.
    • The method successfully incorporates sophisticated spatial configurations (compositional features) into the quality model.
    • SDA-Net demonstrated superior performance on benchmark datasets, outperforming existing deep quality models.

    Conclusions:

    • The novel framework using complets and SDA-Net significantly advances image quality assessment.
    • The approach provides a more robust and interpretable method for evaluating image quality, especially for complex scenes.
    • This work offers a new direction for deep learning-based image quality models by considering arbitrary shapes and compositional information.