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Learning to rank for blind image quality assessment.

Fei Gao, Dacheng Tao, Xinbo Gao

    IEEE Transactions on Neural Networks and Learning Systems
    |January 24, 2015
    PubMed
    Summary

    This study introduces a new method for blind image quality assessment (BIQA) using preference image pairs (PIPs). This approach overcomes limitations of traditional subjective scoring, enabling more efficient and accurate image quality prediction.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Signal Processing

    Background:

    • Blind Image Quality Assessment (BIQA) traditionally relies on subjective scores, which are often imprecise, biased, and inconsistent.
    • Collecting large-scale subjective datasets for BIQA is challenging due to logistical complexities and human evaluation variability.
    • Existing BIQA models struggle with scalability and extension to new distortion types.

    Purpose of the Study:

    • To develop a robust and efficient BIQA model that overcomes the limitations of subjective scoring.
    • To leverage preference image pairs (PIPs) for training BIQA models due to their precision and low-cost generation.
    • To enable easier extension of BIQA models to novel distortion categories.

    Main Methods:

    • The proposed BIQA method utilizes a learning-to-rank approach, formulating the problem as a classification task.

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  • A multiple kernel learning algorithm based on group lasso is investigated for mapping image features to preference labels.
  • A strategy for estimating perceptual image quality scores from learned preferences is presented.

    • Main Results:

    • The proposed BIQA method demonstrates high effectiveness in predicting perceptual image quality.
    • Experimental results show performance comparable to state-of-the-art BIQA algorithms.
    • The method proves to be easily extendable to new distortion categories.

    Conclusions:

    • Leveraging preference image pairs (PIPs) offers a cost-effective and robust alternative to traditional subjective scoring for BIQA.
    • The learning-to-rank approach with multiple kernel learning provides a powerful framework for developing accurate BIQA models.
    • The proposed method offers a scalable and adaptable solution for blind image quality assessment across various distortions.