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Learning Sparse Representation for Objective Image Retargeting Quality Assessment.

Qiuping Jiang, Feng Shao, Weisi Lin

    IEEE Transactions on Cybernetics
    |April 20, 2017
    PubMed
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    This study introduces a novel objective method for assessing image retargeting quality using sparse representation. It effectively measures perceptual similarity between source and retargeted images, outperforming existing approaches.

    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Image retargeting adapts images to various displays, but evaluating retargeting operator performance is challenging.
    • Subjective quality assessment is reliable but impractical for automated systems.
    • Objective assessment methods are needed for efficient optimization.

    Purpose of the Study:

    • To explore the effectiveness of sparse representation for objective image retargeting quality assessment.
    • To develop a method for measuring perceptual similarity between source and retargeted images.
    • To enable automated and efficient evaluation of image retargeting algorithms.

    Main Methods:

    • Extracting distortion-sensitive features from images.
    • Learning overcomplete dictionaries for compact and robust feature representation.

    Related Experiment Videos

  • Utilizing sparse representation to measure feature similarity between images.
  • Employing a regression method to fuse individual quality scores into an overall score.

    • Main Results:

    • The proposed sparse representation method effectively assesses image retargeting quality.
    • Experimental results demonstrate the superiority of the proposed method over existing approaches.
    • The method successfully incorporates both local geometric structure and global context information.

    Conclusions:

    • Sparse representation offers a powerful tool for objective image retargeting quality assessment.
    • The developed method provides a reliable and efficient alternative to subjective evaluations.
    • This approach facilitates the integration of quality assessment into online optimization systems.