Objective Quality Prediction of Image Retargeting Algorithms

Quality assessment of image retargeting results is useful when comparing different methods. However, performing the necessary user studies is a long, cumbersome process. In this paper, we propose a simple yet efficient objective quality assessment method based on five key factors: i) preservation of...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2017-02, Vol.23 (2), p.1099-1110
Main Authors: Yun Liang, Yong-Jin Liu, Gutierrez, Diego
Format: Article
Language:English
Subjects:
Algorithms
Atmospheric measurements
Benchmark testing
Benchmarks
Image quality
Image retargeting
Indexes
Particle measurements
Quality assessment
similarity and aesthetic measure
symmetry
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Summary:Quality assessment of image retargeting results is useful when comparing different methods. However, performing the necessary user studies is a long, cumbersome process. In this paper, we propose a simple yet efficient objective quality assessment method based on five key factors: i) preservation of salient regions; ii) analysis of the influence of artifacts; iii) preservation of the global structure of the image; iv) compliance with well-established aesthetics rules; and v) preservation of symmetry. Experiments on the RetargetMe benchmark, as well as a comprehensive additional user study, demonstrate that our proposed objective quality assessment method outperforms other existing metrics, while correlating better with human judgements. This makes our metric a good predictor of subjective preference.
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2016.2517641