Objective Quality Prediction of Image Retargeting Algorithms
Published in IEEE Transactions on Visualization and Computer Graphics, 2016
Abstract: 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.
Recommended citation: Yun Liang, Yong-Jin Liu*, Diego Gutierrez (2017) Objective Quality Prediction of Image Retargeting Algorithms. IEEE Transactions on Visualization and Computer Graphics, Vol. 23, No. 2, pp. 1099-1110, 2017.