Full Reference Quality Assessment for Image Retargeting Based on Natural Scene Statistics Modeling and Bi-Directional Saliency Similarity
Abstract
Image retargeting technology has been widely studied to adapt images for the devices with heterogeneous screen resolutions. Meanwhile effective objective retargeting quality assessment algorithms are also very important for optimizing and selecting favorable retargeting methods. Unlike previous assessment algorithms which rely on image local structure features and unidirectional prediction of information loss, we propose a Bi-directional Natural Salient Scene Distortion model (BNSSD) including image Natural Scene Statistics (NSS) measurement, salient global structure distortion measurement and bi-directional salient information loss measurement. First, we propose a new NSS model in log-Gabor domain and verify its effectiveness in reflecting nature scene statistical distortions introduced during the retargeting process. Second, the concept of salient global structure distortion is proposed to measure the global structure uniformity in the corresponding salient regions between original and retargeted images. Finally, we propose a bidirectional salient information loss metric to measure the information loss between salient areas in original image and retargeted image. The effectiveness of the BNSSD model is verified on two widely recognized public databases, and the experiment results show that our method outperforms the state-of-the-art algorithms under different statistical assessment criteria.
Paper
Z. Chen, J. Lin, N. Liao and C. W. Chen, "Full Reference Quality Assessment for Image Retargeting Based on Natural Scene Statistics Modeling and Bi-Directional Saliency Similarity," in IEEE Transactions on Image Processing, vol. 26, no. 11, pp. 5138-5148, Nov. 2017.
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