Abstract
A reduced reference quality assessment algorithm for image retargeting by earth mover’s distance is proposed in this paper. In the reference image, all the feature points are extracted using scale invariant feature transform. Let the histograms of image patch around each feature point be local information, and the histograms of saliency feature as global information. Those feature information is extracted at the sender side and transmitted to the receiver side. After that, the same feature information extraction process is performed for the retargeted image at the receiver side. Finally, all feature information of the reference and retargeted images is used collectively to compute the quality of the retargeted image. An overall quality score is calculated from the local and global similarity measure using earth mover’s distance between reference and retargeted images. The key step in our algorithm is to provide an earth mover’s distance metric in a manner that indicates how the local and global information in the reference image is preserved in corresponding retargeted image. Experimental results show that the proposed algorithm can improve the image quality scores on four common criteria in the retargeted image quality assessment community.
Highlights
Most Image Quality Assessment (IQA) models based on full reference (FR) and achieved very good results, while most no reference (NR) IQA methods are designed for some predefined specific distortion types
We have proposed an RR retargeted IQA algorithm using EMD
Each reference image is retargeted through a retargeting channel, and the local and global information, which usually has fewer data than reference image, is transferred through a specific ancillary channel
Summary
Image quality is a basic concept in many image processing and computer vision applications, such as acquisition, transmission, and display. With advances in information technology and visual communication, assessing image quality has become a fundamental and challenging problem. Image Quality Assessment (IQA) automatically measures the image visual quality by effective computational models [1]. The image quality assessment (IQA) approach attempts to estimate the image quality based on human visual perception in an objective manner. Most IQA models based on full reference (FR) and achieved very good results, while most no reference (NR) IQA methods are designed for some predefined specific distortion types. Reduced reference (RR) IQA algorithms provide a proper compromise between FR and NR approaches, and they estimate the image quality with limited access to the reference image [2]
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