2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSMR-4.5
Paper Title MODELING IMAGE QUALITY SCORE DISTRIBUTION USING ALPHA STABLE MODEL
Authors Yixuan Gao, Xiongkuo Min, Wenhan Zhu, Shanghai Jiao Tong University, China; Xiao-Ping Zhang, Ryerson University, Canada; Guangtao Zhai, Shanghai Jiao Tong University, China
SessionSMR-4: Image and Video Sensing, Modeling, and Representation
LocationArea F
Session Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Poster
Topic Image and Video Sensing, Modeling, and Representation: Statistical-model based methods
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract In recent years, image quality is generally described by a mean opinion score (MOS). However, we observe that an image’s quality ratings given by a group of subjects may not follow a Gaussian distribution and the image quality can not be fully described by a MOS. In this paper, we propose to describe the image quality using a parameterized distribution rather than a MOS, and an objective method is also proposed to predict the image quality score distribution (IQSD). Specifically, we selected 100 images from the LIVE database and invited a large group of subjects to evaluate the quality of these images. By analyzing the subjective quality ratings, we find that the IQSD can be well modeled by an alpha stable model and this model can reflect much more information than MOS. Therefore, we propose an algorithm to model the IQSD described by an alpha stable model. Features are extracted from images based on natural scene statistics and support vector regressors are trained to predict the IQSD described by an alpha stable model. We validate the proposed IQSD prediction model on the collected subjective quality ratings. Experimental results verify the effectiveness of the proposed algorithm in modeling the IQSD.