  Recently, image quality has been generally describedby a mean opinion score
(MOS). However, we observe that thequality scores of an image given by a group
of subjects are verysubjective and diverse. Thus it is not enough to use a MOS
todescribe the image quality. In this paper, we propose to describeimage
quality using a parameterized distribution rather thana MOS, and an objective
method is also proposed to predictthe image quality score distribution (IQSD).
At first, the LIVEdatabase is re-recorded. Specifically, we have invited a
largegroup of subjects to evaluate the quality of all images in theLIVE
database, and each image is evaluated by a large numberof subjects (187 valid
subjects), whose scores can form a reliableIQSD. By analyzing the obtained
subjective quality scores, wefind that the IQSD can be well modeled by an alpha
stable model,and it can reflect much more information than a single MOS, suchas
the skewness of opinion score, the subject diversity and themaximum probability
score for an image. Therefore, we proposeto model the IQSD using the alpha
stable model. Moreover, wepropose a framework and an algorithm to predict the
alphastable model based IQSD, where quality features are extractedfrom each
image based on structural information and statisticalinformation, and support
vector regressors are trained to predictthe alpha stable model parameters.
Experimental results verifythe feasibility of using alpha stable model to
describe the IQSD,and prove the effectiveness of objective alpha stable model
basedIQSD prediction method.
