# A No Reference Image Quality Assessment Metric Based on Visual Perception +
## 2 Previous Work
In literature [8], the authors supposed in Discrete Cosine Transform (DCT) domain the change of image statistical characteristics can predict image quality, and proposed Blind Image Integrity Notator using DCT Statistics algorithm (BLIINDS). Subsequently, [PERSON] et al. extended the BLIINDS algorithm, and proposed an alternate approach that relies on a statistical model of local DCT coefficients which they dubbed blind image integrity rotator using DCT statistic (BLIINDS-II) [28]. To begin with, they partitioned the image into n \(\times\) n blocks and computed the local DCT coefficient. Then, they applied a generalized Gaussian density model to each block of DCT coefficients. Furthermore, they computed functions of the derived model parameters. Finally, they used a simple Bayesian model to predict the quality score of the image. In literature [29], [PERSON] et al. proposed a natural scene statistic-based NR IQA model that operated in the spatial domain, namely blind/reference image spatial quality evaluator (BRISQUE). This method did not compute distortion-specific features, but used scene statistics of locally normalized luminance coefficients to evaluate the quality of the image instead. Since [PERSON] found that these normalized luminance values strongly tend towards a unit normal Gaussian characteristic [30] for natural images, this method used this theory to preprocess the image. Then, they extracted features in spatial domain. Those features mainly contain generalized Gauss distribution characteristics and correlation of adjacent coefficients. Finally, those features were used to map the quality to human rating via SVR model. Over the course of last few years, a number of researchers have devoted time to improving the assessment accuracy of NR IQA methods by taking advantage of known natural scene statistics characteristics. These methods are failed to give full consideration to the human visual characteristics [2]. There is also a deviation between the predicted qualities and the real qualities, since the human visual system is the ultimate assessor of image quality. In addition, almost all of the no reference image quality assessment methods are based on gray scale images, and they do not make full use of the color characteristics of the image. Through above analysis of NR IQA methods, at present, both in distortion-specific and general-purpose methods, almost all of the methods use statistical characteristics of natural images to establish quality evaluation model, and have achieved better effect of evaluation. However, considering the statistical characteristics of natural images can hardly reflect the whole regularities of image quality. There is still a certain gap with the subjective consistency of human vision. Since the HVS is the ultimate assessor of image quality, a number of image quality assessment methods based on an important feature of the HVS, namely, visual perception, are emerging in the present day [10]. Among them, the research of region of interest (or visual saliency) is a major branch of the HVS. In the field of image processing, ROI of an image are the areas that differ significantly from their adjacent regions. They can immediately attract our eyes and capture our attention. Therefore, the ROI is a very important region in the image quality assessment. On the other hand, how to build a computational visual saliency model has been attracting tremendous attention in recent years [32]. Nowadays, a number of computational models to simulate human visual attention have been studied by scholars and some powerful models have been proposed. For further details of ROI refer to [9; 10; 11; 32; 33; 34; 35; 36]. In [33], [PERSON] et al. proposed a visual saliency model which was the first influential and best known visual saliency model. In this paper, we call it Itti model. Itti model mainly contains two aspects. Firstly, they introduced image pyramids for feature extraction, which makes the visual saliency computation efficient. Secondly, [PERSON] et al.