# A No Reference Image Quality Assessment Metric Based on Visual Perception +
## 1 Introduction  
With the rapid development of information technologies and widespread usage of smart phones, digital imaging has become a more and more important medium to acquire information and communicate with other people. These digital images generally suffer impairments during the process of acquisition, compression, transmission, processing and storage. In addition, these phenomena have brought us great difficulties in studying images and understanding the objective world. Because of this, the image quality assessment (IQA) metrics have become a fundamentally important and challenging work. Effective IQA metrics could play important roles in applications such as dynamic monitoring and adjustment of image quality, optimizing the parameter settings of image processing systems, and searching high quality images of medical imaging and so on [1]. For one thing, high quality images can help doctors to judge the severity of the disease in medical fields. For another, such as high-definition televisions, personal digital assistants, internet video streaming, and video on demand, necessitate the means to evaluate the images quality of this information. Therefore, in order to obtain high quality and high fidelity images, the study of image quality evaluation has important practical significance [2; 3]. Because human beings represent terminals for the majority of processed digital images, subjective assessment is the ultimate criterion and reliable image quality assessment method. However, the subjective assessment methods hinder its application in practice because of its disadvantages, such as time-consuming, expensive, complex and laborious. Thus, the goal of objective IQA is to automatically evaluate the quality of images as near to human perception as possible [2]. Based on the availability of a reference image, objective IQA metrics can be classified into full reference (FR), reduced reference (RR), and no reference (NR) approaches [4]. Only recently did FR-IQA methods reach a satisfactory level of performance, as demonstrated by high correlations with human subjective judgments of visual perception. SSIM [5], MS-SSIM [6] and VSNR [7] are examples of successful FR-IQA algorithms. These metrics are based on measuring the similarity between the distorted image and its corresponding original image. However, in real-world applications, where the original is not available, FR metrics are not used. This strictly limits the application domain of FR-IQA algorithms. In addition, the NR-IQA is the only possible algorithm that can be used in the practical application. However, a number of NR-IQA metrics have been testified that they do not always correlate with the perceived image quality [8; 9]. Presently, NR-IQA algorithms generally follow one of two trends: The distortion-specific and general-purpose methods. The former evaluate the distorted image of a specific type while the latter directly measure the image quality evaluation without knowing the type of image distortion. Since the result of evaluation ultimately depends on the feeling of the observers, image evaluation with more perfect and more suitable for the actual quality must be based on human visual, psychological characteristics and organic combine the subjective and objective evaluation methods. A large number of studies have shown that: considering the human visual system (HVS) evaluation methods are better than that without considering the characteristics of HVS evaluation methods [10; 11; 12; 13]. However, existing no-reference image quality assessment algorithms are failed to give full consideration to the human visual features [10]. At present, there are some problems in the field of image quality assessment:  
1. Unable to obtain the original image. 2. Difficult to determine whether the type of distortion of the distorted image exists. 3. Existing methods lack of considering human visual characteristics. 4. How to ensure the characteristics to be not significantly difference caused by the distortion images with same degree yet different types.