# A No Reference Image Quality Assessment Metric Based on Visual Perception +
## 2 Previous Work  
Before proceeding, we state some salient aspects of NR-IQA methods. Present day depending on whether we know the type of distortion image or not, NR-IQA methods can be divided into two categories: distortion-specific approaches and general-purpose approaches [14]. Distortion-specific algorithm is capable of assessing the quality of images distorted by a particular distortion type, such as blockiness, ringing, blur after compress, and noise generated during image transmission. In literatures [15; 16; 17; 18], the authors proposed some approaches for JPEG compression images. In [17], [PERSON] et al. aimed to develop NR-IQA algorithms for JPEG images. At first, they not only established a JPEG image database, but also subjective experiments were conducted on the database. Furthermore, they proposed a computational and memory efficient NR IQA model for JPEG images, and estimated the blockiness by the average differences across block boundaries. They used the average absolute difference between in-block image samples and the zero-crossing rate to estimate the activity of the image signal. At last, subjective experiment results were used to train the model, which achieved good quality prediction performance. For JPEG2000 compression, distortion in an image is generally modeled by measuring edge-spread using an edge-detection based approach and this edge spread is related to quality [5; 19; 20; 21; 22]. The literatures [23; 24; 25] are evaluation algorithms for blur. [PERSON] et al. calculated image structural similarity by constructing fuzzy counterpart [20]. Firstly, they used a low pass filter to blur the original blurred image and produce re-blurred image. Then they divided the edge dilation image into \(8\times 8\) blocks. In addition, the sub-blocks were classified into edge dilation block and smooth block. In addition, the gradient structural similarity index was defined by different weighs according to the different types of blocks. Finally, the blur average value of the whole image was produced. The experimental results were shown that the method was more reasonable and stable than others. However, these distortion-specific methods are obviously limited by the fact that it is necessary to know the distortion types in advance, which makes their application field limited [3]. Universal methods are applicable to all distortion types and can be used in any occasion. In recent years, researchers proposed many universal no reference image quality assessment methods. However, most of these methods relied on the prior knowledge of subjective value and distortion types, and used machine learning methods to get the image quality evaluation score. Some meaningful universal methods are reported in the literature [8; 26; 27; 28; 29; 30; 31]. In [26], the author proposed a new two-step framework for no-reference image quality assessment based on natural scene statistics (NSS), namely Blind Image Quality Indices, (BIQI). Firstly, the algorithm estimated the presence of a set of distortions in the image. Secondly, they evaluated the quality of the image according to those distortions. In literature [27], [PERSON] et al. proposed a blind IQA method based on the hypothesis that natural scenes have some certain statistical properties. In addition, those properties can make the quality of image degeneration. This algorithm was called Distortion Identification- based Image Verity and Integrity Evaluation (DIIVINE) and also was a two-step framework. The DIIVINE mainly consists of three steps. Firstly, the wavelet coefficients were normalized. Then, they calculated the statistical characteristics of the wavelet coefficients, such as scale and orientation selective statistics, orientation selective statistics, correlations across scales and spatial correlation. Finally, the quality of the image was calculated using those statistical features. The method has achieved a good performance on evaluation.