# No-Reference Image Quality Assessment Based on Image Multi-Scale Contour Prediction
## 1 Introduction  
Image is an important source of information for human perception and machine recognition [1, 2, 3]. In order for machines to have visual perception, not only does the equipment need to be capable of predictive maintenance [4], but it also needs to capture high-quality images. Image quality plays a decisive role in the sufficiency and accuracy of the acquired information. However, the image is inevitably distorted in the process of acquisition, compression, processing, transmission, and display. How to measure the quality of the image and evaluate whether the image meets a specific requirement becomes a problem. To solve this problem, it is necessary to establish an effective image quality assessment (IQA) system. At present, IQA methods can be divided into subjective evaluation methods and objective evaluation methods. The former relies on the subjective perception of experimenters to evaluate the quality of the object. The latter simulates the perception mechanism of the human visual system based on the quantitative indicators given by the model. According to the classification of images, IQA can be divided into facial image quality [5, 6], synthetic image quality, and so on. Below, we introduce the perspective of model improvement.  
Objective image quality assessments are quite meaningful. They can provide feedback and optimization for denoising algorithms, provide early evaluation and preprocessing of image data for computer vision tasks, and even indirectly reflect the quality of the shooting equipment. According to whether a reference image is needed, the objectiveimage quality assessment is divided into full-reference image quality assessment (FR-IQA), reduced reference image quality assessment (RR-IQA) and no-reference image quality assessment (NR-IQA). The FR-IQA [7; 8; 9; 10] method requires a distortion-free reference image and compares the information or feature similarity of two images to obtain the evaluation result of the distorted image. The RR-IQA [11; 12; 13] method is based on part of the characteristic information of the reference image. The NR-IQA method directly evaluates the quality of distorted images. Despite some NR-IQA methods not needing reference images in the testing phase, they still need reference images in the training phase [14; 15]. According to the type of distortion, the method is divided into specific types of distortion and general image quality assessment. Classical methods are based on natural scene statistics (NSS) [10; 16; 17; 18; 19], transform domain [9; 20], gradient features [17] and unsupervised learning [21; 22], etc.  
Since 2014, most of the NR-IQA methods have adopted CNN-based models, and researchers have constantly changed and deepened the model structure. CNN is a simulation of the biological visual system. With research on the physiology and anatomy of the biological optic nerve, an increasing number of scholars have begun to use mathematical models to reveal the processing mechanism of visual information. Inspired by the biological vision, we simulated the mechanism of the biological optic nerve and receptive field, and proposed a two-stage training method that does not require reference images. This was tested on the LIVE [23] data set and TID2013 [24] data set. The innovations of this article are as follows :  
1. Using multi-scale contour features as the one-stage regression target to solve the problem of too few data sets.
2. Designing the different learning labels of different layers of the model to simulate the evaluation of human eyes on images at different distances.
3. Designing a central attention peripheral inhibition module to simulate the mechanism of the receptive field of retinal ganglion cells.  
The following sections of the paper are organized as follows. Section 2 introduces the current status of the CNN-based NR-IQA. Section 3 details the framework of the model proposed in this paper. Section 4 presents the test results of the model. Section 5 concludes the paper.