# No-Reference Image Quality Assessment Based on Image Multi-Scale Contour Prediction
## 2 Related Work  
The current NR-IQA methods based on convolutional neural network (CNN) are divided into image-based and patch-based according to the input image [25]. In the early years, in order to increase training data, most of the methods were based on patch-based methods.  
In 2014, [PERSON] et al. [26] used CNN for the NR-IQA for the first time. The author first normalized the image, and then divided it into 32 \(\times\) 32 non-overlapping image patches, used the CNN network to estimate the quality score of each image patch, and the final image quality score was the average score of all image patches. The CNN network used in this method has one convolutional layer with max and min pooling, two fully connected layers and an output node. Although this method has better results than traditional manual feature extraction methods, it has the following shortcomings when the distortion types are complex and diverse: (1) It is unreasonable to use the average of the quality scores of all image patches as a quality score for the entire image. (2) It is unreasonable to use the global subjective score as the image local quality score for training.  
In order to solve the problem 1, [PERSON] et al. [27] proposed a method including a weight estimation module. During the training stage, a sub-network is used to train the weights of image patches. The method proposed by [27] used a deeper and more complex neural network structure than the method of [PERSON] et al. [26] Therefore, the network learned more image features, and its performance was improved. However, as the network deepens, the problem of too few training data sets becomes more serious. As in the previous method,with the aim of increasing the training data, the network input was the \(32\times 32\) image patch, and the quality score of the image patch was still the quality score of the entire image.  
For the purpose of solving problem 2, many researchers have proposed a method of first generating the local quality score of the distorted image as the one-stage regression target. In 2017, [PERSON] proposed a two-stage method (BIECON) [14]. The first step is to use the FR-IQA method to obtain the local quality score and use the local quality score as the target label of the CNN model to predict the quality score of image patches. In the second step, the subjective quality score of the distorted image was used as the target label, and all model parameters were optimized at the same time. In spite of the fact that the BIECON method solved the unreasonable problem of using the subjective score of the entire image as the quality score label of the image patch, the local quality score of the distorted image generated by the FR-IQA method has an error in itself, and this method must use the reference image.  
The root cause of the patch-based method is that there are too few data sets. Therefore, many researchers have proposed methods of pre-training CNN networks using data sets in other fields. For example, DeepBIQ [28] proposed by [PERSON] et al. In addition to the pre-training method, [PERSON] et al. proposed the RankIQA [29] method based on the idea of ranking learning. Although it is difficult to directly estimate the quality score of a distorted image, it is relatively easy to compare the relative quality of different degrees of distortion. In 2019, the author of the BIECON method proposed the DIQA [15] method. This method is still a two-stage method, but no longer uses the subjective quality score as the regression target, instead, using the objective error map as the intermediate CNN learning target.  
In addition to the aforementioned method of predicting image quality scores, [PERSON] et al. proposed the NIMA [30] method. This method no longer trains the network to predict the quality score of the image but predicts the distribution of human quality scores of the image.  
In short, due to the lack of IQA data sets, which seriously affects the structural design of the CNN network, this paper proposes a two-stage method to solve this problem.