# Two Low-Level Feature Distributions Based No Reference Image Quality Assessment
## 1 Introduction  
With the rapid development of information and communication technology, end-users continuously propose higher requirements for high-quality experience [1]. The main target of image quality assessment (IQA) is to design an objective image quality evaluation model, which is consistent with human subjective visual perception. Usually IQA methods include two categories: subjective assessment by humans and objective assessment by algorithms designed. Furthermore, objective IQA indices can be classified as full reference (FR), no-reference (NR), and reduced-reference (RR). Especially, NR IQA methods estimate image quality via computer simulation of human vision system (HVS) only from the distorted image, without any information access to the reference images [2, 3], therefore NR IQA is more meaningful in image processing and applications. Nowadays, deep learning has become one of the most attractive fields in the study of artificial intelligence and machine learning. Among them, the convolutional neural network (CNN) is probably one of the most popular, as a special multilayer perception. With the development of deep learning, CNN has not only produced a large number of variant models, but also made great success in various applications, particularly in tasks involving visual, image, and natural language information [4]. Though deep neural networks are powerful in some certain tasks, they have some apparent deficiencies. First, it is well-known that a large amount of training data are usually required in training. Second, deep neural networks, as a complicated black-box model, theoretically, make it so difficult to analyze the deep structure, and powerful computational facilities are usually required inthe training process. More importantly, the learning performance of deep neural networks depends on careful streamline tuning of huge hyper-parameters, seriously. Recently, a series of deep networks and corresponding combination methods have been proposed to improve training speed and theoretical analysis. In [5], [PERSON] et al. proposed an incremental learning system in the form of flat network without the need for deep architecture, named Broad Learning System (BLS). BLS has been demonstrated to have higher learning accuracy with much faster learning speed. In [6], [PERSON] et al. proposed a multi-grained cascade Forest (gcForest) method. This method generates a deep forest ensemble, with a cascade structure which enables gcForest to do representation learning. The gcForest has much fewer hyper-parameters and quite robust to hyper-parameter settings than deep neural networks. Deep network directly provides the original image into the learning algorithm, there is no need to extract hand-crafted features in advance. But it often depends on a lot of training data and GPU, and spends a lot of time and experience on training parameters and network structure, such as [7,8]. In fact, deep neural networks are not the best choice for some tasks at the certain condition. By contrast, classical machine learning methods require the design of a set of features to describe the input information, such as scale invariant feature transform (SIFT), local binary patterns (LBP), histogram of oriented gradient (HOG), then input these features into the shallow classifiers, which have simple and efficient recognition effect, such as support vector machine (SVM), random forest (RF), logistic regression (LR). Good feature description can effectively improve the performance of pattern recognition system. For images, features can be divided into low-level features and high-level semantic features. Low-level features mainly include: color, texture edge, and shape. High-level semantic features need to identify and interpret objects. How to extract effective features is very important in IQA as well as many other vision tasks. Due to the limitation of computer recognition and image understanding technology, it is impossible to accurately analyze and interpret the semantic features of images.