# Two Low-Level Feature Distributions Based No Reference Image Quality Assessment
## 2 Related Work  
The NR IQA method based on low-level features and the methods closely related to our work are introduced in this section. The LBP feature proposed by [PERSON] et al., in [9, 10, 11] is an effective texture local description feature, which encodes the relative intensity values between the central pixel and surrounding pixels, and has been successfully applied in texture classification, face recognition image retrieval, and other fields. Compared with other local texture descriptors, LBP can capture the local texture features simply and quickly. Only in the past decade LBP has been widely used in NR IQA, and become an active research topic. A novel NR IQA method based on structure and luminance information was proposed in [12], which is obtained by extracting LBP to reflect the structural features of the distorted image and extracting the distribution of normalized luminance values to express the brightness characteristics. In [13], [PERSON] et al. used the LBP operator to extract the structural information from the gradient map and the contrast normalization graph respectively. In [14], [PERSON] et al. proposed a fuzzy NR IQA based on the LBP histogram feature. In [15], [PERSON] et al. used a Gaussian Laplacian filter to decompose the image into multi-scale sub-images, and explored the weighted LBP histogram as the quality-aware feature to input into the support vector regression (SVR) system for obtaining the quality score. LBP has been extensively and deeply studied in many fields, but the method of feature extraction is sensitive to image noise, and the recognition effect is susceptible to the environment, which makes the local structure description limited. This still needs to be discussed in theory and algorithm. [PERSON] et al. [16] proposed structural similarity (SSIM) method, which is a milestone in the field of IQA research. The core idea of SSIM is that the change of the image local structure can effectively reflect the degradation of image quality. The disadvantage of SSIM for severely distorted or unstructured distorted images is inconsistent with subjective evaluation especially. For this problem, [PERSON] et al. proposed the feature similarity (FSIM) index by using two low-level features of phase consistency and gradient magnitude to replace the statistical features in SSIM [17]. [PERSON] et al. [18] implemented a gradient similarity method by combining gradient features and pixel difference features, which emphasized the gradient can effectively capture structural and contrast changes. In [19], [PERSON] et al. proposed the gradient magnitude similarity deviation (GMSD) method by using the gradient as the feature and the standard deviation instead of the previous mean as the pooling. As it is well-known that the image gradient is sensitive to image distortion and can well characterize the degree of quality degradation of different local structures in the distorted image. [PERSON] et al. [20] considered that gradient direction feature plays an important role in image quality evaluation. They used relative gradient direction and relative gradient amplitude feature to evaluate image quality by using AdaBoosting back propagation neural network. In [12; 13; 14; 15], the authors used LBP for feature extraction and quality evaluation. LBP has good local description, but poor global performance. Since the gradient can reflect the overall structure information of the image, Refs. [18; 19] both used different forms of gradient information for quality evaluation. Inspired by the above methods, we propose an NR IQA method based on two low-level feature distributions (TLLFD). The model extracts two types of complementary low-level feature distributions, which not only enhances computational efficiency, but also improves the description and discrimination of the image.