Full Reference Image Quality Evaluation Based On Spatial Distribution And Statistical Learning

Internet technology and media are popular in this era.We have obtained all kinds of information from the texts,pictures and video resources through the Internet,which have become an indispensable channel to open our eyes.Consequently,the quality of these resources is closely related to the effect of the information delivered,such as the accuracy of text,image fidelity and video resolution.Image is the entry point for this paper.Three methods to measure the degree of distortion of images with reference images are observed,which make the evaluation results as consistent as possible with the subjective evaluation of human eyes.The followings are the three image quality assessment models: the first one is based on the spatial distribution of the image which focuses on five transformation methods of pixel point matrix,namely most similar differences(MSDI),most dissimilar differences(MDD),most similar neighbour differences(MSND),most dissimilar neighbor differences(MDND)and average neighbor differences(AVND),and compares the differences between the original image and transformed image by KS-test.The second model is based on sparse representation.The image matrix is decomposed into dictionary matrix and coefficient matrix by the K-SVD algorithm,and the coefficient matrix is used as the image feature to calculate the difference between the two image features.The third one is based on the deep convolutional neural network.The neural network model is used to extract image features and compares the differences between the two images,so as to obtain the quality score of the target image.The last two models make use of the idea of matrix transformation of the first model: first,transform the image matrix,and then carry out the corresponding calculations.Research objects of this paper are five types of distorted images in the LIVE image database,namely JP2 K,JPEG,WN,GBlur and Fastfading.Experiments show that the image quality scores of each type of distorted image observed by the proposed method can maintain high similarity with the subjective score of human eyes.In addition,the effects of the methods based on sparse representation and neural network have been dramatically improved,which also show good accuracy compared with the existing objective quality assessment methods.