Objective assessment method of image quality based on visual perception of image content

Abstract

Objective image quality assessment (IQA) plays a very important role in transmission, encoding, and quality of service (QoS) of the image and video data. However, the existing IQA methods often do not consider image content features and their visual perception, so there is a certain gap between the objective IQA sores and the subjective perception. To solve this problem, in the study, we propose an objective IQA method based on the visual perception of image content, which combines the complexity characteristics of image content, and the properties of masking, contrast sensitivity and luminance perception nonlinearity of human visual system (HVS). In the proposed method, the image is first transformed using a nonlinear model of luminance perception to obtain the intensity perception image. Then, the intensity information is summed using the contrast sensitivity values of HVS and the average contrast values of the local image as a weighting factor of the intensity. The summed data information is taken as the content of human perceiving image, and an image perception model is constructed. Finally, the reference images and distorted images are perceived by simulating the HVS with this model. Moreover, the difference in intensity between two perceived images is calculated. Based on the intensity difference and peak signal-to -noise ratio model, an objective IQA model is constructed. Further, the simulation with 47 reference images and 1549 test images in the LIVE, TID2008, and CSIQ databases is conducted. Moreover, the experimental results are compared with those of four typical objective IQA models, namely SSIM, VSNR, FSIM, and PSNRHVS. In addition, we explore the factors that affect the IQA accuracy and a way to improve assessment accuracy by combining HVS characteristics, through analyzing the correlation between IQA results of the proposed model and the subjective mean opinion scores (MOSs) provided in the three image databases from the following two aspects. Namely, (1) all reference images in three image databases are distorted by multiple types, and the distorted images of each reference image are taken as a test sequence. Then, the proposed model is used to evaluate each test sequence to obtain the IQA scores. By analyzing the correlation between the IQA scores of each test sequence and the subjective MOSs and comparing them with the assessment results of SSIM, we explore the influence of the image content complexity on the objective IQA accuracy. (2) The test images which are distorted by each type and many distortion degrees are used as another sequence, and they are evaluated by the proposed IQA model. By analyzing the correlation between the subjective MOSs and the IQA results of each test sequence, and comparing them with assessment results of SSIM, we discuss the influence of image distortion mode on the IQA accuracy. The experimental results show that the coefficient values of Pearson linear correlation and Spearman rank order correlation between the objective IQA scores obtained by the proposed method and the subjective MOSs have been averagely improved by 9.5402% and 3.2852%, respectively, in comparison with IQA results from the SSIM method. Also, they are enhanced more significantly than those fom the PSNRHVS and VSNR methods. In summary, it is shown that the proposed IQA method is an effective and feasible method of objectively assessing the image quality; moreover, it is shown that in the objective assessment of image quality it is very helpful to improve the consistency of subjective and objective assessment of image quality by considering the content perception and complexity analysis of the images.