Objective

2

Although 3D technology is increasingly used in film and television

the development of 3D display technology has been stagnant in recent years. The main reason is that image degradation occurs in the process of 3D information transmission

along with the decline of the depth perception of stereo images to a certain extent. These conditions affect users' immersion experience. Furthermore

the discomfort caused by 3D display limits the development of 3D technology. On the one hand

an effective stereo image quality evaluation technology can provide new ideas for image compression standards. On the other hand

the technology provides a reference for the rational improvement of the quality of 3D videos

thereby accelerating the development of 3D multimedia application technology. Providing content that conforms to users' viewing experience is of paramount importance for the further promotion of 3D multimedia technology. The stereoscopic image quality evaluation method that conforms to users' visual characteristics helps to accurately and objectively reflect the visual perception experience when users watch 3D images or videos. On the basis of different dimensions that affect the quality of stereoscopic image experience

four categories of image quality assessment algorithms are used:image quality evaluation based on distortion

quality of experience based on depth perception

quality of experience based on comfort

and comprehensive dimensions. The quality of experience (QoE) represents the quality of the stereoscopic visual experience of users. QoE is an objective result that takes users as the core and considers the multi-dimensional perception factors that comprehensively affect it. The stereo image quality is the result of the three perceptions of distortion

depth

and comfort of 3D images. Distortion quality indicates the degree of image degradation caused by image distortion. Depth quality indicates the depth and immersion feeling experienced when viewing 3D content. Visual comfort indicates the degree of visual fatigue experienced when viewing stereoscopic images. The existing research on the objective evaluation of 3D QoE only evaluates results beginning from one or two dimensions of image distortion

stereoscopic perception

and visual comfort. However

in an actual subjective experiment

we found that any change in dimensions leads to changes in the quality of the stereo image experience and that existing methods do not comprehensively consider the three factors of image distortion

depth perception

and comfort. To evaluate the visual perception experience of 3D images comprehensively and accurately

this study proposes a stereoscopic image experience quality evaluation method that is based on users multi-dimensional perception.

Method

2

A distortion-free natural scene image has a certain regularity in distribution

and image distortion causes its distribution law to change; thus

image quality can be estimated from the extracted feature parameters. The left and right eye images are subtracted and added to obtain the difference image and fused image. Then

the difference image and fused image are fitted by the generalized Gaussian distribution function

and the fitting parameters are obtained as the distortion quality features. Distortion reduces the depth perception quality of stereo images. It exerts two main effects on depth perception. First

the relative depth information between objects is lost

and the position of the object consequently becomes blurred

thereby affecting depth perception. Second

distortion reduces the feature points at which the left and right viewpoint images are matched; thus

the binocular depth perception information is reduced

thereby diminishing the sense of depth. Then

the distortion-sensitive pixel map is obtained. For each distortion-sensitive pixel

the neighborhood brightness distribution is calculated. SIFT (scale invariant feature transform) key point matching is performed on the left and right views. The statistical result of the neighborhood brightness distribution and the key point matching quantity are used as the depth quality feature. When the parallax of the stereoscopic image exceeds a certain range

the human eye may generate a convergence conflict

thereby resulting in visual fatigue. The human eye is only sensitive to the comfort/discomfort characteristics of the significant area. Thus

we adopt the comfort evaluation model based on the visual important regions and extract the mean parallax value of the significant area. Finally

the three-dimensional features are combined as the experience quality feature vector

and the objective prediction model is constructed by support vector regression.

Result

2

Experimental results on the LIVE database and Waterloo IVC database show that the proposed method correlates with people's subjective perception at values of 0.942 and 0.858

which are better than those of other methods.

Conclusion

2

The method fully uses the characteristics of the stereo image

and the evaluation result is better than that of several classical algorithms. Therefore

the evaluation result of the constructed model shows improved consistency with the subjective experience of the user. In the future

we will combine the evaluation process with the stereo image quality optimization process and guide quality optimization to stereo images from various dimensions.