# Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation
## 2 Related Work
The algorithm in [32] uses feature matching points in the synthesized and reference images to compute the quality degradation. The JustNotice Difference (JND) model is exploited in [33] to compute the global sharpness and distortion in holes in the DIBR image to assess its quality. The quality metric proposed in [34] identifies the critical blocks in the DIBR synthesized image and the reference image. The texture and color contrast similarities between these blocks are compared to estimate the quality of the synthesized image. The method in [35] works by extracting the features of energy-weighted spatial and temporal information and entropy. Then, support vector regression uses these features for depth estimation. [PERSON] et al. proposed a stereo-band-limited contrast method in [36] that considers contrast sensitivity and luminance changes as important factors for the assessment of image quality. The method presented in [37] extracts the natural scene features from a discrete cosine transform (DCT) domain, and a deep belief network (DBN) model was trained to get the deep features. These generated deep features and DMOS values were used to train a support vector regression (SVR) model to predict the image quality. The learning framework proposed in [38] also uses a regression model to learn the features and besides assessing the quality, it also improves the quality of stereo images. The method proposed in [39] considers the global visual characteristics by using structural similarities and the local quality was evaluated by computing the local magnitude and local phase. The global and local quality scores were combined to get the final score. Binocular perception or binocular rivalry is an important factor in 3D image quality assessment [40,41]. Humans perceive images with both eyes and it is obvious that there is a difference between the perceptions of the left and the right eye in relation to an image. Indeed, binocular rivalry is the visual perception phenomenon in which there exists a difference in the perception of an image when it is seen from the left eye and the right eye. This difference is called the binocular parallax or binocular disparity. The binocular disparity can be divided into horizontal and vertical parallax. The horizontal parallax affects depth perception and the vertical parallax affects visual comfort [37]. This binocular perception was taken into account in [42] and a binocular fusion process was proposed for quality assessment of stereoscopic images. The 3D-IQA metric proposed in [41] is also based on binocular visual characteristics. A learning-based metric [43] uses binocular receptive field properties for assessing the quality of stereo images. [PERSON] et al. [44] proposed a metric that simplifies the process of binocular quality prediction by dividing the problem into monocular feature encoding and binocular feature combination. [PERSON] et al. combine binocular integration behaviors such as binocular combination and binocular frequency integration with conventional 2D metrics in [45] to evaluate the quality of stereo images. Binocular spatial sensitivity influenced by binocular fusion and binocular rivalry properties was taken into consideration in [46]. The method proposed in [47] uses binocular responses, e.g., binocular energy response (BER), binocular rivalry response (BRR), and local structure distribution, for 3D-IQA. Quality assessment of asymmetrically distorted stereoscopic images was targeted in [48]. The method is inspired by binocular rivalry and it uses estimated disparity and Gabor filter responses to create an intermediate synthesized view whose quality is estimated using 2D-IQA algorithms. A multi-scale model using binocular rivalry is presented in [49] for quality assessment of 3D images. Numerous other 3D-IQA algorithms use binocular cues for evaluating the quality of 3D images, e.g., [50, 51, 52].