## 3 Results  
In the experiment, the effectiveness of our method was verified using the LIVE S3D [5] dataset, which includes 365 symmetrically distorted S3D images (in the future, we plan to understand how asymmetrical distortion affects perceptual quality (for the case of blur artifacts, the perceptual quality of asymmetric blurred S3D images is mainly dominated by the view that contains more information. For the case of JPEG artifacts, the binocular perceptual quality has a tendency towards the lower quality view) and extension of the proposed method to facilitate asymmetrical distortion S3D-IQM). In order to facilitate the experiment (refer to [16]), we utilized only four distortion types: JP2K, WN, JPEG, and Gblur. The images in the dataset were assigned difference mean opinion scores (DMOS), which represent subjective judgments. In order to compare the proposed method against existing S3D-IQM methods, two widely used performance criteria were calculated by applying the DMOS and the predicted quality scores. The first was a [PERSON]'s linear correlation coefficient (PLCC), which was computed to evaluate the prediction accuracy. The second was a [PERSON]'s rank order correlation coefficient (SROCC), which measured the prediction monotonicity. For an ideal IQM method, values close to 1 were needed for SROCC and PLCC. To demonstrate the robustness and effectiveness of the proposed method, we compared it with several existing representative IQM methods, including three 2D-IQM methods--SSIM (full reference, FR) [26], IL_NIQE (blind, not depending on DMOSs) [15], and [PERSON]'s method (blind, not depending on DMOSs) [16]--and seven S3D-IQM methods--[PERSON]'s method (FR) [2], [PERSON]'s method (blind, depending on DMOSs) [4], [PERSON]'s method (blind, depending on DMOSs) [5], [PERSON]'s method (blind, depending on DMOSs) [9], [PERSON]'s method (blind, depending on DMOSs) [12], [PERSON]'s method (blind, not depending on DMOSs) [13], and [PERSON]'s method (blind, not depending on DMOSs) [14]. All IQM methods are based on the grayscales of S3D images. The SROCC and PLCC results are listed in Table 1; the criterion that provides the best evaluation performance is emphasized in boldface (all the blind methods that do not depend on DMOSs). As shown in Table 1, compared with other methods, it is evident that our method correlates well with the DMOSs, owing to its sufficient application of human visual perception. It thereby delivers a performance that is superior to most blind and FR methods, including SSIM and IL_NIQE, and the methods proposed by [PERSON], [PERSON], [PERSON], and [PERSON]. Thus, our method is an accurate, effective, and consistent S3D-IQM method, even though it does not use human-scored S3D images. Another experiment was conducted to further investigate the evaluation ability of the IQM methods for specific distortion types. In this experiment, we tested the performance of the state-of-the-art methods on individual distortion types. The SROCC and PLCC results are listed in Table 2. For each individual distortion type, the top two results are emphasized in boldface (all blind methods that do not depend on DMOS). As shown in Table 2, our method placed among the top methods most often. Although some methods may provide good performance for some distortion types, the proposed method performs better than, or on par with, the best method for each individual distortion type. Next, we discuss the influence of disparity estimation performance on the proposed method. The performance of the proposed method using ground truth, SAD [27], and Klaus [21] are listed in Table 3. Table 3 shows that a similar performance is attained when using either the ground truth or the estimated disparity.