# Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation
## 2 Related Work  
The quality of an image can be either assessed through subjective tests or by using an automated objective metric [16]. As human eyes are the ultimate receiver of the image, a subjective test is certainly the best and the most reliable way to assess the visual image quality. In such tests, a set of human observers assigns quality scores to the image, which are averaged to get one score. This method, however, is a time-consuming and expensive approach. Therefore, it was felt necessary to introduce an automatic and fast way to assess the quality of an image. This provides the opportunity for researchers to introduce objective metrics for quantitative image quality evaluation, which proves to be a significant improvement in the field of image quality assessment. Objective image and video quality metrics can be grouped into three classes based on the availability of the original reference images: full-reference (FR), no-reference (NR), and reduced-reference (RR) [17]. The IQA metric that requires the original reference image to evaluate the quality of its distorted version is referred to as a full-reference metric. The IQA approach that assesses the quality of an image in the absence of a corresponding reference image is classified as the no-reference metric. The reduced-reference metrics lie between the two categories, they do not require the reference images but some of their features must be available for comparison. In the literature, several 2D and 3D objective quality assessment metrics have been proposed to assess visual image quality. Initially, 2D metrics were used to assess the quality of 3D content, however, the use of conventional 2D metrics was found inappropriate to assess the true quality of 3D images due to several additional factors of 3D videos that were not considered by 2D-IQA algorithms [18; 19; 20]. Therefore, novel IQA algorithms were needed to evaluate the quality of 3D videos. Such algorithms, in addition to 2D-related artifacts, must also consider artifacts introduced due to the additional dimension of depth in the videos. In recent years, several algorithms have been proposed to evaluate the quality of 3D images. Many of them utilize the existing 2D quality metrics for this purpose, e.g., [21; 22; 23; 24]. Since these algorithms rely on metrics especially designed for 2D images, they do not consider the most important factor of 3D images, i.e., depth, and therefore they are not accurate and reliable. Many 3D-IQA techniques consider depth/disparity information while assessing the quality of 3D images, e.g., [25; 26; 27; 28]. [PERSON] et al. [19] adopted a belief-propagation-based method to estimate the disparity and combined the quality maps of distorted image and distorted disparity computed using conventional 2D metrics. The method proposed in [25] exploits the disparity as well as binocular rivalry to determine the quality. It uses the Multi-scale Structural Similarity Index Measure (MSSIM) [29] metric to evaluate the quality of disparity of stereo images. [PERSON] et al. [26] presented a machine-learning-based method that works by learning the features from 2D-IQA metrics and specially designed 3D features using the Scale Invariant Feature Transform (SIFT) flow algorithm [30], and was used to obtain the depth information. The different features of disparity and three types of distortions (blur, noise, and compression) were used by [28] in evaluating the quality of 3D images. These features were used to train a quality prediction model by using the random forest regression algorithm. The method proposed in [18] addressed the issue of structural distortion in a synthesized view due to DIBR, but the method is limited to structural distortions so it cannot be used to evaluate the overall quality of the image. The 3D-IQA method presented in [31] identifies the disocclusion edges in the synthesized image and inversely maps them to the original image, and the corresponding regions are then compared to assess the quality.