## 2 Background and Related Work
### Related Work on Conventional Approaches in No-Reference Quality Evaluation  
In the past decade, research interest in no-reference quality evaluation has steadily grown with the introduction of a number of approaches in the literature. An approach that utilises natural scene statistics (NSS) is presented in [24] to blindly measure the quality of images compressed by wavelet-based image encoders. This work consists of using a non-linear statistical model that incorporates quantisation distortion modelling (by considering JPEG2000 compression distortions) to develop an algorithm that quantifies the deviation of compressed signals from the expected natural behaviour. This quantification was calibrated against the human judgement of video/image quality.  
In many cases, earlier evaluation methods assumed that the types of video/image distortions were known, such as compression or blurred distortions. A two-step general-purpose framework approach is proposed in [25; 26] for no-reference image quality evaluation based on the NSS model of images. The proposed framework's two stages can be identified as an image distortion classification that is based on the modification of the NSS, which is followed by the selection of a distortion specific algorithm. The framework measures the image quality completely blindly, i.e., without any prior information on the type of source distortions.  
A no-reference quality evaluation technique, which does not consider any specific type of distortions, is proposed in [27], where the image quality is assessed through scene statistics of locally normalised luminance coefficients to quantify the loss of naturalness in the processed image. The evaluation takes place in the spatial domain to identify the amount of loss in image naturalness through features derived from the empirical distribution of locally normalised luminance values.  
A natural image quality evaluator (NIQE) proposed in [28] is described as a completely blind image quality analyser. The NIQE utilises measurable deviations from the statistical regularities observed in natural images without training on human-rated distorted images. This approach is advantageous when distorted images and human assessment of these distortions are not available during the quality evaluation model training and development.  
An image sharpness assessment based no-reference quality evaluation is presented in [29], where the image sharpness is identified as a strong local phase coherence (LPC) near distinctive image features evaluated in the complex wavelet transform domain. This work also presents a further simplification of the LPC computation through an efficient algorithm, rendering the computation as attractive and applicable in practical applications.  
A no-reference quality evaluation approach based on spatial and spectral entropies is proposed in [30]. This approach uses entropy as an effective measure of the amount of information present in an image. It utilises down-sampled responses as inputs, then extracts local entropy feature vectors from the inputs and learns to predict the image quality scores from these features. The complexity of this method was said to be superior to similar approaches with improved quality assessment results.  
No-reference approach adopted whilst developing our proposed quality evaluation model is different from the conventional approaches of no-reference quality assessment methods as introduced above. While those conventional methods are blindly assessing the quality of colour views directly without taking much into account the depth dimension, our proposed model provides the quality measure indirectly by assessing the quality of the depth map used in the rendering process, as presented in the methods section next.