## 4 Results
### Performance of the Conventional Objective Quality Evaluation Methods  
It is possible to objectively assess the quality of a rendered view against the available original colour view at the same location with the camera setup depicted in Figure 1. The 2D objective quality evaluation methods used for analysis in this section are: Peak signal to noise ratio (PSNR) [44], peak signal to perceptual noise ratio (PSNR) [45], structural similarity index (SSIM) [46] and video quality metric (VQM) [47]. To better understand the performance of these methods in assessing the quality of the rendered views, a regression approximation using a symmetrical logistic function (as detailed in [39]) has been performed. The regression analysis utilises the 2D video subjective assessment results obtained in the previous section (Section 4.1) and 2D objective measurements for the stimuli related to those subjective assessments. The aim of this analysis is to attain a correlation to estimate the relation between the objective measurements and recorded subjective assessment results. The results of the regression analysis are depicted in Figure 8, where the obtained CC values together with the sum of square error (SSE) and RMSE statistics for the regression operation, are presented. Note that the figure presents the regression results for PSPNR and VQM only, as the regression analysis for both PSNR and SSIM offered insignificant CC values, and thus, have been omitted. PSPNR and VQM offer partial correlation with the subjective results to some extent, as they are used for video quality evaluation rather than image quality measurements. Although PSPNR and VQM results offer some degree of a match for the quality of the assessed rendered views, the correlation levels obtained are very limited. This can be explained by the fact that the traditional objective quality evaluation methods are designed to be effective in measuring specific distortions and artefacts, such as compression noise, random noise and blurriness. The artefacts present in the synthesised rendered views are different from those and can vary depending on the quality of the depth map used in the rendering process. As such, the traditional methods are ineffective at quantifying the unconventional artefacts that may appear in the synthesised views resulting from the DIBR process. To get a full insight into the abovementioned phenomenon, Table 1 presents the subjective and objective measurements recorded for two rendered stimuli: Views rendered from the _Musicians_ colour sequence utilising the RSGM_GAUS and RSGM_SCAN depth maps. The RSGM_GAUS depth map has been obtained by applying Gaussian blur on the RSGM_FIL maps to study the effects of non-sharp edges in a depth/disparity map on the resulting rendered sequences. An RSGM_SCAN depth map has been obtained by introducing high edge errors in the RSGM_FIL depth maps using a scan-line error code. The recorded 2D MOS values indicate a clear difference between the subjective qualities of the two stimuli. The stimuli rendered utilising the RSGM_GAUS depth map has a much higher MOS than that recorded for the stimuli rendered utilising the RSGM_SCAN depth map. On the contrary, the 2D objective measurements recorded for both stimuli indicate the complete opposite, i.e., the objective measurements obtained by all four objective quality evaluation methods state that the RSGM_SCAN based depth map rendered view is objectively better than the RSGM_GAUS based depth map rendered view. It is worth noting that for VQM, the lower the value recorded the better the quality of the measured video. The opposite is true for the remainder of the utilised objective assessment methods. Figure 9 shows a visual comparison between the two stimuli. These results present testimony for not relying on the conventional objective assessment methods when it comes to assessing the rendered view qualities in immersive video applications, particularly when a variety of depth map qualities are involved in view rendering in complete no-reference scenarios.