## 5 Discussion  
### Findings and Their Implications  
The two previous sections have presented the development and experimental results of the proposed no-reference evaluation model in assessing the quality of depth maps used in view rendering via DIBR. This model has exploited the edges existing in the available depth maps and compared those to the edges in the corresponding colour views. Through this comparison, edge pixels in-depth maps have been classified into correct and error edge pixels. The results have been correlated with the subjective results. The result analysis of the model has provided clear indications of depth map performance and its dependency on the associated colour sequence selection. The novelty of this work is realised by the adoption of a proposed depth map quality measure, namely the DEC measure, as an indication of the quality of the view rendered using the particular depth map whose quality has been scrutinised. DEC measure has proved to be a very powerful tool in quantifying the quality of a depth map in rendering the DIBR based view. No-reference approach adopted whilst developing the proposed quality evaluation model is different from the conventional approaches of no-reference quality assessment methods. While conventional methods are blindly assessing the quality of colour views directly, our proposed model provided the quality measure indirectly by assessing the quality of the depth map used in the rendering process. The developed no-reference quality evaluation model can be used to indicate the quality of the depth map used in the rendering process, as it has provided strong correlation with the subjective assessment results of the views rendered using these depth maps, as seen in Figure 10. The developed model produced good results in evaluating the quality of the rendered views, particularly when compared to the results of the traditional 2D full-reference objective quality assessment methods or the conventional no-reference methods reported in the literature. To be specific, the DEC measurement-based model has offered a 27% improvement in correlation with the subjective results when compared to the VQM correlation results when compared to the results shown in Tables 3 and 4. A better performance can also be observed when the results presented in Figures 8 and 10 are compared. This improvement has also been verified by considering the correlation for the range of video sequences tested. VQM has been selected for benchmarking, as it was overall the best performing objective quality assessment method among the state-of-the-art methods employed in this research. It is also clear from the results presented in Table 2 that the conventional approaches to no-reference quality evaluation do not offer good results when the views assessed are the views resulting from the DIBR process. This can be explained by the fact that these methods are designed to assess conventional artefacts and distortions in a natural scene. The artefacts specific to the synthesised DIBR rendered views are of a different nature. Thus, measuring the quality of the depth map used in the rendering process, as advocated by the study presented in this paper, is more representative of the overall quality of the rendered view. Another observation that can be made from the obtained experimental results is related to the effect of using different colour sequences in the rendering process on the resulting quality of the rendered view. It seems it is more beneficial to use colour sequences with high spatio-temporal information to more accurately assess the performance of a set of depth maps in the rendering process. This observation is \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CC** & **SSE** & **RMSE** \\ \hline _Band_ & 0.78979 & 0.166160 & 0.128900 \\ _BMX_ & 0.93044 & 0.022614 & 0.050127 \\ _Musicians_ & 0.80634 & 0.078822 & 0.106110 \\ _Poker_ & 0.73161 & 0.076600 & 0.087522 \\ _Act_ & 0.36877 & 0.112690 & 0.137040 \\ _Parishand_ & 0.16154 & 0.169690 & 0.130260 \\ \hline \hline \end{tabular}
\end{table}