## 7 Conclusions and Further Work  
Presented results confirm the usefulness of the combined IQA metrics for the quality evaluation of the 3D printed surfaces, leading to a high correlation with subjective opinions of the surface aesthetics. The proposed approach to automatic objective quality evaluation of the 3D printed surfaces is based only on the full-reference image quality metrics that rely on the mutual similarity, hence its application is relatively easy since the values of the elementary metrics necessary for the combination may be calculated in parallel. The utilized individual metrics may be effectively calculated [44,49] and therefore \begin{table}
\begin{tabular}{c l c c c} \hline \hline
**Number of Regions** & **Metrics** & **PLCC** & **SROCC** & **KROCC** \\ \hline \multirow{4}{*}{4} & FSIM + CW-SSIM + MCSD + ESSIM & 0.8405 & 0.8431 & 0.6639 \\  & FSIM + CW-SSIM + MCSD + GSM & 0.8474 & 0.8485 & 0.6710 \\  & FSIM + CW-SSIM + MCSD + ESSIM + GSM & 0.8565 & 0.8586 & 0.6826 \\ \hline \multirow{4}{*}{9} & FSIM + CW-SSIM + MCSD + ESSIM & 0.8463 & 0.8357 & 0.6558 \\  & FSIM + CW-SSIM + MCSD + GSM & 0.8487 & 0.8385 & 0.6565 \\  & FSIM + CW-SSIM + MCSD + ESSIM + GSM & 0.8495 & 0.8397 & 0.6604 \\ \hline \multirow{4}{*}{16} & FSIM + CW-SSIM + MCSD + ESSIM & 0.8502 & 0.8453 & 0.6660 \\  & FSIM + CW-SSIM + MCSD + GSM & 0.8514 & 0.8443 & 0.6653 \\ \cline{1-1}  & FSIM + CW-SSIM + MCSD + ESSIM + GSM & 0.8537 & 0.8480 & 0.6748 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The correlation coefficients obtained for 107 images using the “best” combinations of four and five elementary metrics assuming the division into 4, 9, and 16 regions. the presented approach may be successfully implemented for the real-time quality monitoring during the manufacturing, understood as not introducing noticeable delays in comparison to the relatively slow printing process. An important remark in view of the computation time is the possibility of an increase in the correlation with MOS values using more metrics but decreasing the number of regions. Application of the proposed combined metrics makes it possible to increase the correlation with the subjective quality evaluation of the 3D printed surfaces by over 0.17 in comparison to the use of the best single metric, considering the prediction accuracy. A similar increase may be observed for SROCC with the increase of KROCC by more than 0.15, providing significantly higher prediction monotonicity. As the proposed method is color independent, the use of a monochrome camera is enough, although the application of some other color to grayscale conversion methods as well as various color spaces is planned in further research. Another direction of future work is the extension of the proposed method towards the use of some other image quality assessment methods and integration with previously developed methods used so far for classification purposes. Nevertheless, the development of even better-correlated metrics requires additional experiments, as well as the possible extension of the developed dataset using also some additional samples with non-planar surfaces (with the time-consuming acquisition of subjective quality scores for the new samples). Such an extension of the proposed method for the evaluation of non-planar surfaces is possible similarly as for previously investigated entropy-based approaches [4]. Obviously, reasonable results may be expected only for the division into relatively small regions, additionally avoiding the mutual similarity calculations for distant image fragments due to expected different orientation of both compared to local patterns.