# Quality Assessment of 2.5D Prints Using 2D Image Quality Metrics
## 5 Conclusions and Future Works  
The QA of 2.5D prints is currently attracting the attention of researchers, but objective QA of 2.5D prints is less studied. Therefore, an attempt to test existing 2D IQMs to predict the quality of 2.5D prints captured by the multiple-shot and single-shot set-ups was made. We acquired the following observations:  
* iCID's lightness difference, lightness contrast, and lightness structure maps, CIEDE2000, and SCIELAB can find differences between 2.5D prints as well as between 2.5D prints and their 2D reference images in both set-up captures;
* More differences are detected mostly in the multiple-shot set-up captures than in the single-shot set-up ones and the captured differences are clearer visible in the multiple-shot set-up captures by iCID's lightness difference, lightness contrast, and lightness structure maps, CIEDE2000, and SCIELAB based on their (mean) quality maps;
* To create a metric for 2.5D prints' perceptual QA, it important to have a model of the HVS.  
In conclusion, iCID's lightness difference, lightness contrast, and lightness structure maps, CIEDE2000, and SCIELAB were found to be relevant to use to detect differences on the edges, on the surface of the elevated parts, and on the background between 2.5D prints and their 2D reference images as well as between 2.5D prints, especially in the multiple-shot set-up captures. Our results on the responsiveness of the selected 2D IQMs on the quality variations of 2.5D prints can be useful in, for example a quality assurance application, where the industry is interested to detect defects or any other differences between prints (or with respect to the reference image/print) without necessarily defining which prints' quality is the best. We consider testing more IQMs and to apply the CSF in the preprocessing stage before applying the IQMs to account for the distance from the prints to the observer as a future work.  
Conceptualization, A.K., V.K., M.P.; methodology, A.K., V.K., M.P.; data acquisition and processing, A.K., V.K.; analysis, A.K., V.K., M.P.; writing--original draft, A.K.; writing--review and editing, A.K., V.K., M.P.; visualization, A.K.; supervision, M.P., V.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the ApPEARS project from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 814158.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Dataset used in this work can be found at [[https://zenodo.org/record/4609031#.YRTIMt8](https://zenodo.org/record/4609031#.YRTIMt8) RVPY]([https://zenodo.org/record/4609031#.YRTIMt8](https://zenodo.org/record/4609031#.YRTIMt8) RVPY), accessed on 15 March 2021.  
**Acknowledgments:** We would like to thank [PERSON] and [PERSON] for fruitful discussions.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyzes, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.