Abstract
In this study, the authors describe an objective smoothness assessment method for volume data. The metric can predict the extent of the difference in smoothness between a reference model, which may not be of perfect quality, and a distorted version. The proposed metric is based on the wavelet characterisation of Besov function spaces. The comparison of Besov norms between two models can resolve the global and local differences in smoothness between them. Experimental results from volume datasets with smoothing and sharpening operations demonstrate its effectiveness. By comparing direct volume rendered images, the experimental results show that the proposed smoothness index correlates well with human perceived vision. Finally, the metric can help the analyse compression distortions when they compare volume data with different smoothness.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
