Please use this identifier to cite or link to this item:
|Title: ||Wavelets-Based Smoothness Comparisons for Volume Data|
|Authors: ||Mong-Shu Lee|
|Keywords: ||Controlled Indexing|
|Issue Date: ||2017-01-16T01:37:36Z
|Publisher: ||IET Image Processing|
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.
|Appears in Collections:||[食品科學系] 期刊論文|
Files in This Item:
All items in NTOUR are protected by copyright, with all rights reserved.