Name
Abstract | ||
|---|---|---|
Nowadays, it is evident that we must consider human perceptual properties to visualize information clearly and efficiently. We may utilize computational models of human visual systems to consider human perception well. Image quality assessment is a challenging task that is traditionally approached by such computational models. Recently, a new assessment methodology based on structural similarity has been proposed. In this paper we select two representative models of each group, the Visible Differences Predictor and the Structural SIMilarity index, for evaluation. We begin with the description of these two approaches and models. We then depict the subjective tests that we have conducted to obtain mean opinion scores. Inputs to these tests included uniformly compressed images and images compressed non-uniformly with regions of interest. Then, we discuss the performance of the two models, and the similarities and differences between the two models. We end with a summary of the important advantages of each approach. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/IV.2004.53 | IV |
Keywords | Field | DocType |
challenging task,image quality assessment,computational model,human visual system,objective image,quality assessment,visible differences,human perception,important advantage,human perceptual property,principal approaches,new assessment methodology,structural similarity index,visualization,computer science,structural similarity,testing,utility computing,data visualisation,mean opinion score,information visualization,computational modeling,image compression,region of interest,data compression,indexation,predictive models,computer model,image quality,visual system | Data mining,Data visualization,Information visualization,Visualization,Computer science,Image quality,Computational model,Data compression,Perception,Image compression | Conference |
ISSN | ISBN | Citations |
1093-9547 | 0-7695-2177-0 | 6 |
PageRank | References | Authors |
0.70 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Cadik | 1 | 56 | 4.31 |
| Pavel Slavik | 2 | 69 | 10.38 |