Name
Title | ||
|---|---|---|
Approximate Image Quality Measure In Low-Dimensional Domain Based On Random Projection |
Abstract | ||
|---|---|---|
Image Quality Measure (IQM) is used to automatically measure the degree of image artifacts such as blocking, ringing and blurring effects. It is calculated traditionally in the image spatial domain. In this paper, we present a new method of transforming an image into a low-dimensional domain based on random projection, so we can efficiently obtain the compatible IQM. From the transformed domain, we can calculate the Peak Signal-to-Noise Ratio (PSNR) and apply fuzzy logic to generate a Low-Dimensional Quality Index (LDQI). Experimental results show that the LDQI can approximate the IQM in the image spatial domain. We observe that the LDQI is suited for measuring the compression blur due to its relatively low distortion. The relative error is about 0.15 as the compression blur increases. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1142/S0218001408006235 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
image quality measure, random projection, image compression | Random projection,Computer vision,Feature detection (computer vision),Ringing,Image quality,Artificial intelligence,Image restoration,Distortion,Image compression,Mathematics,Approximation error | Journal |
Volume | Issue | ISSN |
22 | 2 | 0218-0014 |
Citations | PageRank | References |
1 | 0.36 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frank Y. Shih | 1 | 1103 | 89.56 |
| Yan-yu Fu | 2 | 5 | 0.85 |