Name
Abstract | ||
|---|---|---|
To evaluate the quality of images, most methods compare a degraded image to a perfect reference. Nevertheless in many cases, a reference does not exist. We propose an original univariant (i.e., without a reference) method based on the use of artificial neural networks. The principle behind it is to first teach a neural network to assess image quality using images taken from a pool of known examples, then use it to assess the quality of unknown images. The defects considered are compression artifacts, ringing, local singularities, etc. To simplify, only images with defects that are not mixed with one another were first used. Two illustrative examples are presented: assessment of the quality of JPEG compressed images and detection of local defects. The quality of the images is assessed without a reference and with error less than 6%-7% compared to the bivariant method that was learned. Our method can even be used to model some very simple visual comportment. (C) 2002 SPIE and IST. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1117/1.1482096 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
artificial neural networks,neural networks,neural network,image quality,artificial neural network | Computer vision,Quality measurement,Pattern recognition,Compression artifact,Ringing,Computer science,Image quality,JPEG,Artificial intelligence,Artificial neural network | Journal |
Volume | Issue | ISSN |
11 | 3 | 1017-9909 |
Citations | PageRank | References |
11 | 2.11 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mathieu Jung | 1 | 11 | 2.11 |
| Dominique Léger | 2 | 11 | 2.11 |
| Marc Gazalet | 3 | 68 | 16.44 |