Name
Abstract | ||
|---|---|---|
De-interlacing is one of the key technologies in modern dis- plays and multimedia personal computers. Various meth- ods have been proposed including motion compensated (MC) methods and non motion compensated methods. Hybrid methods that combine different de-interlacing techniques are widely used to take advantages from individual algorithms. The combination is normally based on the quality criterion of individual de-interlacing algorithms. In this paper, we pro- pose a classification based data mixing algorithm for hybrid de-interlacing. The algorithm first classifies the interpolated pixels from individual de-interlacing methods and then mix them to give the final output. The optimal mixing coeffi- cients are obtained from an off-line training, which employs the Least Mean Squared (LMS) algorithm. |
Year | Venue | Keywords |
|---|---|---|
2005 | EUSIPCO | image classification,interpolation,least mean squares methods,motion compensation,lms algorithm,mc method,data mixing algorithm,hybrid deinterlacing technique,least mean squared algorithm,motion compensated method,multimedia personal computer,nonmotion compensated method,optimal mixing coefficient |
Field | DocType | ISBN |
Interlacing,Square (algebra),Interpolation,Algorithm,Theoretical computer science,Pixel,Mathematics | Conference | 978-160-4238-21-1 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Zhao | 1 | 2 | 1.17 |
| Ciuhu, C. | 2 | 0 | 0.34 |
| Gerard De Haan | 3 | 243 | 28.98 |