Name
Abstract | ||
|---|---|---|
Bit-depth expansion is an art of converting low bit-depth image into high bit-depth image. Bit-depth of an image represents the number of bits required to represent an intensity value of the image. Bit-depth expansion is an important field since it directly affects the display quality. In this paper, we propose a novel method for bit-depth expansion which uses Minimum Risk Based Classification to create high bit-depth image. Blurring and other annoying artifacts are lowered in this method. Our method gives better objective (PSNR) and superior visual quality as compared to recently developed bit-depth expansion algorithms. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/VCIP.2012.6410837 | Visual Communications and Image Processing |
Keywords | Field | DocType |
image classification,image representation,image restoration,bit-depth expansion algorithm,blurring,display quality,high-bit-depth image,image representation,low-bit-depth image,minimum risk-based classification,visual quality,Bit-Depth expansion,Minimum risk based classification,Posterior probability,Prediction,Risk calculation | Feature detection (computer vision),Computer science,Image representation,Image quality,Theoretical computer science,Artificial intelligence,Image restoration,Contextual image classification,Computer vision,Image warping,Pattern recognition,Image texture,Color depth | Conference |
ISBN | Citations | PageRank |
978-1-4673-4406-7 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gaurav Mittal | 1 | 160 | 21.03 |
| Vinit Jakhetiya | 2 | 102 | 12.89 |
| Sunil Prasad Jaiswal | 3 | 42 | 7.94 |
| Oscar C. Au | 4 | 1592 | 176.54 |
| Anil Kumar Tiwari | 5 | 65 | 17.51 |
| Wei Dai | 6 | 18 | 3.46 |