Name
Abstract | ||
|---|---|---|
Look-up table (LUT) halftoning is an efficient way to construct halftone images and approximately simulate the dot distribution of the learned halftone image set. In this paper, a general mechanism named multiple look-up table (MLUT) halftoning is proposed to generate the halftones of direct binary search (DBS), whereas the high efficient characteristic of the LUT is still preserved. In the MLUT, the standard deviation is adopted as an important feature to classify various tables. In addition, the proposed quick standard deviation evaluation is employed to yield an extremely low computational complexity in calculating the standard deviation. In the parameter optimization, the autocorrelation is adopted because it can fully characterize the periodicity of dot distribution. Experimental results demonstrate that the dot distribution generated by the proposed method approximates to that of the DBS, which enables the proposed scheme as a very competitive candidate in the copying and printing industry. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TIP.2013.2277774 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
image processing | Lookup table,Computer science,Error diffusion,Image processing,Halftone,Artificial intelligence,Binary search algorithm,Autocorrelation,Computer vision,Pattern recognition,Algorithm,Standard deviation,Computational complexity theory | Journal |
Volume | Issue | ISSN |
22 | 11 | 1941-0042 |
Citations | PageRank | References |
2 | 0.38 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing-Ming Guo | 1 | 830 | 77.60 |
| Yun-Fu Liu | 2 | 277 | 19.65 |
| Jia-Yu Chang | 3 | 6 | 1.45 |
| Jiann-Der Lee | 4 | 211 | 34.02 |