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 study, a general mechanism named Multiple Look-Up Table (MLUT) halftoning is proposed to generate the halftones of Direct Binary Search (DBS), while 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. Moreover, the proposed Quick Standard Deviation Evaluation (QSDE) is employed to yield an extremely low computational complexity in calculating the standard deviation. In the parameter optimization, the autocorrelation is adopted since it can fully characterize the periodicity of dot distribution. Experimental results demonstrate that the visual quality of the proposed method can approximate to that of the DBS which is considered as the best halftoning in terms of image quality, which enable the proposed scheme as a very competitive candidate in coping printing industry. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICIP.2012.6466984 | ICIP |
Keywords | Field | DocType |
printing industry,halftone images,dot distribution,image quality,look-up table (lut),image processing,learned halftone image set,high efficient direct binary search,autocorrelation,halftone,standard deviation,search problems,dbs,computational complexity,multiple look-up table halftoning,parameter optimization,integral image,multiple lookup tables,quick standard deviation evaluation,table lookup,visual quality | Lookup table,Pattern recognition,Computer science,Image processing,Algorithm,Image quality,Halftone,Artificial intelligence,Binary search algorithm,Standard deviation,Computational complexity theory,Autocorrelation | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4673-2532-5 | 978-1-4673-2532-5 | 2 |
PageRank | References | Authors |
0.36 | 3 | 3 |
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 |