Novel Stacking and Boundary Enhanced Hybrid Halftone Screen for Laser Printers
Authors: 
Jing-Ming Guo, Sankarasrinivasan Seshathiri, Yun-Fu Liu, Shih-Chieh LinAuthors Info & Claims
Pages 327 - 337
Abstract
Digital halftoning is a common technique used in printing to produce high-quality printouts, but current methods based on iterative and dual-mode techniques have limitations in terms of quality. These methods often create artifacts in transition regions between smooth and edge regions, and struggle to produce high-quality results for textured or finely detailed images. As a solution, this study proposes a new halftoning technique that produces enhanced clustered-dot patterns suitable for laser printers. The proposed method incorporates a new stacking constraint approach to address boundary issues between low and high-frequency regions, as well as an adaptive variance-based halftone scheme to improve overall image quality. Comprehensive experiments demonstrate that this new technique outperforms existing methods and is an excellent choice for versatile printing applications that rely on laser printers.
References
[1]
N. Kuwata, Y. Murayama, and S. Ilno, “A new bi-level quantizing method for document images,” IEEE Trans. Consum. Electron., vol. 38, no. 3, pp. 718–724, Aug. 1992.
[2]
D. L. Lau and G. R. Arce, Modern Digital Halftoning, 2nd ed. Boca Raton, FL, USA: CRC Press, 2008.
[3]
D. Kacker, T. Camis, and J. P. Allebach, “Electrophotographic process embedded in direct binary search,” IEEE Trans. Image Process., vol. 11, no. 3, pp. 234–257, Mar. 2002.
[4]
V. Ostromoukhov and S. Nehab, “Halftoning with gradient-based selection of dither matrices,” U.S. Patent 5 701 366, 1997.
[5]
J. Huang and A. Bhattacharjya, “An adaptive halftone algorithm for composite documents,” in Proc. Color Imag. Process. Hardcopy Appl. IX, vol. 5293, 2004, pp. 425–433.
[6]
C. Y. Su and Y. L. Sie, “An FPGA implementation of chaotic and edge enhanced error diffusion,” IEEE Trans. Consum. Electron., vol. 56, no. 3, pp. 1755–1762, Aug. 2010.
[7]
S. Daly and X. Feng, “Methods and systems for adaptive dither structures,” U.S. Patent 7 098 927, 2004.
[8]
Y. F. Liu, et al., “Multi-mode halftoning using stochastic clustered-dot screen,” in Proc. Int. Conf. Front. Comput., 2017, pp. 423–431.
[9]
S. J. Parket al., “Halftone blending between smooth and detail screens to improve print quality with electrophotographic printers,” IEEE Trans. Image Process., vol. 25, no. 2, pp. 601–614, Feb. 2016.
[10]
J. Liuet al., “New results for aperiodic, clustered-dot halftoning,” in Proc. IS & T Int. Symp. Electron. Imag. Sci. Technol., 2020, p. 195.
[11]
T. Liu, “Probabilistic error diffusion for image enhancement,” IEEE Trans. Consum. Electron., vol. 53, no. 2, pp. 528–534, May 2007.
[12]
Q. Lin, “Adaptive halftoning based on image content,” U.S Patent 5 970 178,1999.
[13]
K. Kritayakirana, D. Tretter, and Q. Lin, “Adaptive halftoning method and apparatus,” U.S Patent 6 760 126, 2000.
[14]
H. Z. Hel-Or, X. Zhang, and B. A. Wandell, “Adaptive cluster dot dithering,” J. Electron. Imag., vol. 8, pp. 133–144, Apr. 1999.
[15]
Y. F. Liu and J. M. Guo, “Clustered-dot screen design for digital multitoning,” IEEE Trans. Image Process., vol. 25, no. 7, pp. 2971–2982, Jul. 2016.
[16]
P. Goyal, M. Gupta, C. Staelin, M. Fischer, O. Shacham, and J. P. Allebach, “Clustered-dot halftoning with direct binary search,” IEEE Trans. Image Process., vol. 22, no. 2, pp. 483–487, Feb. 2013.
[17]
“Database of uncompressed colour image.” Accessed: Jan. 20, 2023. [Online]. Available: https://homepages.lboro.ac.uk/~cogs/datasets/ucid/ucid.html
[18]
J. M. Guo and S. Sankarasrinivasan, “Visually encrypted watermarking for ordered-dithered clustered-dot halftones,” IEEE Trans. Circuits Syst. Video Technol., vol. 33, no. 8, pp. 4375–4387, Aug. 2023.
[19]
A. Khmag, A. R. Ramli, and N. Kamarudin. “Clustering-based natural image denoising using dictionary learning approach in wavelet domain,” Soft Comput., vol. 23, no. 17, pp. 8013–8027, 2019.
[20]
A. Khmag, “Additive Gaussian noise removal based on generative adversarial network model and semi-soft thresholding approach,” Multimedia Tools Appl., vol. 82, no. 5, pp. 7757–7777, 2013.
[21]
A. Khmag and N. Kamarudin, “Natural image deblurring using recursive deep convolutional neural network (R-DbCNN) and second-generation wavelets,” in Proc. IEEE Int. Conf. Signal Image Process. Appl. (ICSIPA), 2019, pp. 285–290.
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Published: 14 November 2023
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