Green Noise Digital Halftoning With Multiscale Error Diffusion

Fung, Yik-Hing; Chan, Yuk‐Hee

doi:10.1109/tip.2010.2044961

Cited by 22 publications

(13 citation statements)

References 14 publications

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“…In our simulation, FMEDg [12] was exploited as it allows one to adjust the average dot cluster size and hence the noise characteristics of the halftoning output linearly and flexibly by adjusting a single parameter R 1. R 1 is actually the inner radius of the isotropic ring diffusion filter used in FMEDg for error diffusion.…”

Section: Simulatio Resultsmentioning

confidence: 99%

Green noise video halftoning

Fung

Chan

2014

2014 19th International Conference on Digital Signal Processing

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Video halftoning is a technology used to render a video onto a display device that can only display limited number of levels. Conventional video halftoning algorithms produce blue noise video halftones which are prone to flickering. Dedicated deflickering processes are hence required to reduce flickering. These processes share a common approach in which pixels are artificially made stable subject to some quality constraints. Due to the difficulty to control the extent of stability, artifacts caused by overstability such as dirty window effect, subtle motion and residual shadow are easily found in video halftones. In this paper, we suggest producing green noise video halftones instead of blue noise video halftones. By doing so, we can effectively reduce flickering and eliminate artifacts caused by overstability from the root simultaneously. I. I TRODUCTIOVideo halftoning is a technique used to convert a gray-level video into a bi-level video. It is widely used to render a gray-level video onto a display device that can only support binary levels such as electronic paper.A natural realization of video halftoning is to halftone each frame of the original video sequence separately with a well-developed halftoning algorithm such as error diffusion. However, as output frames are made up of pixels of binary levels (0 and 1), when frames are halftoned independently, it is very likely that in the halftoned video the intensity values of a pixel toggles between 0 and 1 frequently. As a consequence, flickering occurs when the halftoned video is played at a medium frame rate.Various techniques have been proposed to reduce the flickering artifacts[1]- [8]. The basic idea behind them is to reduce the unnecessary toggle of pixel values along the time axis by making use of the temporal correlation among frames. For example, a three-dimensional (3-D) error diffusion algorithm was proposed in [1] to minimize the flickering artifacts. In [2], Gotsman proposed an iterative video halftoning algorithm in which the halftoning output of a particular frame is initialized to be the halftoning output of its previous frame and then rectified iteratively to minimize the temporal flicker between two successive frames. In [4], different 3-D error diffusion filters were designed to produce video halftones for being played at different frame rates to solve the problem. In [5], a direct binary search algorithm is applied for video halftoning. In [7], Sun proposed a video halftoning algorithm in which quantization error of a pixel is diffused to its spatiotemporal neighbors by separable one-dimensional temporal and two-dimensional spatial error diffusions. Motion adaptive gain control is employed to enhance the temporal consistency of the visual patterns by minimizing the flickering artifacts. In [8], a reference frame is derived from a group of frames and then used to control the temporal stability of the pixels in the group of frames based on a flickering sensitivity-based human visual model.As mentioned earlier, all these algorithms share the sa...

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Section: Simulatio Resultsmentioning

confidence: 99%

Green noise video halftoning

Fung

Chan

2014

2014 19th International Conference on Digital Signal Processing

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“…30,[32][33][34][35][36][37] Without loss of generality, consider the case that we are handling layer I n . Layer I 1 is handled first because it carries more energy.…”

Section: Details Of Stagementioning

confidence: 99%

“…31 Extensive studies on FMED show that it is able to eliminate directional hysteresis, preserve spatial features, and provide outputs with good blue noise characteristics. [32][33][34][35][36][37] During its realization, pixels are not processed sequentially with a predefined order, which allows one to effectively and efficiently control dot positioning. Because controlling color overlapping, dot positioning, and dot coloring is the key to success in HED, we would like to explore whether fusing HED and FMED can create greater synergy in color halftoning.…”

Section: Introductionmentioning

confidence: 99%

Blue noise digital color halftoning with multiscale error diffusion

Fung

Chan

2014

J. Electron. Imaging

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A recent trend in color halftoning is to explicitly control color overlapping, dot positioning, and dot coloring in different stages of the halftoning process. As feature-preserving multiscale error diffusion (FMED) shows its capability and flexibility in dot positioning in both binary and multilevel halftoning applications, it naturally becomes a potential candidate in the development of new color halftoning algorithms. This paper presents an FMED-based color halftoning algorithm developed based on the aforementioned strategy. In contrast to the algorithms that adopt the same strategy, the proposed algorithm has no bias for specific Neugebauer primaries in dot coloring and has no fixed scanning path to place dots in dot positioning. These features allow the algorithm to place dots of the correct colors on the right positions with less constraint. Hence, the algorithm is better able to preserve image features. Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms Journal of Electronic Imaging 063013-10 Nov∕Dec 2014 • Vol. 23(6) Fung and Chan: Blue noise digital color halftoning with multiscale error diffusion Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 05/15/2015 Terms of Use: http://spiedl.org/terms

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“…Fung ports parallel processing and can further reduce the computational complexity [9]. Fung also proposed a MED algorithm to produce halftones of desirable green noise characteristics [10].…”

Section: Introductionmentioning

confidence: 99%

Digital Halftoning through Approximate Optimization of Scale-Related Perceived Error Metric

Zhang

2016

IEICE Trans. Inf. & Syst.

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SUMMARYThis work presents an approximate global optimization method for image halftone by fusing multi-scale information of the tree model. We employ Gaussian mixture model and hidden Markov tree to characterized the intra-scale clustering and inter-scale persistence properties of the detailed coefficients, respectively. The model of multiscale perceived error metric and the theory of scale-related perceived error metric are used to fuse the statistical distribution of the error metric of the scale of clustering and cross-scale persistence. An Energy function is then generated. Through energy minimization via graph cuts, we gain the halftone image. In the related experiment, we demonstrate the superior performance of this new algorithm when compared with several algorithms and quantitative evaluation.

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Green Noise Digital Halftoning With Multiscale Error Diffusion

Cited by 22 publications

References 14 publications

Green noise video halftoning

Green noise video halftoning

Blue noise digital color halftoning with multiscale error diffusion

Digital Halftoning through Approximate Optimization of Scale-Related Perceived Error Metric

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