Feature-preserving multiscale error diffusion for digital halftoning

Abstract

Abstract. Multiscale error diffusion is superior to conventional error diffusion methods in digital halftoning as it can eliminate directional hysteresis completely. However, there is a bias to favor a particular type of dots in the course of the halftoning process. A new multiscale error diffusion method is proposed to improve the diffusion performance by reducing the aforementioned bias. The proposed method can eliminate the pattern noise in flat regions and the boundary effect found in some other conventional multiscale error diffusion methods. At the same time, it can preserve the local features of the input image in the output. This is critical to quality, especially when the resolution of the output is limited by the physical constraints of the display unit.

Low-complexity high-performance multiscale error diffusion technique for digital halftoning

“…This analysis serves two purposes. First, it quantitatively shows that MED is better than conventional error diffusion and its remedial schemes in various aspects including spatial dot distribution and noise characteristics, which is not provided in any literature [7][8][9] about MED. Second, it shows that, as compared with other MED algorithms, the performance of the proposed algorithm is comparable to those in Refs.…”

“…The difference is that it puts its focus on the realization complexity and reduces it by tackling the technical problems in a different way. Unlike other MED algorithms, [7][8][9] which are basically frameoriented, the proposed algorithm performs error diffusion in a block-based manner to support parallel processing and reduce the effort for locating critical pixels. This is based on the idea that, during multiscale error diffusion, the quantization error of a pixel is usually consumed during its propagation to a distant pixel.…”

“…Essentially, all multiscale error diffusion ͑MED͒ algorithms [7][8][9] are two-step iterative algorithms. [7][8][9] At each iteration, they first locate a critical pixel in the output image B based on an updated version of the input image X and assign it a binary value.…”

“…[7][8][9] At each iteration, they first locate a critical pixel in the output image B based on an updated version of the input image X and assign it a binary value. Then the quantization error of the selected pixel is diffused to the neighboring pixels with a noncausal filter so as to update X.…”

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

“…Some approaches adopt a multiscale approach to produce an image halftone by handling the process at multiple spatial resolutions [3]- [5]. Wong considered a method for adjusting the error-diffusion filter concurrently with the error-diffusion process so that an error criterion is minimized.…”