Yik-Hing Fung

Green Noise Digital Halftoning With Multiscale Error Diffusion

Multiscale error diffusion (MED) is superior to conventional error diffusion algorithms as it can eliminate directional hysteresis completely and possesses a good blue noise characteristic. However, due to its filter design, it is not suitable for systems with poor isolated dot generation and instable dot gain. In this paper, we propose a MED algorithm to produce halftones of desirable green noise characteristics. This algorithm allows one to adjust the desirable cluster size freely through a single parameter and supports a linear relationship between the cluster size and the input gray level. With a close-to-isotropic diffusion filter, the algorithm can effectively remove pattern artifacts, eliminate directional artifacts and preserve original image details. Analysis and simulation results show that it provides better performance in terms of various aspects including dot distribution, anisotropy and output image quality as compared with other conventional green noise error diffusion algorithms.

A POCS-based restoration algorithm for restoring halftoned color-quantized images

This paper studies the restoration of images which are color-quantized with error diffusion. Though there are many reported algorithms proposed for restoring noisy blurred color images and inverse halftoning, restoration of color-quantized images is rarely addressed in the literature especially when the images are color-quantized with halftoning. Direct application of existing restoration techniques are generally inadequate to deal with this problem. In this paper, a restoration algorithm based on projection onto convex sets is proposed. This algorithm makes use of the available color palette and the mechanism of a halftoning process to derive useful a priori information for restoration. Simulation results showed that it could improve the quality of a halftoned color-quantized image remarkably in terms of both SNR and CIELAB color difference metric.

Tone-dependent noise model for high-quality halftones

Abstract. A digital halftone of blue noise characteristics is preferred as dots in the halftone of a constant input should be isotropically and homogeneously distributed. In practice, the placement of dots is constrained by a sampling grid and hence aliasing happens when the input gray level is in the middle range. To solve this problem, Lau et al. suggested replacing isolated dots by dot clusters to maintain the principal frequency of the output to be 1/2 when this happens. However, this model does not take into account that due to the stochastic nature of the dot distribution there is a considerable amount of energy distributed around the principal frequency and it causes aliasing problems even when the principal frequency of the output is 1/2. Here, we present a new noise model that takes this factor into account. A halftoning algorithm is then proposed to generate halftones that satisfy the new noise model. By comparing its performance with that of some other algorithms that are proposed based on the traditional blue noise model and Lau et al.’s noise model, one can see that the proposed noise model can be a better model to describe the noise characteristics of a high-quality halftone.

Optimizing the Error Diffusion Filter for Blue Noise Halftoning With Multiscale Error Diffusion

A good halftoning output should bear a blue noise characteristic contributed by isotropically-distributed isolated dots. Multiscale error diffusion (MED) algorithms try to achieve this by exploiting radially symmetric and noncausal error diffusion filters to guarantee spatial homogeneity. In this brief, an optimized diffusion filter is suggested to make the diffusion close to isotropic. When it is used with MED, the resulting output has a nearly ideal blue noise characteristic.

Embedding halftones of different resolutions in a full-scale halftone

To efficiently render halftone images for various printers and displays that support different resolutions, it is desirable that all halftoning results can be produced at a time, and all of them can be embedded in a single full-scale halftone image such that a simple down-sampling process can extract images of suitable resolutions from it if necessary. This letter presents a framework for achieving this objective with multiscale error diffusion. A multi-resolution halftoning algorithm is proposed based on this framework. Simulation results show that the proposed algorithm can provide good image quality at different resolutions.

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

Multiscale error diffusion (MED) is superior to conven- tional error diffusion algorithms as it can eliminate directional hys- teresis completely. However, due to its frame-oriented processing nature, the computational complexity is comparatively high. Further- more, though theoretically MED can remove directional hysteresis by eliminating predefined scanning paths and causal filters, no com- prehensive quantitative analysis on this issue can be found in the literature. A fast MED algorithm is proposed and a detailed analysis on the performance of various MED algorithms including the pro- posed one are provided. Analysis and simulation results show that the proposed algorithm can reduce the computational complexity without sacrificing the image quality as compared with conventional MED algorithms. The proposed algorithm also supports parallel pro- cessing and hence can further reduce the processing time.

Multilevel halftoning using multiscale error diffusion

Advances in printing technology has made multilevel halftoning become important, as printers can now print inks of dif- ferent intensities. This work presents a multilevel halftoning algo- rithm based on multiscale error diffusion. This algorithm takes care of constrained pixels before handling the unconstrained pixels, and diffuses errors with a noncausal filter such that halftones of better image quality can be achieved.

Image enlargement using fractal

In fractal coding technique, an image is encoded by making use of its self-similarity property. The image can be reconstructed with some well-defined contractive mappings based on this property and hence, in theory, the reconstructed image can be of any desirable size by using an initial image of appropriate size during the decoding process. However, in practice, the enlarged image is always degraded due to the sub-optimal contractive mappings used. In this paper, a fractal-based image enlargement technique is proposed to reduce this problem. This technique can preserve the details in edge regions while maintaining the smoothness in flat regions, which is superior to conventional image enlargement techniques such as bilinear interpolation and cubic convolution.

A simulated annealing restoration algorithm for restoring halftoned color-quantized images

Restoration of color-quantized images is rarely addressed in the literature especially when the images are color-quantized with halftoning. Most existing restoration algorithms are generally inadequate to deal with this problem as they were proposed for restoring noisy blurred images. In this paper, a restoration algorithm based on simulated annealing is proposed to solve the problem. This algorithm makes a good use of the available color palette and the mechanism of a halftoning process to derive useful a priori information for restoration. Simulation results show that it can improve the quality of a halftoned color-quantized image remarkably in terms of both SNR and CIELAB color difference metric. The subjective quality of the restored images can also be improved.

Tone-dependent error diffusion based on an updated blue-noise model

Abstract. The conventional blue-noise model that specifies the desired noise characteristics of an ideal halftone has been updated recently, and simulation results showed that the updated model can serve as a better guideline for developing halftone algorithms. At the moment, only a feature-preserving multiscale error diffusion-based algorithm was developed based on the updated noise model. As the algorithm does not support real-time applications, a tone-dependent error diffusion (TDED) algorithm is developed based on the updated noise model. To support the proposed TDED algorithm, we optimize a diffusion filter and a quantizer threshold for each possible input gray level based on the updated noise model, such that the algorithm can adapt its diffusion filter and quantizer according to the input intensity value of a pixel to produce a halftone. Simulation results showed that the proposed TDED algorithm can successfully produce halftones bearing the desired noise characteristics as specified by the updated noise model. As a consequence, it provides better performance than conventional error diffusion-based algorithms in terms of various measures including radially averaged power spectrum density and anisotropy. When processing real images, it can eliminate directional artifacts, regular structure patterns, and unintended sharpening effects in its halftoning outputs.