Digital color halftoning with generalized error diffusion andmultichannel green-noise masks

In this paper, we introduce two novel techniques for digital color halftoning with green-noise-stochastic dither patterns generated by homogeneously distributing minority pixel clusters. The first technique employs error diffusion with output-dependent feedback where, unlike monochrome image halftoning, an interference term is added such that the overlapping of pixels of different colors can be regulated for increased color control. The second technique uses a green-noise mask, a dither array designed to create green-noise halftone patterns, which has been constructed to also regulate the overlapping of different colored pixels. As is the case with monochrome image halftoning, both techniques are tunable, allowing for large clusters in printers with high dot-gain characteristics, and small clusters in printers with low dot-gain characteristics.     

Fuzzy error diffusion

Quantization errors are generally hidden by performing a dithering operation on the image. A common method is to utilize error diffusion. However, this method is prone to error accumulation, resulting in color impulses and streaks. This paper presents a new approach to error diffusion dithering through a fuzzy error diffusion algorithm. In this method, the amount of error to be diffused is determined by considering the relative location of the pixel not only to the closest codebook vector, but to all other palette entries. The goal is to hide the quantization errors by error diffusion, while preventing the excess accumulation of errors. This is achieved through an attraction-repulsion schema according to a fuzzy membership function. We also explored methods to speed up the fuzzy error diffusion process through a L-filter approach by determining a fixed set of membership values. We have implemented the fuzzy error diffusion algorithm for color images and achieved drastic improvements, resulting in superior quality dithered images and significantly lower mean squared error values. A different error measure modeling the characteristic of the human visual system also indicates the superiority of our method.     

Tone-dependent error diffusion

We present an enhanced error diffusion halftoning algorithm for which the filter weights and the quantizer thresholds vary depending on input pixel value. The weights and thresholds are optimized based on a human visual system model. Based on an analysis of the edge behavior, a tone dependent threshold is designed to reduce edge effects and start-up delay. We also propose an error diffusion system with parallel scan that uses variable weight locations to reduce worms.     

Printer models and error diffusion

A new model-based approach to digital halftoning is proposed. It is intended primarily for laser printers, which generate "distortions" such as "dot overlap". Conventional methods, such as clustered-dot ordered dither, resist distortions at the expense of spatial and gray-scale resolution. The proposed approach relies on printer models that predict distortions, and rather than merely resisting them, it exploits them to increase, rather than decrease, both spatial and gray-scale resolution. We propose a general framework for printer models and find a specific model for laser printers. As an example of model-based halftoning, we propose a modification of error diffusion, which is often considered the best halftoning method for CRT displays with no significant distortions. The new version exploits the printer model to extend the benefits of error diffusion to printers. Experiments show that it provides high-quality reproductions with reasonable complexity. The proposed modified error diffusion technique is compared with Stucki's (1981) MECCA, which is a similar but not widely known technique that accounts for dot overlap. Model-based halftoning can be especially useful in transmission of high-quality documents using high-fidelity gray-scale image encoders.     

Memory efficient error diffusion

Because of its good image quality and moderate computational requirements, error diffusion has become a popular halftoning solution for desktop printers, especially inkjet printers. By making the weights and thresholds tone-dependent and using a predesigned halftone bitmap for tone-dependent threshold modulation, it is possible to achieve image quality very close to that obtained with far more computationally complex iterative methods. However, the ability to implement error diffusion in very low cost or large format products is hampered by the requirement to store the tone-dependent parameters and halftone bitmap, and also the need to store error information for an entire row of the image at any given point during the halftoning process. For the first problem, we replace the halftone bitmap by deterministic bit flipping, which has been previously applied to halftoning, and we linearly interpolate the tone-dependent weights and thresholds from a small set of knot points. We call this implementation a reduced lookup table. For the second problem, we introduce a new serial block-based approach to error diffusion. This approach depends on a novel intrablock scan path and the use of different parameter sets at different points along that path. We show that serial block-based error diffusion reduces off-chip memory access by a factor equal to the block height. With both these solutions, satisfactory image quality can only be obtained with new cost functions that we have developed for the training process. With these new cost functions and moderate block size, we can obtain image quality that is very close to that of the original tone-dependent error diffusion algorithm.     

Embedded multilevel error diffusion

We present an algorithm for image browsing systems that embeds the output of binary Floyd-Steinberg (1975) error diffusion, or a low bit-depth gray-scale or color error diffused image into higher bit-depth gray-scale or color error diffused images. The benefits of this algorithm are that a low bit-depth halftoned image can be directly obtained from a higher bit-depth halftone for printing or progressive transmission simply by masking one or more bits off of the higher bit-depth image. The embedding can be done in any bits of the output, although the most significant or the least significant bits are most convenient. Due to constraints on the palette introduced by embedding, the image quality for the higher bit-depth halftone may be reduced. To preserve the image quality, we present algorithms for color palette organization, or binary index assignment, to be used as a preprocessing step to the embedding algorithm.     

Theoretical analysis of the error correction performance ofmajority-logic-like vector symbol codes

The average codeword success probability of the majority-logic-like vector symbol (MLLVS) code is derived for the following two cases: (1) single-pass decoding and (2) upper bound of multipass decoding, when the received word has more than (J-1) symbol errors, where J is the number of check sum equations. The MLLVS code has been simulated by Metzner (1996), and it was concluded that the average error correcting capability of MLLVS codes exceed the decoding capability of Reed-Solomon codes, but is achieved with less complexity. Additionally, for codes that have larger structures, the error correcting capability is sustained even further with a high probability of decoding success through multipass decoding procedures. The mathematical derivations of the error correction performance beyond (J-1) symbol errors serve as theoretical proof of the MLLVS code error correcting capability that was shown only through simulation results until now by Metzner. One characteristic feature of this derivation is that it does not assume any specific inner code usage, enabling the derived decoding probability equations to be easily applied to any inner code selected, of a concatenated coding structure     

Design and analysis of general rotor-flux-oriented vector controlsystems

Reduced-order observers for rotor flux estimation of induction motors are considered. The “current” model and “voltage” model are obtained as special cases. It is shown that the flux dynamics form a nonlinear closed-loop system when the flux estimate is used for field orientation. The observer gain selection is extremely critical for good behavior of this system. A framework is developed, in which the properties of any gain selection can easily be assessed. Four candidate gain selections are considered, two of which yield schemes that do not use the rotor speed in their equations (inherently sensorless schemes). It is also shown that for any gain selection, an equivalent synchronous-frame implementation (i.e., indirect field orientation) always exists     

运动视觉误差分析的模型设计与实现

针对运动目标模型三维建模中,视觉误差导致建模的准确性不好的问题,提出一种基于运动视觉误差补偿的三维模型设计方法。采用全息投影方法进行三维运动模型图形采集,结合边缘像素特征提取方法进行三维模型的体绘制,根据运动视觉误差补偿方法构建图像重构的三维数据场,进行运动模型的边缘轮廓特征提取和Harris角点检测,实现运动模型的三维重建和视景仿真重构。仿真结果表明,采用该方法进行运动模型重建,输出三维模型的噪点较小,轮廓特征配准精度较高,运动图像识别的视觉误差收敛到零,提高成像质量。     

Threshold modulation and stability in error diffusion

Many modifications to error diffusion have been proposed to improve the algorithm and to extend its application to print media and to color. In this article, we describe some of the lessons that have been learned in two areas of error-diffusion research: threshold modulation and error stability. Both of these areas are important in understanding how to improve and extend the algorithm. Many suggested modifications have been made over the years to eliminate these unwanted textures. One method to improve the algorithm involves different ways of calculating and distributing the errors. Some of the modified algorithms have even proposed amplifying or suppressing the errors. Strange effects can occur, however, when the errors are amplified or if negative components are introduced. The stability analysis presented in this article will illustrate these instabilities and explain their relationship to changes in the error distribution.     

Interpolative vector quantization of color images

Interpolative vector quantization has been devised to alleviate the visible block structure of coded images plus the sensitive codebook problems produced by a simple vector quantizer. In addition, the problem of selecting color components for color picture vector quantization is discussed. Computer simulations demonstrate the success of this coding technique for color image compression at approximately 0.3 b/pel. Some background information on vector quantization is provided     

Adaptive error diffusion and its application in multiresolutionrendering

Error diffusion is a procedure for generating high quality bilevel images from continuous-tone images so that both the continuous and halftone images appear similar when observed from a distance. It is well known that certain objectionable patterning artifacts can occur in error-diffused images. Here, we consider a method for adjusting the error-diffusion filter concurrently with the error-diffusion process so that an error criterion is minimized. The minimization is performed using the least mean squares (LMS) algorithm in adaptive signal processing. Using both raster and serpentine scanning, we show that such an algorithm produces better halftone image quality compared to traditional error diffusion with a fixed filter. Based on the adaptive error-diffusion algorithm, we propose a method for constructing a halftone image that can be rendered at multiple resolutions. Specifically, the method generates a halftone from a continuous tone image such that if the halftone is down-sampled, a binary image would result that is also a high quality rendition of the continuous-tone image at a reduced resolution. Such a halftone image is suitable for progressive transmission, and for cases where rendition at several resolutions is required. Cases for noninteger scaling factors are also considered.     

Inverse halftoning and kernel estimation for error diffusion

Two different approaches in the inverse halftoning of error-diffused images are considered. The first approach uses linear filtering and statistical smoothing that reconstructs a gray-scale image from a given error-diffused image. The second approach can be viewed as a projection operation, where one assumes the error diffusion kernel is known, and finds a gray-scale image that will be halftoned into the same binary image. Two projection algorithms, viz., minimum mean square error (MMSE) projection and maximum a posteriori probability (MAP) projection, that differ on the way an inverse quantization step is performed, are developed. Among the filtering and the two projection algorithms, MAP projection provides the best performance for inverse halftoning. Using techniques from adaptive signal processing, we suggest a method for estimating the error diffusion kernel from the given halftone. This means that the projection algorithms can be applied in the inverse halftoning of any error-diffused image without requiring any a priori information on the error diffusion kernel. It is shown that the kernel estimation algorithm combined with MAP projection provide the same performance in inverse halftoning compared to the case where the error diffusion kernel is known.     

Modeling and quality assessment of halftoning by error diffusion

Digital halftoning quantizes a graylevel image to one bit per pixel. Halftoning by error diffusion reduces local quantization error by filtering the quantization error in a feedback loop. In this paper, we linearize error diffusion algorithms by modeling the quantizer as a linear gain plus additive noise. We confirm the accuracy of the linear model in three independent ways. Using the linear model, we quantify the two primary effects of error diffusion: edge sharpening and noise shaping. For each effect, we develop an objective measure of its impact on the subjective quality of the halftone. Edge sharpening is proportional to the linear gain, and we give a formula to estimate the gain from a given error filter. In quantifying the noise, we modify the input image to compensate for the sharpening distortion and apply a perceptually weighted signal-to-noise ratio to the residual of the halftone and modified input image. We compute the correlation between the residual and the original image to show when the residual can be considered signal independent. We also compute a tonality measure similar to total harmonic distortion. We use the proposed measures for edge sharpening, noise shaping, and tonality to evaluate the quality of error diffusion algorithms.     

Adaptive error diffusion method for image quantisation

An adaptive error diffusion method for image quantisation is presented. The error filter coefficients are adapted using a generalised two-dimensional LMS algorithm and the quantisation scale is calculated on the basis of the image histogram. The developed method provides a minimum reconstruction error, a decrease in false contours in the homogeneous areas and precise reproduction of the edges in the output image.<>     

Adaptive threshold modulation for error diffusion halftoning

Grayscale digital image halftoning quantizes each pixel to one bit. In error diffusion halftoning, the quantization error at each pixel is filtered and fed back to the input in order to diffuse the quantization error among the neighboring grayscale pixels. Error diffusion introduces nonlinear distortion (directional artifacts), linear distortion (sharpening), and additive noise. Threshold modulation, which alters the quantizer input, has been previously used to reduce either directional artifacts or linear distortion. This paper presents an adaptive threshold modulation framework to improve halftone quality by optimizing error diffusion parameters in the least squares sense. The framework models the quantizer implicitly, so a wide variety of quantizers may be used. Based on the framework, we derive adaptive algorithms to optimize 1) edge enhancement halftoning and 2) green noise halftoning. In edge enhancement halftoning, we minimize linear distortion by controlling the sharpening control parameter. We may also break up directional artifacts by replacing the thresholding quantizer with a deterministic bit flipping (DBF) quantizer. For green noise halftoning, we optimize the hysteresis coefficients.     

Row-oriented multiscale error diffusion technique for digitalhalftoning

A digital halftoning method is proposed to diffuse error with a more symmetric error distribution by making use of the concept of multiscale error diffusion. The method can improve the diffusion performance by effectively reducing directional hysteresis. The diffusion is row-oriented rather than frame-oriented and hence can reduce the latency and computational effort as compared with conventional multiscale error diffusion schemes. This makes it possible for real-time applications     

Digital half-toning by error diffusion with perturbation

An error diffusion method is well known as one of the half-toning methods for displaying grey tone pictures on a bilevel display. However, the error diffusion method has some shortcomings, such as the appearance of correlated artefacts and directional hysteresis. To overcome these shortcomings, the author proposes a new error diffusion method with perturbation     

Design, performance, and complexity analysis of residualtrellis-coded vector quantizers

Multistage trellis-coded vector quantization (MS-TCVQ) is developed as a constrained trellis source-coding technique. The performance of the two-stage TCVQ is studied for Gaussian sources. Issues of stage-by-stage design, output alphabet selection, and complexity are addressed with emphasis on selecting and partitioning the stage codebooks. For a given rate, MS-TCVQ achieves low encoding and storage complexity compared to TCVQ, and comparisons with same-dimensional multistage vector quantization indicate a 0.5-3-dB improvement in signal-to-quantization-noise ratio