A modified multiscale error diffusion technique for digital halftoning

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Adaptive methods for dithering color images

Cataloged from PDF version of article.Most color image printing and display devices do not have the capability of reproducing true color images. A common remedy is the use of dithering techniques that take advantage of the lower sensitivity of the eye to spatial resolution and exchange higher color resolution with lower spatial resolution. In this paper, an adaptive error diffusion method for color images is presented. The error diffusion filter coefficients are updated by a normalized least mean square-type (LMS-type) algorithm to prevent textural contours, color impulses, and color shifts, which are among the most common side effects of the standard dithering algorithms. Another novelty of the new method is its vector character: Previous applications of error diffusion have treated the individual color components of an image separately. Here, we develop a general vector approach and demonstrate through simulation studies that superior results are achieved. © 1997 IEEE

Degree of quantization and spatial addressability tradeoffs in perceived quality of color images

The objective of this thesis research was to investigate the tradeoffs between the number of quantization levels and spatial addressability of printed color images. Image quantization was done by employing the error-diffusion algorithm. The diffusion of error was performed in CMYK color space. The resulting images were printed on a color output device simulating different spatial addressabilities. To evaluate the perceived image quality, a psychophysical experiment was conducted followed by a statistical analysis of the experimental data. Based on the results of this analysis, the conclusions on the tradeoffs between the number of quantization levels and spatial addressability were drawn. It was determined that the tradeoffs were scene dependent with photographic scenes being able to sustain greater reduction in addressability without perceived image quality being decreased than graphics. The experiment showed that photographic scenes were sufficient to be printed with 5 bits per pixel per color at 100 dots per inch, and graphics with 3 bits per pixel per color at 300 dots per inch. If a single bits per color / dots per inch combination is to be named as the optimum combination equivalent to the best possible image for the given system (8bpc/300dpi), it would have to be 3bpc/300dpi. This combination was found to be equivalent to the quality of the best possible image at the normal viewing distance for all scenes in the experiment

A Structurally Regularized CNN Architecture via Adaptive Subband Decomposition

We propose a generalized convolutional neural network (CNN) architecture that first decomposes the input signal into subbands by an adaptive filter bank structure, and then uses convolutional layers to extract features from each subband independently. Fully connected layers finally combine the extracted features to perform classification. The proposed architecture restrains each of the subband CNNs from learning using the entire input signal spectrum, resulting in structural regularization. Our proposed CNN architecture is fully compatible with the end-to-end learning mechanism of typical CNN architectures and learns the subband decomposition from the input dataset. We show that the proposed CNN architecture has attractive properties, such as robustness to input and weight-and-bias quantization noise, compared to regular full-band CNN architectures. Importantly, the proposed architecture significantly reduces computational costs, while maintaining state-of-the-art classification accuracy. Experiments on image classification tasks using the MNIST, CIFAR-10/100, Caltech-101, and ImageNet-2012 datasets show that the proposed architecture allows accuracy surpassing state-of-the-art results. On the ImageNet-2012 dataset, we achieved top-5 and top-1 validation set accuracy of 86.91% and 69.73%, respectively. Notably, the proposed architecture offers over 90% reduction in computation cost in the inference path and approximately 75% reduction in back-propagation (per iteration) with just a single-layer subband decomposition. With a 2-layer subband decomposition, the computational gains are even more significant with comparable accuracy results to the single-layer decomposition

Visual-Based error diffusion for printers

An approach for halftoning is presented that incorporates a printer model and also explicitly uses the human visual model. Conventional methods, such as clustered-dot screening or dispersed-dot screening, do not solve the gray-level distortion of printers and just implicitly use the eye as a lowpass filter. Error diffusion accounts for errors when processing subsequent pixels to minimize the overall mean-square errors. Recently developed model-based halftoning technique eliminates the effect of printer luminance distortion, but this method does not consider the filtering action of the eye, that is, some artifacts of standard error diffusion still exist when the printing resolution and view distance change. Another visual error diffusion method incorporates the human visual filter into error diffusion and results in improved noise characteristics and better resolution for structured image regions, but gray levels are still distorted. Experiments prove that human viewers judge the quality of a halftoning image based mainly on the region which exhibits the worst local error, and low-frequency distortions introduced by the halftoning process are responsible for more visually annoying artifacts in the halftone image than high-frequency distortion. Consequently, we adjust the correction factors of the feedback filter by local characteristics and adjust the dot patterns for some gray levels to minimize the visual blurred local error. Based on the human visual model, we obtain the visual-based error diffusion algorithm, and further we will also incorporate the printer model to correct the printing distortion. The artifacts connected with standard error diffusion are expected to be eliminated or decreased and therefore better print quality should be achieved. In addition to qualitative analysis, we also introduce a subjective evaluation of algorithms. The tests show that the algorithms developed here have improved the performance of error diffusion for printers

Development Of Digital Half toning Techniques For Grayscale Image

In this paper a complete overview of digital halftoning is given. This paper concludes all about haftoning from its definition to different methods which is used to achieve it. Also drawback of each method is given in this paper. How a new method is better than previous one it is also explained theoretically and mathematically. Floyd & Steinberg's error diffusion technique is a well-known approach to digital halftoning. Its main drawback is that it is inherently serial. This paper is briefly discussing about the method and a complete process to achieve error diffusion and thus to achieve halftoning. ‘Any model which is designed for the human visual system (HVS) is an important component of many halftoning algorithms. Also by using the iterative direct binary search (DBS) algorithm, we have compared the halftone texture quality which is provided by the four HVS models’. We design filter for the four different HVS model and tried to compare these models. Using the different HVS model we have calculated the MSE and thus got the best HVS model

The development of the toner density sensor for closed-loop feedback laser printer calibration

A new infrared (IR) sensor was developed for application in closed-loop feedback printer calibration as it relates to monochrome (black toner only) laser printers. The toner density IR sensor (TDS) was introduced in the early 1980’s; however, due to cost and limitation of technologies at the time, implementation was not accomplished until within the past decade. Existing IR sensor designs do not discuss/address: • EMI (electromagnetic interference) effects on the sensor due to EP (electrophotography) components • Design considerations for environmental conditions • Sensor response time as it affects printer process speed The toner density sensor (TDS) implemented in the Lexmark E series printer reduces these problems and eliminates the use of the current traditional “open-loop” (meaning feedback are parameters not directly affecting print darkness such as page count, toner level, etc.) calibration process where print darkness is adjusted using previously calculated and stored EP process parameters. The historical process does not have the ability to capture cartridge component variation and environmental changes which affect print darkness variation. The TDS captures real time data which is used to calculate EP process parameters for the adjustment of print darkness; as a result, greatly reducing variations uncontrolled by historical printer calibration. Specifically, the first and primary purpose of this research is to reduce print darkness variation using the TDS. The second goal is to mitigate the TDS EMI implementation issue for reliable data accuracy

Black-box printer models and their applications

In the electrophotographic printing process, the deposition of toner within the area of a given printer addressable pixel is strongly influenced by the values of its neighboring pixels. The interaction between neighboring pixels, which is commonly referred to as dot-gain, is complicated. The printer models which are developed according to a pre-designed test page can either be embedded in the halftoning algorithm, or used to predict the printed halftone image at the input to an algorithm being used to assess print quality. In our research, we examine the potential influence of a larger neighborhood (45?45) of the digital halftone image on the measured value of a printed pixel at the center of that neighborhood by introducing a feasible strategy for the contribution. We developed a series of six models with different accuracy and computational complexity to account for local neighborhood effects and the influence of a 45?45 neighborhood of pixels on the central printer-addressable pixel tone development. All these models are referred to as Black Box Model (BBM) since they are based solely on measuring what is on the printed page, and do not incorporate any information about the marking process itself. We developed two different types of printer models Standard Definition (SD) BBM and High Definition (HD) BBM with capture device Epson Expression 10000XL (Epson America, Inc., Long Beach, CA, USA) flatbed scanner operated at 2400 dpi under different analysis resolutions. The experiment results show that the larger neighborhood models yield a significant improvement in the accuracy of the prediction of the pixel values of the printed halftone image. The sample function generation black box model (SFG-BBM) is an extension of SD-BBM that adds the printing variation to the mean prediction to improve the prediction by more accurately matching the characteristics of the actual printed image. We also followed a structure similar to that used to develop our series of BBMs to develop a two-stage toner usage predictor for electrophotographic printers. We first obtained on a pixel-by-pixel basis, the predicted absorptance of printed and scanned page with the digital input using BBM. We then form a weighted sum of these predicted pixel values to predict overall toner usage on the printed page. Our two-stage predictor significantly outperforms existing method that is based on a simple pixel counting strategy, in terms of both accuracy and robustness of the prediction

A New framework for an electrophotographic printer model

Digital halftoning is a printing technology that creates the illusion of continuous tone images for printing devices such as electrophotographic printers that can only produce a limited number of tone levels. Digital halftoning works because the human visual system has limited spatial resolution which blurs the printed dots of the halftone image, creating the gray sensation of a continuous tone image. Because the printing process is imperfect it introduces distortions to the halftone image. The quality of the printed image depends, among other factors, on the complex interactions between the halftone image, the printer characteristics, the colorant, and the printing substrate. Printer models are used to assist in the development of new types of halftone algorithms that are designed to withstand the effects of printer distortions. For example, model-based halftone algorithms optimize the halftone image through an iterative process that integrates a printer model within the algorithm. The two main goals of a printer model are to provide accurate estimates of the tone and of the spatial characteristics of the printed halftone pattern. Various classes of printer models, from simple tone calibrations, to complex mechanistic models, have been reported in the literature. Existing models have one or more of the following limiting factors: they only predict tone reproduction, they depend on the halftone pattern, they require complex calibrations or complex calculations, they are printer specific, they reproduce unrealistic dot structures, and they are unable to adapt responses to new data. The two research objectives of this dissertation are (1) to introduce a new framework for printer modeling and (2) to demonstrate the feasibility of such a framework in building an electrophotographic printer model. The proposed framework introduces the concept of modeling a printer as a texture transformation machine. The basic premise is that modeling the texture differences between the output printed images and the input images encompasses all printing distortions. The feasibility of the framework was tested with a case study modeling a monotone electrophotographic printer. The printer model was implemented as a bank of feed-forward neural networks, each one specialized in modeling a group of textural features of the printed halftone pattern. The textural features were obtained using a parametric representation of texture developed from a multiresolution decomposition proposed by other researchers. The textural properties of halftone patterns were analyzed and the key texture parameters to be modeled by the bank were identified. Guidelines for the multiresolution texture decomposition and the model operational parameters and operational limits were established. A method for the selection of training sets based on the morphological properties of the halftone patterns was also developed. The model is fast and has the capability to continue to learn with additional training. The model can be easily implemented because it only requires a calibrated scanner. The model was tested with halftone patterns representing a range of spatial characteristics found in halftoning. Results show that the model provides accurate predictions for the tone and the spatial characteristics when modeling halftone patterns individually and it provides close approximations when modeling multiple halftone patterns simultaneously. The success of the model justifies continued research of this new printer model framework