Least-squares color Halftoning Algorithm Based on Clustering Analysis

This work employs the well known weighted least squares method to optimization to produce halftone images using improved K-means clustering theory. Our algorithm applies to both a printer model and a model for the human visual system (HVS). In this algorithm, the improved K-means clustering method is used to segment an image several regions. In the halftone process, each clustering uses the weighted least-squares model-based(WLSMB) algorithm by use of direct binary search iterative method to obtain halftone image. Analysis and simulation results show that the proposed algorithm produces better gray-scale halftone image quality when we increase the number of clustering with a certain range and outperforms least-squares model-based algorithm in the PSNR (Peak Signal Noise Ratio), WSNR (Weighted Signal Noise Ratio) criteria.

Data hiding and reversible data hiding research has primarily focused on grayscale and color images, because binary and halftone images are prone to visual distortion caused by a small number of errors in pixel representation. As a result, reversible data hiding is more useful than halftone-based data hiding. This study proposes an investigation of encrypted halftone images based on dual reversible data hiding, which improves the reversibility and security of the image by utilizing a dual cover image. Since halftone images are adequately compressed, they are beneficial in low-channel-bandwidth environments. Hamming code (HC) (7,4) is applied to each block of the halftone image to hide the secret data, and two halftone images are recorded and sent to different receivers at the end of the embedding process. Recipients can use the proposed method and the two marked images to extract the message and recover the cover halftone image. The proposed data hiding method can enhance the quality of the decrypted image by appropriately increasing the block size, and conversely, sufficiently large amounts of data can be hidden by reducing the block size. The experimental results provide evidence of the effectiveness of the proposed method in terms of both image quality and the embedding rate.

Face recognition is done gray or color images. Face recognition can be also done in binary images. Halftone images are binary images having gray shade information. These images are more informative as compared to binary images. Halftone images can be generated from different kernels. The quality of a halftone image depends on the kernel used to generate it. Halftone images can be used to generate human recognizable features for face recognition. The human recognizable features are fed to multiclass SVM classifiers for face recognition. The performance of using different human recognizable features generated from halftone images of different standard kernels is studied. It is found that some of kernels give higher recognition rate than the other. Also, the performance is also dependent on size of the window used for feature extraction. In most cases, the minimum size window 3 × 3 gives higher recognition rate in most of the cases. Of the standard kernels Frankie Siera-3, Stucki kernels give higher recognition rate and of the proposed kernels P2 kernel gives higher recognition rate than other proposed kernels.

This paper presents new results in the halftoning of color images. The first automatic generation of the Wall Street Journal type halftones of gray-scale images was done using the M-lattice system and reported in Sherstinsky and Picard [1994]. The M-lattice system was derived from the reaction-diffusion model, first proposed by Turing [1952] in order to explain mammal coat patterns. The M-lattice is a non-linear dynamical system that is well-suited for a variety of applications formulated as constrained non-linear optimization. In particular, it can perform image processing operations that emphasize oriented patterns. The present study uses this property to extend the special-effects halftoning of gray-scale images to that of color images. The duality metric comes from the directionality information extracted by steerable filters from the gray-scale version of the original color image. The binary requirement is stated as an explicit constraint, and all three (red, green, and blue) halftone components are synthesized simultaneously by the M-lattice.

Two kinds of models were derived that predicted spectral reflectance factor of colors formed using an ink-jet printer. One was the spectral Murray–Davies–Yule–Nielsen model in which n-value was assumed to vary as a function of wavelength. The other was based on the Omatsu model in which the path length of light scattering was assumed to vary as a function of wavelength. Model parameters were optimized using a test target of 57 samples consisting of cyan, magenta, yellow, red, green, blue, and black colors varying between white and the maximum ink amount. Average accuracy of an independent data set sampling the printer's color gamut was 4.2 and 3.9 ΔEab*, for the Murray–Davies–Yule–Nielsen and the Omatsu models, respectively. The difference in performance was not significant. The Yule–Nielsen model was selected to build device profiles because of its simplicity in comparison to the Omatsu model. A desktop scanner was colorimetrically characterized using a multiple-linear-regression model to build a concatenated device profile in which digital counts of a scanned photographic reflection print were the input and those of the printer were the output. Because the printer model was analytically noninvertable, the Newton-Raphson and the Simplex iterative methods were evaluated as candidate optimization methods to build 33 × 33 × 33 color look-up tables. These tables were evaluated by comparing a photographic reflection IT8.7/2 target with its printed reproduction. The Simplex method yielded superior results, particularly for colors near the edge or outside of the printer's color gamut. The average ΔEab* error from a profile based on the Simplex method was 5.9 including colors outside of the printer's color gamut.

Color halftoning is a method for representing continuous tone color images with limited colors. To implement halftone, error diffusion is one of the most popular algorithms due to its highperformance results. In the conventional error diffusion, the fixed thresholds and filter weights are used. In the process, many artifacts are introduced in the halftone images as side effects. With the color aspect, each of the three color planes is independently calculated during the process of traditional error diffusion. However, the pattern of dots formed independently by combining the dots of each of color planes is often not visually pleasing because no effort is made to insure that the dots of each of the different color planes are ideally distributed relative to the dots of the other color planes. In this paper, we present an integrated error diffusion algorithm in which variable filter weights, threshold matrix, serpentine scan path and multi-color halftoning are used to reduce the noises generated during the halftone process. Our experimental results showed that worm-like artifacts, structural artifacts and start-up artifacts are successfully decreased and the color halftoned images are more comfortable to the human visual system.

Visual cryptography is a cryptographic technique which allows visual information to be encrypted in such a way that the decryption can be performed by the human visual system, without the aid of computers. This paper proposes a schema of information hiding within the method of visual cryptography. The gray image is changed into two halftone images via the different dither matrixes respectively, and the secret binary pixels are encoded into shares. The secret information can be restored by stacking the different shared halftone images together. Simulation results show that the secret binary image can be decoded efficiently.

Adaptive model-based digital halftoning incorporating image enhancement

Halftoning is a process that deliberately injects noise into the original image in order to obtain visually pleasing output images with a smaller number of bits per pixel for displaying or printing purposes. In this paper, a novel inverse halftoning method is proposed to restore a continuous tone image from the given halftone image. A set theoretic formulation is used where three sets are defined using the prior information about the problem. A new space domain projection is introduced assuming the halftoning is performed with error diffusion, and the error diffusion filter kernel is known. The space domain, frequency domain, and space-scale domain projections are used alternately to obtain a feasible solution for the inverse halftoning problem which does not have a unique solution.

Visual information, in the form of images and video, comes from the interaction of light with objects. Illumination is a fundamental element of visual information. Detecting and interpreting illumination effects is part of our everyday life visual experience. Shading for instance allows us to perceive the three-dimensional nature of objects. Shadows are particularly salient cues for inferring depth information. However, we do not make any conscious or unconscious effort to avoid them as if they were an obstacle when we walk around. Moreover, when humans are asked to describe a picture, they generally omit the presence of illumination effects, such as shadows, shading, and highlights, to give a list of objects and their relative position in the scene. Processing visual information in a way that is close to what the human visual system does, thus being aware of illumination effects, represents a challenging task for computer vision systems. Illumination phenomena interfere in fact with fundamental tasks in image analysis and interpretation applications, such as object extraction and description. On the other hand, illumination conditions are an important element to be considered when creating new and richer visual content that combines objects from different sources, both natural and synthetic. When taken into account, illumination effects can play an important role in achieving realism. Among illumination effects, shadows are often integral part of natural scenes and one of the elements contributing to naturalness of synthetic scenes. In this thesis, the problem of extracting shadows from digital images is discussed. A new analysis method for the segmentation of cast shadows in still and moving images without the need of human supervision is proposed. The problem of separating moving cast shadows from moving objects in image sequences is particularly relevant for an always wider range of applications, ranging from video analysis to video coding, and from video manipulation to interactive environments. Therefore, particular attention has been dedicated to the segmentation of shadows in video. The validity of the proposed approach is however also demonstrated through its application to the detection of cast shadows in still color images. Shadows are a difficult phenomenon to model. Their appearance changes with changes in the appearance of the surface they are cast upon. It is therefore important to exploit multiple constraints derived from the analysis of the spectral, geometric and temporal properties of shadows to develop effective techniques for their extraction. The proposed method combines an analysis of color information and of photometric invariant features to a spatio-temporal verification process. With regards to the use of color information for shadow analysis, a complete picture of the existing solutions is provided, which points out the fundamental assumptions, the adopted color models and the link with research problems such as computational color constancy and color invariance. The proposed spatial verification does not make any assumption about scene geometry nor about object shape. The temporal analysis is based on a novel shadow tracking technique. On the basis of the tracking results, a temporal reliability estimation of shadows is proposed which allows to discard shadows which do not present time coherence. The proposed approach is general and can be applied to a wide class of applications and input data. The proposed cast shadow segmentation method has been evaluated on a number of different video data representing indoor and outdoor real-world environments. The obtained results have confirmed the validity of the approach, in particular its ability to deal with different types of content and its robustness to different physically important independent variables, and have demonstrated the improvement with respect to the state of the art. Examples of application of the proposed shadow segmentation tool to the enhancement of video object segmentation, tracking and description operations, and to video composition, have demonstrated the advantages of a shadow-aware video processing.

Natural color image enhancement and evaluation algorithm based on human visual system

Information System for Administrating and Distributing Color Images Through Internet

To print continuous tone images, Electrophotographic (EP) printer uses halftoning technique. Image halftoning prints the image as matrix of dots which are halftone patterns those are hardly perceived by human eyes. So scanned images obtained from such hard printed copies are normally affected by screen like artifacts and moire patterns. Scanned color Image improvement using Image descreening technique will be used to descreen color scanned images so as to remove screening patterns and Moire effects. Technique applied on grayscale images produces clean smooth regions and sharp edges from scanned halftone images. For color images technique consists of separating image plane and applying denoising algorithm to filter out printing noise and distortions. Features are then extracted to build adaptive filter kernel which while remove halftone patterns. Edge preserving filtering will be carried out to enhance the sharpness of the edges and finally all planes will be merged to get final denoised color image.

Imperceptibility is one of the most important aspects of the steganography method. Specifically, for steganography in images, embedded messages must not be felt by the human visual system. Humans have a higher sensitivity in color images compared to grayscale images, therefore the method that is still being tested on grayscale images needs to be retested on color images. Previous research has shown that the bit flipping method can increase imperceptibility to around 9dB in grayscale images. In this research the bit flipping method is tested on color images in the RGB format, the message capacity embedded in the image is 1 bit per pixel. The results of the imperceptibility test turned out to result in a more varied increase due to the greater number of layers in the color image. However, the bit flipping method is also proven to work well on color images, and even a maximum PSNR increase of more than 13dB. At the extraction stage, the message image can also be extracted perfectly, namely with the value NC = 1.

Among the digital halftoning, the error diffusion halftoning gives better subjective quality than other halftoning techniques. But it also makes edges of objects blurred. To overcome the defect, this thesis proposes the modified error diffusion halftoning algorithm to enhance the edges using the spatial perceptual properties of the human visual system. Using the properties that the human eyes perceive not the pixel's luminance itself but the local average luminance and that information that human eyes perceive spatial variation, the proposed method computes information of edge enhancement (IEE). The IEE is added to the quantizer's input pixel and feeds into the halftoning quantizer. The quantizer produces the halftone image having the enhanced edge. Also this thesis proposes the technique that the coefficients of the error diffusion filter are adapted according to the correlation among color components. The computer simulation results show that the proposed method produces finer halftoning images than conventional methods due to the enhanced edges. And the proposed method also preserves similar in edges to original image and reduces some defects such as color impulse and false contours.