Image Quantization Algorithm Research Articles

The destruction of cytoplasmic skeleton leads to the change of nuclear structure and the looseness of lamin A submicroscopic network

The interaction between lamin A and the cytoplasmic skeleton plays a key role in maintaining nuclear mechanical properties. However, the effect of destruction of the cytoplasmic skeleton on the 3D submicroscopic structure of lamin A has not been elucidated. In this study, we developed an image quantization algorithm to quantify changes in the submicroscopic structure of the intact lamin A 3D network within the nucleus. We used blebbistatin or nocodazole to disrupt the fibrillar structure of F-actin or tubulin, respectively, and then quantified changes in the lamin A super-resolution network structure, the morphological and mechanical properties of the nucleus and the spatial distribution of chromosomes. Ultimately, we found for the first time that disruption of the cytoplasmic skeleton changes the lamin A submicroscopic network and nuclear structural characteristics. In summary, this study contributes to understanding the trans-nuclear membrane interaction characteristics of lamin A and the cytoplasmic skeleton.

МЕТОД СТИСНЕННЯ ДАНИХ ДИФРАКЦІЙНИХ ТА ІНТЕРФЕРЕНЦІЙНИХ ЗОБРАЖЕНЬ

In the paper the diffraction and interference images received by numerical simulation and experimentally in solving fundamental and applied problems of photonics. Images are structures with a special intensity distribution formed by the initial field and the optical system. To increase the speed of processing of the image data compression method with further implementation in databases is developed. The method of diffraction and interference images compression is based on the intensity quantization. An algorithm for image quantization has been developed: target intensity values have been determined, which allow setting quantization levels, and data visualization techniques, which determine the threshold values for these levels. The algorithm also contains image segmentation by the size of the minimum size of the topological object. The vicinity of topological object is defined under the conditions of a visual registration form and do not crossing with other regions. The topological objects of the diffraction field determine as the maximum, minimum and zero intensity, and in the interference pattern such topological objects are the maximum, minimum and the region of the band splitting. Important parameters are the average value of the intensity of the whole image - which highlights its overall structure and the average value of the intensity of the local segment. The following results were obtained by compressing data from an 8-bit image in grayscale to 2 bits of color depth are enough for an interference image, and to 3 bits, which are enough for a diffraction image. The quantization differences for diffraction images and interference patterns are shown. Data compression ratios are calculated. On the one hand, the application of the obtained results and recommendations is possible in various fields of medicine, biology and pharmacy , which use laser technology, and on the other hand in the development of separate IT identification of topological objects in the light field, optical image processing and decision support in optical problems.

Color image quantization using flower pollination algorithm

Flower pollination algorithm (FPA) is a swarm-based optimization technique that has attracted the attention of many researchers in several optimization fields due to its impressive characteristics. This paper proposes a new application for FPA in the field of image processing to solve the color quantization problem, which is use the mean square error is selected as the objective function of the optimization color quantization problem to be solved. By comparing with the K-means and other swarm intelligence techniques, the proposed FPA for Color Image Quantization algorithm is verified. Computational results show that the proposed method can generate a quantized image with low computational cost. Moreover, the quality of the image generated is better than that of the images obtained by six well-known color quantization methods.

A Hybrid Color Image Quantization Algorithm Based on k-Means and Harmony Search Algorithms

ABSTRACTColor quantization is one of the most important preprocessing stages in many applications in computer graphics and image processing. In this article, a new algorithm for color image quantization based on the harmony search (HS) algorithm is proposed. The proposed algorithm utilizes the clustering method, which is one of the most extensively applied methods to the color quantization problem. Two variants of the algorithm are examined. The first is based on a standalone HS algorithm, and the second is a hybrid algorithm of k-means (KM) and HS. The objective of the hybrid algorithm is to strengthen the local search process and balance the quantization quality and computational complexity. In the first stage, the high-resolution color space is initially condensed to a lower-dimensional color space by multilevel thresholding. In the second stage, the compressed colors are clustered to a palette using the hybrid KMHS to obtain final quantization results. The algorithm aims to design a postclustering quantization scheme at the color-space level instead of the pixel level. This significantly reduces the computational complexity while maintaining the quantization quality. Experimental results on some of the most commonly used test images in the quantization literature demonstrate that the proposed method is a powerful method, suggesting a higher degree of precision and robustness compared to existing algorithms.

Color Image Quantization Algorithm Based on Differential Evolution

Some stochastic optimization methods, such as Particle Swarm Optimization Algorithms (PSO) and Genetic Algorithms (GA), have been used to solve the color image quantization. Differential Evolution Algorithm (DE) is one of the novel stochastic optimization methods. Few researches have been done for using DE to solve the color image quantization. This paper proposes a DE-based color image quantization algorithm. In the proposed algorithm, a better colormap is designed by using DE to update some randomly initialized candidate colormaps. Some numerical experiments are conducted on a set of commonly used test images. The experimental results show that the proposed algorithm is practicable, and it has better performance than the color image quantization algorithm using PSO.

Color Image Quantization Algorithm Based on Self-Adaptive Differential Evolution

Differential evolution algorithm (DE) is one of the novel stochastic optimization methods. It has a better performance in the problem of the color image quantization, but it is difficult to set the parameters of DE for users. This paper proposes a color image quantization algorithm based on self-adaptive DE. In the proposed algorithm, a self-adaptive mechanic is used to automatically adjust the parameters of DE during the evolution, and a mixed mechanic of DE and K-means is applied to strengthen the local search. The numerical experimental results, on a set of commonly used test images, show that the proposed algorithm is a practicable quantization method and is more competitive than K-means and particle swarm algorithm (PSO) for the color image quantization.

Color Quantization Based on Gaussian Mixture Mode

Color quantization is an important technique for image analysis that reduces the number of distinct colors for a color image. A novel color image quantization algorithm based on Gaussian mixture model is proposed. In the approach, we develop a Gaussian mixture model to design the color palette. Each component in the GMM represents a type of color in the color palette. The task of color quantization is to group pixels into different component. Experimental results show that our quantization method can obtain better results than other methods.

Color Quantization Based on Gaussian Mixture Mode

Color quantization is an important technique for image analysis that reduces the number of distinct colors for a color image. A novel color image quantization algorithm based on Gaussian mixture model is proposed. In the approach, we develop a Gaussian mixture model to design the color palette. Each component in the GMM represents a type of color in the color palette. The task of color quantization is to group pixels into different component. Experimental results show that our quantization method can obtain better results than other methods.

A Colour Image Quantization Algorithm for Time-Constrained Applications

Many techniques have been proposed to quantize a digital colour image in order to reduce the representative number of colours to be suitable for presenting on different types of display screens. In addition, the techniques have been used to significantly reduce the amount of image data required to transfer over a communication network. Most of the published techniques are targetted for implementing on a general purpose multitasking computer with low restriction on time and resource utilizations. The drawback of these techniques relies on the fact that they cannot fulfill the requirement of some applications for real-time constraint and limited resources. In addition, most of the techniques are too complex for hardware realization. In this paper, an algorithm which is more suitable for time critical applications with an additional feature of simplicity to implement on FPGA (Field Programmable Gate Array) platforms is proposed and the details of its implementation and experimentation are presented. The dominate point of the proposed algorithm relies on the fact that it utilizes the weighted sum of the nearest distance along the axis under consideration, which is nontrivial to calculate, instead of the squared Euclidean distance to find the axis to split during. Also, the proposed algorithm has proved that by reducing the number of subspaces to be considered during the variance representative value calculation from 8 to 2 subspaces, the quality of quantized images are comparable to the previously proposed approaches. This makes it possible to further speed up the computational time of the quantization algorithm.

A New Color Image Quantization Algorithm Based on Fuzzy Kernel Clustering

A New Color Image Quantization Algorithm Based on Fuzzy Kernel Clustering

New method for obtaining proper initial clusters to perform FCM algorithm for colour image clustering

To print a colour image on a fabric, colour image clustering is an important step to reduce the number of colours and separate the coloured pattern. Therefore, the performance of colour-clustering algorithm can strongly affect the quality of printing process. Fuzzy c-mean (FCM) clustering is a known clustering algorithm for colour image quantization but the results of FCM depend on the choice of initialization. Therefore, the problem is to find the appropriate initial centres, which are commonly chosen randomly. This paper introduces a novel initialization method for FCM algorithm for clustering colour images. We use the probability density function (pdf) of the colour image to estimate the initial centres. Considering that the three-dimensional computations are complicated and time consuming, we apply principal component analysis (PCA) to find an appropriate direction. Firstly, the three-dimensional colour points should be mapped on the first PCA vector of the colour image data. Then, to obtain the dominant colours, the pdf of the new data is calculated, the points with the highest pdf values have more chance to be initial centres. By selecting a centre, the neighbourhood data in a diameter of σ are eliminated. The number of clusters estimates the value of σ. The process is continued until approaching the desired number of colours. The experimental results show that the proposed method performs well for clustering colour images.

A Robust Color Image Quantization Algorithm Based on Knowledge Reuse of K-Means Clustering Ensemble

This paper presents a novel color image quantization algorithm. This algorithm improves color image quantization stability and accuracy using clustering ensemble. In our approach, we firstly adopt manifold single k-means clusterings for the color image to form a preliminary ensemble committee. Then, in order to avoid inexplicit correspondence among clustering groups, we use the original color values of each clustering centroid directly to construct a final ensemble committee. A mixture model based on the expectation-maximization (EM) algorithm is used as a consensus function to combine the clustering groups of the final ensemble committee to obtain color quantization results. Experimental results reveal that the proposed color quantization algorithm is more stable and accurate than k-means clustering. The preprocessing step of the algorithm, k-means clustering, can be implemented and executed in parallel to improve processing speed.

Accelerated k-means clustering algorithm for colour image quantization

The k-means clustering algorithm is a commonly used algorithm for palette design. If an adequate initial palette is selected, a good quality reconstructed image of a compressed colour image can be achieved. The major problem is that a great deal of computational cost is consumed. To accelerate the k-means clustering algorithm, two test conditions are employed in the proposed algorithm. From the experimental results, it is found that the proposed algorithm significantly cuts down the computational cost of the k-means clustering algorithm without incurring any extra distortion.

The hardware-based implementation of the colour palette generation stage of a colour image quantization algorithm

Many techniques have been proposed to quantize a digital colour image in order to reduce the representative number of colours to be suitable for presenting on different type of display screens. Most of the techniques are targeted for implementation on a general purpose multitasking computer with low restriction on resource utilization. The drawback of these techniques relies on the fact that they cannot fulfil the requirement of some applications with real-time constraint and limited resources. In this paper, the most suitable algorithm for hardware implementation is reviewed and the details of its implementation and experimentation which is focused on the colour palette generation stage are presented. The implementation given in this paper is for use in construction of colour palette image compression for real-time transferring of video data to an LED display board. It can also be adapted to use as a dedicated computer hardware for other real-time image compression applications.

Iterative algorithm for image quantization with signal adaptation

An iterative Fourier transform algorithm (IFTA) is applied for multilevel image quantization. A spatial operator is introduced, based on local signal adaptation, which allows a flexible handling of the quantization noise, especially near the medium quantization level. Examples are shown and comparisons with other iterative Fourier transform algorithms are performed.

Image quantization by a windowed iterative Fourier transform algorithm with local object dependent control

The iterative Fourier transform algorithm (IFTA) is the image quantization algorithm which allows global control of the spectral distribution of quantization noise. In general the spectral distribution of the image information varies locally in the image. A windowed iterative Fourier transform algorithm (WIFTA) is proposed, which allows to vary the spectral control of the quantization noise locally in the image. Thus the algorithm can be adapted to the local image information. This local object dependent control can be utilized to preserve the image information to a larger extent than with a space invariant control.

Texture Analysis for Enhanced Color Image Quantization

A traditional problem with color image quantization techniques is their inability to handle smooth variations in intensity and chromaticity, leading to contours in the quantized image. To address this problem, this paper describes new techniques for augmenting the performance of a seminal color image quantization algorithm, the median-cut quantizer. Applying a simple texture analysis method from computer vision in conjunction with the median-cut algorithm using a new variant of a k-d tree, we show that contouring effects can be alleviated without resorting to dithering methods and the accompanying decrease in signal-to-noise ratio. The merits of this approach are evaluated using remotely sensed aerial imagery and synthetically generated scenes.

A genetic Lloyd-Max image quantization algorithm

This paper is devoted to the study of optimal image quantization and its sensitivity to initial conditions. The optimal mean squared algorithm is elaborated, for gray-level as well as for color images. A genetic quantization algorithm is developed, which is a hybrid technique combining optimal quantization with a genetic algorithm. It is shown that the latter technique is almost insensitive to initial conditions and performs better than the former. In the case of color images, the difference between both techniques clearly affects the visual quality.