Color Quantization Algorithm Research Articles

A dichotomy color quantization algorithm for the HSI color space

Color quantization is used to obtain an image with the same number of pixels as the original but represented using fewer colors. Most existing color quantization algorithms are based on the Red Green Blue (RGB) color space, and there are few color quantization algorithms for the Hue Saturation Intensity (HSI) color space with a simple uniform quantization algorithm. In this paper, we propose a dichotomy color quantization algorithm for the HSI color space. The proposed color quantization algorithm can display images with a smaller number of colors than other quantization methods of RGB color space. The proposed algorithm has three main steps as follows: first, a single-valued monotonic function of the Hue (H) component in the from RGB color space to HSI color space (RGB-HSI) color space conversion is constructed, which can avoid the partition calculation of the H component in the RGB-HSI color space; second, an iterative quantization algorithm based on the single-valued monotonic function is proposed; and third, a dichotomy quantization algorithm is proposed to improve the iterative quantization algorithm. Both visual and numerical evaluations reveal that the proposed method presents promising quantization results.

A convolutional autoencoder and a neural gas model based on Bregman divergences for hierarchical color quantization

Color quantization (CQ) is one of the most common and important procedures to be performed on digital images. In this paper, a new approach to hierarchical color quantization is described, presenting a novel neural network architecture integrated by a convolutional autoencoder and a Growing Hierarchical Bregman Neural Gas (GHBNG). GHBNG is a CQ algorithm that allows the compression of an image by choosing a reduced set of the most representative colors to generate a high-quality reproduction of the original image. In the technique proposed here, an autoencoder is used to translate the image into a latent representation with higher per-pixel dimensionality but reduced resolution, and GHBNG is then used to quantize it. Experimental results confirm the performance of this technique and its suitability for tasks related to color quantization.

The incremental online k-means clustering algorithm and its application to color quantization

Color quantization is a common image processing operation with various applications in computer graphics, image processing, and computer vision. Color quantization is essentially a large-scale combinatorial optimization problem. Many clustering algorithms, both of hierarchical and partitional types, have been applied to this problem since the 1980s. In general, hierarchical color quantization algorithms are faster, whereas partitional ones produce better results provided that they are initialized properly. In this paper, we propose a novel partitional color quantization algorithm based on a binary splitting formulation of MacQueen’s online k-means algorithm. Unlike MacQueen’s original algorithm, the proposed algorithm is both deterministic and free of initialization. Experiments on a diverse set of public test images demonstrate that the proposed algorithm is significantly faster than two popular batch k-means algorithms while yielding nearly identical results. In other words, unlike previously proposed k-means variants, our algorithm addresses both the initialization and acceleration issues of k-means without sacrificing the simplicity of the algorithm. The presented algorithm may be of independent interest as a general-purpose clustering algorithm.

Revisiting the Iterative Ant-tree for color quantization algorithm

The Iterative Ant-tree for color quantization algorithm has recently been proposed to reduce the colors of an image at a low computational cost. It is a clustering-based method that generates good images compared to several well-known color quantization methods. This article proposes the modification of two features of the original algorithm: the value assigned to the parameter associated with the algorithm and the order in which the pixels of the image are processed. As a result, the new variant of the algorithm generates better images than the original and the results are less sensitive to the value selected for the parameter.

Color image quantization with peak-picking and color space

Color image quantization is a significant procedure of reducing the huge range of color values of a digital color image into a limited range. In this paper, an automated clustering of pixels and color quantization algorithm is proposed. The ideal number of representative colors is unknown beforehand in most color quantization algorithms. This is an important handicap in most practical cases. The proposed color quantization approach (PPCS) is able to automatically estimate an appropriate number of colors in a quantized palette. Hence, PPCS requires no number of representative colors to be set in advance. This algorithm has two main steps to follow: color palette design and pixel mapping. The color palette is generated by the combination of the entire peaks of all color component histograms. Such that, all color component histogram was smoothed in order to remove unreliable peaks. Next, unreliable colors will be removed from the palette. Then, each pixel in the image will be assigned to the cluster (unit color in the palette) which has the least Euclidean distance. To evaluate the ability of the PPCS, 22 images from Berkeley segmentation dataset have been randomly selected and tested with PPCS and also by two well-known quantization algorithms. The numerical evaluations have been carried out by using computation time, PSNR, MSE, and SSIM performance criteria. Both visual and numerical evaluations reveal that the proposed method presents promising quantization results. Such that, PPCS is ranked first, second, first and first according to PSNR, MSE, SSIM and computation time, respectively.

A hybrid approach for stain normalisation in digital histopathological images

Stain in-homogeneity adversely affects segmentation and quantification of tissues in histology images. Stain normalisation techniques have been used to standardise the appearance of images. However, most the available stain normalisation techniques only work on a particular kind of stain images. In addition, some of these techniques fail to utilise both the spatial and textural information in histology images, leading to image tissue distortion. In this paper, a hybrid approach has been developed, based on an octree colour quantisation algorithm combined with the Beer-Lambert law, a modified blind source separation algorithm, and a modified colour transfer approach. The hybrid method consists of two stages the stain separation stage and colour transfer stage. An octree colour quantisation algorithm combined with Beer-Lambert law, and a modified blind source separation algorithm are used during the stain separation stage to computationally estimate the amount of stain in an histology image based on its chromatic and luminous response. A modified colour transfer algorithm is used during the colour transfer stage to minimise the effect of varying staining and illumination. The hybrid method addresses the colour variation problem in both H&DAB (Haemotoxylin and Diaminobenzidine) and H&E (Haemotoxylin and Eosin) stain images. The stain normalisation method is validated against ground truth data. It is widely known that the Beer-Lambert law applies to only stains (such as haematoxylin, eosin) that absorb light. We demonstrate that the Beer-Lambert law applies is applicable to images containing a DAB stain. Better stain normalisation results are obtained in both H&E and H&DAB images.

Image retrieval of wool fabric. Part II: based on low-level color features

Color is difficult to distinguish by human vision and is described by keywords, resulting in low efficiency of wool fabric retrieval in factories at present. To obtain the process sheets of existing products and reduce the work of color measurement in sample analysis, this paper proposes an effective method based on dominant colors (DCs) and color moments (CMs) for wool fabric image retrieval. Firstly, the image was scaled to reduce computational time. Then, the hue, saturation, value color space was divided into 128 parts by the fast color quantization algorithm to extract the DCs of the image. Meanwhile, the CMs based on image partition were calculated in CIE L* a* b* color space to describe the spatial color information. Subsequently, different similarity measure methods were carried out based on the DC feature and CM feature. Finally, experiments were conducted on a wool fabric image database with 20,000 images for parameter optimization and verification. The average precision and recall were up to 87% and 44%, respectively. Experimental results show that the proposed scheme can retrieve images with the same or similar colors quickly and effectively and it outperformed other methods, providing referential assistance for the factory worker when retrieving wool fabrics.

Color quantization with Particle swarm optimization and artificial ants

This article describes a color quantization algorithm that combines two swarm-based methods: Particle swarm optimization and artificial ants. The proposed method is based on a previous method that solves the quantization problem by combining the Particle swarm optimization algorithm with the K-means algorithm. K-means is a popular clustering method that has been applied to solve a variety of problems, including the color quantization problem. Nevertheless, it is a time-consuming method, which makes combining the Particle swarm optimization algorithm and K-means less suitable than other color quantization techniques. The proposed method, however, discards the K-means algorithm and applies the Ant-tree for color quantization algorithm in order to reduce execution time. This article shows that the new method outperforms the original one, since it requires less time to obtain higher quality images. In addition, the images produced are also of better quality than those produced by other well-known color quantization methods, such as Neuquant, Octree, Median-cut, Variance-based, Binary splitting and Wu’s methods.

Color Quantization Application Based on K-Means in Remote Sensing Image Processing

In this paper, four kinds of RGB remote sensing images are processed using color quantization algorithm based on K-Means to reduce the number of colors in the image. The color quantization algorithm is to select the most representative color and reduce the useless color in the image as much as possible. This paper assumes that the RGB remote sensing image is composed of multiple pixels. Using the K-Means algorithm to perform unsupervised clustering on these pixels with specific colors, color quantization can be realized. The use of K-Means for color quantization of remote sensing images can reduce the number of colors in those images, so that remote sensing images can be reproduced well in lower performance computer equipment. At the same time, color quantization reduces the size of remote sensing images and improves the efficiency of remote sensing image processing.

The color quantization problem solved by swarm-based operations

The objective of the color quantization problem is to reduce the number of different colors of an image, in order to obtain a new image as similar as possible to the original. This is a complex problem and several solution techniques have been proposed to solve it. Among the most novel solution methods are those that apply swarm-based algorithms. These algorithms define an interesting solution approach, since they have been successfully applied to solve many different problems. This paper presents a color quantization method that combines the Artificial Bee Colony algorithm with the Ant-tree for Color Quantization algorithm, creating an improved version of a previous method that combines artificial bees with the K-means algorithm. Computational results show that the new method significantly reduces computing time compared to the initial method, and generates good quality images. Moreover, this new method generates better images than other well-known color quantization methods such as Wu’s method, Neuquant, Octree or the Variance-based method.

A Hybrid Color Quantization Algorithm That Combines the Greedy Orthogonal Bi-Partitioning Method With Artificial Ants

A color quantization technique that combines the operations of two existing methods is proposed. The first method considered is the Greedy orthogonal bi-partitioning method. This is a very popular technique in the color quantization field that can obtain a solution quickly. The second method, called Ant-tree for color quantization, was recently proposed and can obtain better images than some other color quantization techniques. The solution described in this article combines both methods to obtain images with good quality at a low computational cost. The resulting images are always better than those generated by each method applied separately. In addition, the results also improve those obtained by other well-known color quantization methods, such as Octree, Median-cut, Neuquant, Binary splitting or Variance-based methods. The features of the proposed method make it suitable for real-time image processing applications, which are related to many practical problems in diverse disciplines, such as medicine and engineering.

Artificial ants and fireflies can perform colour quantisation

Several methods based on the behaviour of biological systems have been proposed during the last years to solve a wide range of problems. Ant algorithms and Firefly algorithms are two methods of this type.The Ant-Tree for Colour Quantisation algorithm was recently proposed to quantise colour images. It is a clustering-based method which creates a tree structure where the pixels of the image are grouped in subtrees according to their similarity. A threshold value determines if the similarity is sufficient to associate a pixel to a subtree. The computation of that threshold includes a parameter in (0,1], whose value must be set taking into account the number of colours of the quantised image. Since this parameter influences the quality of the final image, it is recommended to perform several tests with different values of that parameter and then select the best result. In order to reduce the influence of said parameter and improve the resulting image, this paper proposes to combine the Ant-Tree for Colour Quantisation algorithm with the Firefly algorithm.Computational experiments show that the new method generates better images than some popular colour quantisation methods such as Median-cut, Octree, Neuquant, Wu’s method or the Variance-based method.

An Effective Color Quantization Method Using Octree-Based Self-Organizing Maps

Color quantization is an essential technique in color image processing, which has been continuously researched. It is often used, in particular, as preprocessing for many applications. Self-Organizing Map (SOM) color quantization is one of the most effective methods. However, it is inefficient for obtaining accurate results when it performs quantization with too few colors. In this paper, we present a more effective color quantization algorithm that reduces the number of colors to a small number by using octree quantization. This generates more natural results with less difference from the original image. The proposed method is evaluated by comparing it with well-known quantization methods. The experimental results show that the proposed method is more effective than other methods when using a small number of colors to quantize the colors. Also, it takes only 71.73% of the processing time of the conventional SOM method.

Multiobjective Image Color Quantization Algorithm Based on Self-Adaptive Hybrid Differential Evolution.

In recent years, some researchers considered image color quantization as a single-objective problem and applied heuristic algorithms to solve it. This paper establishes a multiobjective image color quantization model with intracluster distance and intercluster separation as its objectives. Inspired by a multipopulation idea, a multiobjective image color quantization algorithm based on self-adaptive hybrid differential evolution (MoDE-CIQ) is then proposed to solve this model. Two numerical experiments on four common test images are conducted to analyze the effectiveness and competitiveness of the multiobjective model and the proposed algorithm.

A Hybrid Color Quantization Algorithm Incorporating a Human Visual Perception Model

Color quantization is a common image processing technique where full color images are to be displayed using a limited palette of colors. The choice of a good palette is crucial as it directly determines the quality of the resulting image. Standard quantization approaches aim to minimize the mean squared error (MSE) between the original and the quantized image, which does not correspond well to how humans perceive the image differences. In this article, we introduce a color quantization algorithm that hybridizes an optimization scheme based with an image quality metric that mimics the human visual system. Rather than minimizing the MSE, its objective is to maximize the image fidelity as evaluated by S‐CIELAB, an image quality metric that has been shown to work well for various image processing tasks. In particular, we employ a variant of simulated annealing with the objective function describing the S‐CIELAB image quality of the quantized image compared with its original. Experimental results based on a set of standard images demonstrate the superiority of our approach in terms of achieved image quality.

URINE COLOR AUTOMATIC IDENTIFICATION DEVICE BASED ON MICROCONTROLLER FRAMEWORK AND COLOR QUANTIZATION ALGORITHM

Urine color is an indicator of health status, especially for patients undergoing medical intervention treatment of urinary catheterization. Urinary tract infections are the most frequently developed infections among patients receiving treatment in medical institutions. Such infections can be detected from urine color, like in the case of the purple urine bag syndrome. However, it is a difficult task for non-nursing care staff and even the nursing staff to correctly conduct naked-eye identification without proper tools. To better assist both nursing and non-nursing care staff with the detection of infection signs in urine bag patients, a urine color automatic identification device has been developed. The device is based on microcontroller framework and color quantization algorithm. A hybrid color quantization algorithm and two features were proposed to identify the urine color. The identified color, as query data instead of human-described color keyword, can be used to retrieve the information from the database and then find possible symptoms for early warning. Instead of the nursing staff, the device can automatically identify the patient's urine color. From experimental results, the device with the proposed algorithm shows its capability and feasibility of the urine color automatic identification.

Soft computing-based colour quantisation

Soft computing techniques have shown much potential in a variety of computer vision and image analysis tasks. In this paper, an overview of recent soft computing approaches to the colour quantisation problem is presented. Colour quantisation is a common image processing technique to reduce the number of distinct colours in an image. Those selected colours form a colour palette, while the resulting image quality is directly determined by the choice of colours in the palette. The use of generic optimisation techniques such as simulated annealing and soft computing-based clustering algorithms founded on fuzzy and rough set ideas to formulate colour quantisation algorithms is discussed. These methods are capable of deriving good colour palettes and are shown to outperform standard colour quantisation techniques in terms of image quality. Furthermore, a hybrid colour quantisation algorithm which combines a generic optimisation approach with a common clustering algorithm is shown to lead to improved image quality. Finally, it is demonstrated how optimisation-based colour quantisation can be employed in conjunction with a more appropriate measure for image quality.

Improved block based segmentation algorithm for compression of compound images

Compression and transmission of compound images are essential processes in real time applications where there is a necessity that the compression technique should attain high compression ratio, low complexity, high PSNR value and a required level of security. This paper proposes a novel method for block-based compound image compression. The pictorial blocks and text/graphics blocks are separated from the compound image. The pictorial blocks are compressed using discrete Haar wavelet transformation and text/graphics colors are mapped to primary colors using color quantization algorithm and the resulting index values are then compressed using the lossless Huffman coding technique. The compressed image is then encrypted using an Advanced Encryption Standard algorithm which ensures that the transmission is fast and highly secured. Experimental results conclude that the proposed algorithm provides better results like low complexity, high compression ratio and a high PSNR value than most of the other image compression techniques.

Lossless Compression of Dithered Images

In order to display high-bit resolution images on low-bit resolution displays, bit resolution needs to be reduced. This problem is vital especially for low-cost or small (mobile) devices. To untangle the bit reduction problem, special color quantization algorithms, called dithering, are employed on high-bit resolution images. The dithering process helps to remedy the problem, but it does not help much in terms of storage and transmission of images. To reduce storage needs and lower data transmission, numerous special compression techniques have been proposed in the last several decades. While the well-known compression algorithms, such as gzip, help lower image file sizes, usually, they are not adequate. To improve the compression gain, special compression techniques that take into account structure of image data must be developed. In this paper, we show that, when the pseudo-distance technique (PDT) is used for dithered images, it yields better compression results than GIF and PNG.

Structural Similarity Measure for Color Images

images reveal more meaningful information to the human observers rather than grayscale ones. Regardless of the advantages of the existing well-known objective image quality measures, one of the common and major limitations of these measures is that they evaluate the quality of grayscale images only and don't make use of color information. In this paper we propose an improved method for image quality assessment that adds a color comparison to the criteria of the well-known Multiscale Structural Similarity index (MSSIM). We evaluated the new color image quality measure through human subjective experiments. Our human subjective evaluation data contains 25 reference images and 875 test images produced by five popular color quantization algorithms. Each of the quantized images was evaluated by twenty two subjects and more than 19200 individual human quality judgments were carried out to obtain the final mean opinion scores. We also tested the proposed method on TID2008 image database to further verify our results. These results indicate that adding color comparison improves MSSIM for many distortions in TID2008 and for assessing quantized images in our database. Keywordsquality assessment, Structural similarity index, Color quantization.