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Urban scenic forests provide urban residents with various physical and mental health and wellbeing services. However, in the research on the color quantification and health services of scenic forests, it is still unclear how their color quantification characteristics feedback on the stress - attention of the adolescent group. In this study, visual color elements (green, red and yellow) of three landscape forests were used to generate 48 images of four groups of urban landscape forests according to color combinations and proportions. Virtual images were used to assess the stress indicators and attention indicators of participants before and after viewing. The results showed that the four groups of experimental groups showed varying degrees of α wave reduction and β / α ratio increase after viewing, G1 group showed an extremely significant increase in β wave after image stimulation, G1 and G3 group significantly decreased θ / β power, G4 group and G5 control group had no significant change. Among the 16 channels of absolute α wave and absolute β wave in the brain, the F3 and F4 channels corresponding to the frontal lobe of the G3 group showed the most prominent consistency of β wave in the frontal cortex during highly concentrated mental activities. Our study shows that positive EEG (Electroencephalogram) feedback of arousal and attention recovery can be obtained by using monochromatic or two-color changing color features in urban landscape forest. This study can provide references and methods for urban forest planning, design and visual evaluation. [ABSTRACT FROM AUTHOR]

Introduction. Lossy image compression algorithms play a crucial role in various domains, including graphics, and image processing. As image information density increases, so do the resources required for processing and transmission. One of the most prominent approaches to address this challenge is color quantization, proposed by Orchard et al. (1991). This technique optimally maps each pixel of an image to a color from a limited palette, maintaining image resolution while significantly reducing information content. Color quantization can be interpreted as a clustering problem (Krishna et al. (1997), Wan (2019)), where image pixels are represented in a three-dimensional space, with each axis corresponding to the intensity of an RGB channel. The purpose of the paper. Scaling of traditional algorithms like K-Means can be challenging for large data, such as modern images with millions of colors. This paper reframes color quantization as a three-dimensional stochastic transportation problem between the set of image pixels and an optimal color palette, where the number of colors is a predefined hyperparameter. We employ Stochastic Quantization (SQ) with a seeding technique proposed by Arthur et al. (2007) to enhance the scalability of color quantization. This method introduces a probabilistic element to the quantization process, potentially improving efficiency and adaptability to diverse image characteristics. Results. To demonstrate the efficiency of our approach, we present experimental results using images from the ImageNet dataset. These experiments illustrate the performance of our Stochastic Quantization method in terms of compression quality, computational efficiency, and scalability compared to traditional color quantization techniques. Conclusions. This study introduces a scalable algorithm for solving the color quantization problem without memory constraints, demonstrating its efficiency on a subset of images from the ImageNet dataset. The convergence speed of the algorithm can be further enhanced by modifying the update rule with alternative methods to Stochastic Gradient Descent (SGD) that incorporate adaptive learning rates. Moreover, the stochastic nature of the proposed solution enables the utilization of parallelization techniques to simultaneously update the positions of multiple quants, potentially leading to significant performance improvements. This aspect of parallelization and its impact on algorithm efficiency presents a topic for future research. The proposed method not only addresses the limitations of existing color quantization techniques but also opens up new possibilities for optimizing image compression algorithms in resource-constrained environments.

Copyright of Laser Technology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Urban scenic forests provide urban residents with various physical and mental health and wellbeing services. However, in the research on the color quantification and health services of scenic forests, it is still unclear how their color quantification characteristics feedback on the stress - attention of the adolescent group. In this study, visual color elements (green, red and yellow) of three landscape forests were used to generate 48 images of four groups of urban landscape forests according to color combinations and proportions. Virtual images were used to assess the stress indicators and attention indicators of participants before and after viewing. The results showed that the four groups of experimental groups showed varying degrees of α wave reduction and β/α ratio increase after viewing, G1 group showed an extremely significant increase in β wave after image stimulation, G1 and G3 group significantly decreased θ/β power, G4 group and G5 control group had no significant change. Among the 16 channels of absolute α wave and absolute β wave in the brain, the F3 and F4 channels corresponding to the frontal lobe of the G3 group showed the most prominent consistency of β wave in the frontal cortex during highly concentrated mental activities. Our study shows that positive EEG (Electroencephalogram) feedback of arousal and attention recovery can be obtained by using monochromatic or two-color changing color features in urban landscape forest. This study can provide references and methods for urban forest planning, design and visual evaluation.

The color palette of traditional dwellings is intricately shaped by the characteristics of the geographical environment and the long-standing lifestyle of residents, thus constituting a vital component of rural culture. However, the rapid development in recent years has resulted in the dilapidation of rural features, including haphazard colors employed in buildings. With the advancement of science and technology, the studies of color have transitioned from a qualitative to a quantitative approach. Through a comprehensive comparison of RGB, HSB, CMYK, and Lab color spaces, as well as an evaluation of color card comparison method, software analysis method, and instrument measurement method; this paper reveals that employing the instrument measurement method in conjunction with the Lab color space enables independent assessment free from subjective influence. This approach facilitates more objective and accurate collection of color data while ensuring seamless and lossless transmission between devices. Therefore, to safeguard and enhance the authenticity of traditional dwellings and preserve the architectural style of traditional villages, this paper focuses on investigating the color palette of traditional dwellings in the Jianxi River Basin. The CM-700d spectrophotometer is employed for color data collection, while quantitative analysis is conducted using the Lab color space to unravel the color logics in this region. The findings reveal that earth yellow (average Lab (50.35,7.40,19.63)) constitutes the main color of rammed earth walls, complemented by dark brown (average Lab (38.72,2.97,8.58)) tiled roofs and gray-brown (average Lab (41.95,4.30,12.75)) stone wall bases as accent color; additionally, gray-yellow (average Lab (49 .82,1 .93,10.98)) brick gatehouses serve as accent colors. The integration between these colors and nature is harmonious; however, the absence of distinct color layers and highlights in the traditional dwellings necessitates incorporating bright architectural decorations along with arrangements of flowering plants. This paper conducted a quantitative analysis on the color of traditional dwellings with three main objectives. Firstly, to establish a comprehensive color database that can effectively preserve and promote regional traditional dwelling colors, while aiding designers in capturing regional color characteristics. Secondly, by collecting precise color data, this paper lays the groundwork for future applications of digital color technology. Lastly, it aims to explore the regional color logic and its potential integration with future studies on color evaluation to uncover preferred color combinations among the public for dwellings.

The landscape color of an urban park is the carrier of urban culture and the embodiment of urban style since it is an advanced form resulting from urban growth. It is expected to offer some theoretical references for the color design of urban parks to quantify the color features of urban parks in various seasons and the variations in people's perceptions of parks. This study used People's Park and Huanhuanxi Park in Chengdu as research objects. It used the K-means clustering algorithm to quantify the seasonal characteristics of spatial color changes of different landscape types, and it used the SD. method to examine how people perceived the landscape color in the two parks during various seasons. The findings indicate that N (no color) and G (green) are People's Park's and Huanhuaxi Park's base colors. Architectural landscapes' use of color exhibits a trend toward medium saturation and medium-high brightness. Low saturation and low brightness are characteristics of the color of the pavement landscape. The landscape color in the sketch demonstrates the traits of wide hue dispersion, consistent saturation distribution, and brightness distribution. Seasonal change considerably impacts plant color, according to the color analysis of the natural landscape. According to the SD. evaluation results, spring had the most excellent crowd perception score, followed by summer and winter. Autumn had the highest crowd perception score. The landscape color recommendation chromatography of two urban parks is then put forth in conjunction with quantitative data and population perception evaluation, offering some reference guidelines for creating urban parks. [ABSTRACT FROM AUTHOR]

This article analyzes various color quantization methods using multiple image quality assessment indices. Experiments were conducted with ten color quantization methods and eight image quality indices on a dataset containing 100 RGB color images. The set of color quantization methods selected for this study includes well-known methods used by many researchers as a baseline against which to compare new methods. On the other hand, the image quality assessment indices selected are the following: mean squared error, mean absolute error, peak signal-to-noise ratio, structural similarity index, multi-scale structural similarity index, visual information fidelity index, universal image quality index, and spectral angle mapper index. The selected indices not only include the most popular indices in the color quantization literature but also more recent ones that have not yet been adopted in the aforementioned literature. The analysis of the results indicates that the conventional assessment indices used in the color quantization literature generate different results from those obtained by newer indices that take into account the visual characteristics of the images. Therefore, when comparing color quantization methods, it is recommended not to use a single index based solely on pixelwise comparisons, as is the case with most studies to date, but rather to use several indices that consider the various characteristics of the human visual system. [ABSTRACT FROM AUTHOR]

Color quantization (cq), the reduction of the number of distinct colors in a given image with minimal distortion, is a common image processing operation with various applications in computer graphics, image processing/analysis, and computer vision. The first cq algorithm, median-cut, was proposed over 40 years ago. Since then, many clustering algorithms have been applied to the cq problem. In this paper, we present a comprehensive overview of the cq algorithms proposed in the literature. We first examine various aspects of cq, including the number of distinguishable colors, cq artifacts, types of cq, applications of cq, data structures, data reduction, color spaces and color difference equations, and color image fidelity assessment. We then provide an overview of image-independent cq algorithms, followed by a detailed survey of image-dependent ones. After presenting a brief discussion of pixel mapping, we conclude our survey with an outline of the open problems in cq. [ABSTRACT FROM AUTHOR]

Computer vision is widely used in fabric texture recognition. In this paper, a new method based on double-sided fusion of reflection image and transmission image is proposed for recognition and analysis of fabric texture. The yarn location is obtained through transmission image, and fabric texture is obtained through reflection image. The position information of weave floats obtained by gray projection on the transmission image is given to the reflection image for classification and color recognition of weave floats. In the stage of weave float classification, an improved KNN algorithm based on FCM is proposed. First, FCM is used to cluster the HOG features of the two types of floats respectively to obtain new cluster centers, and then KNN is used for classification to obtain the weave patterns. In the stage of color recognition, the K-means clustering algorithm is used on a single weave float to obtain the color pattern. Based on the two attributes of a single point obtained from the above two patterns, a system of bidirectional error correction is designed. Experimental results show that this method effectively improves the recognition accuracy of yarn-dyed fabrics which interlaced yarns are different colors without color and texture disturbed to the greatest extent. [ABSTRACT FROM AUTHOR]

Abstract In this study, a novel color quantization approach which automatically estimates the number of colors by multi-level thresholding based on the histogram is proposed. The method consists of three stages. First, red–green–blue is clustered by threshold values. Thus, the pixels are positioned in a cluster or sub-prism. Second, the color palette is produced by determining the centroids of the clusters. Finally, the pixels are reassigned to clusters based on their distance from each centroid. The average of the pixels included in each cluster also represents the color of that cluster. While conventional methods are user-dependent, the proposed algorithm automatically generates the number of colors by considering the pixels assigned to the clusters. Additionally, the multi-level thresholding approach is also a solution to the initialization problem, which is another important issue for quantization. Consequently, the experimental results of the method tested with various images show better performance than many frequently used quantization techniques.

Conventional image compression techniques targeted for the perceptual quality are not generally optimized for classification tasks using deep neural networks (DNNs). To compress images for DNN inference tasks, recent studies have proposed task-centric image compression methods with quantization techniques optimized for DNN inference. Among them, color quantization was proposed to reduce the amount of data per pixel by limiting the number of distinct colors (color space) in an image. However, quantizing images into various color space sizes requires training and inference of multiple DNNs, each of which is dedicated to each color space. To overcome this limitation, we propose a scalable color quantization method, where images with variable color space sizes can be extracted from a master image generated by a single DNN model. This scalability is enabled by weighted color grouping that constructs a color palette using critical color components for the classification task. We also propose an adaptive training method that can jointly optimize images with various color-space sizes. The results show that the proposed method supports dynamic changes of the color space size between 1–6 bit color space per pixel, while even increasing the inference accuracy at a low bit precision up to 20.2% and 46.6% compared to other task- and human-centric color quantizations, respectively. [ABSTRACT FROM AUTHOR]

Ultraviolet (UV) induced biofluorescence is being discovered with increasing frequency across the tree of life. However, there is not yet a standardized, low-cost, photographic methodology used to document, quantify, and minimize sources of bias that often accompany reports made by researchers new to fluorescence imaging. Here, a technique is described to create accurate photographs of biofluorescent specimens as well as how to use these images to quantify fluorescence via color quantization, using open-source code that utilizes K-means clusters within the International Commission on Illumination L*a*b* (CIELAB) color space. The complexity of photographing different excitation and emission wavelengths and methods to reduce bias from illumination source and/or camera color sensitivity (e.g., white balance) without the use of modified equipment is also addressed. This technique was applied to preserved southern flying squirrel (Glaucomys volans) specimens to quantify the color shift between illumination under visible and UV light and analyze variation among specimens. This relatively simple methodology can be adapted to future studies across a range of fluorescent wavelengths and project goals, and assist future research in which unbiased photographs and analyses are key to understanding the physiological and ecological role of biofluorescence.

Image retrieval is defined as indexing similar or identical images in a digital image database. Various feature vectors obtained from the images are used while searching for a similar digital image. However, processing all pixels of the images requires costly algorithms. In addition, it is a possible issue that the images used in retrieval approaches are of different sizes. For this reason, pixel-level operations are insufficient when comparing images. Therefore, it requires vectorial structures that represent images. The process of obtaining these vectorial structures is called feature extraction, and it is one of the most important stages of content-based image retrieval. On the other hand, the histogram is the most basic feature vector that is independent of the dimensions of the image and can be easily calculated. In gray-level images, the size of the histogram is suitable for use as a feature vector. However, three different channels in color images contain too much data to be used as feature vectors. The data of 3 separate histograms are reduced using various thresholding processes and feature vectors are extracted. Therefore, reducing the vector size is an inevitable operation. In this study, a new multi-thresholding method based on the Spiking Neural Network model, inspired by the human visual system, is proposed. With the proposed model, 3 threshold values are determined for each of the RGB color channels, and each color channel is divided into 4 parts. Thus, the color palette of the image is quantized to 64 different colors and a feature vector with 64 elements is obtained. The proposed method was compared with the commonly used multilevel thresholding methods. The results obtained showed that the proposed method is quite successful. [ABSTRACT FROM AUTHOR]

We propose three methods for the color quantization of superpixel images. Prior to the application of each method, the target image is first segmented into a finite number of superpixels by grouping the pixels that are similar in color. The color of a superpixel is given by the arithmetic mean of the colors of all constituent pixels. Following this, the superpixels are quantized using common splitting or clustering methods, such as median cut, k-means, and fuzzy c-means. In this manner, a color palette is generated while the original pixel image undergoes color mapping. The effectiveness of each proposed superpixel method is validated via experimentation using different color images. We compare the proposed methods with state-of-the-art color quantization methods. The results show significantly decreased computation time along with high quality of the quantized images. However, a multi-index evaluation process shows that the image quality is slightly worse than that obtained via pixel methods. [ABSTRACT FROM AUTHOR]

This research analyzed the relevant practices in the color composition of packaging and its color psychology for soft drink and spirits products in the world‐renowned packaging design competitions. The packaging color characteristics of the two types of products were analyzed using a quantitative analysis‐based approach (using HSV color model), supplemented by qualitative analysis. The colors of 534 drink packages were analyzed quantitatively, and the three main components of soft drinks and spirits packaging were evaluated in terms of hue, saturation, and value. The color ranges and characteristics of soft drinks and spirits were summarized and compared. The results showed that the vast majority of both products used multi‐color packaging. Soft drink packaging is mostly colored with high value of red, orange, and yellow hue areas, while spirits packaging mostly uses low‐saturation colors. In addition, the average saturation (42.44%) and average value (78.18%) of the main color of soft drink packaging were higher than the average saturation (25.94%) and average value (57.55%) of spirits. Soft drink products are mostly packaged in white or warm colors to gain consumers' attention, while spirits are packaged in black to highlight the high quality and taste. This study shows that the color composition of soft drink and spirits packaging is clearly different. It also reflects that specific colors have a specific psychological meaning, thus causing people's psychological resonance and recognition, which has positive significance for further research on the influence of color psychology on product packaging. [ABSTRACT FROM AUTHOR]

Color quantization (CQ) is a fixed-rate vector quantization developed for color images to reduce their number of distinct colors while keeping the resulting distortion to a minimum. Various clustering algorithms have been adapted to the CQ problem over the past 40 years. Among these, hierarchical algorithms are generally more efficient (i.e., faster), whereas partitional ones are more effective (in minimizing distortion). Among the partitional algorithms, the effectiveness and efficiency of the Lloyd (or batch) k -means algorithm have been shown by multiple recent studies. We investigate an alternative, lesser-known k -means algorithm proposed by Jancey, which differs from Lloyd k -means (LKM) in the way it updates the cluster centers at the end of each iteration. To obtain a competitive color quantizer, we develop a weighted variant of Jancey k -means (JKM) and then accelerate the weighted algorithm using the triangle inequality. Through extensive experiments on 100 color images, we demonstrate that, with the proposed modifications, JKM outperforms LKM significantly in terms of efficiency without sacrificing effectiveness. In addition, the proposed JKM-based color quantizer is as straightforward to implement as the popular LKM color quantizer. [ABSTRACT FROM AUTHOR]

The visual quality evaluation is one of the fundamental challenging problems in image processing. It plays a central role in the shaping, implementation, optimization, and testing of many methods. The existing image quality assessment methods centered mainly on images altered by common distortions while paying little attention to the distortion introduced by color quantization. This happens despite there is a wide range of applications requiring color quantization as a preprocessing step since many color-based tasks are more efficiently accomplished on an image with a reduced number of colors. To fill this gap, at least partially, we carry out a quantitative performance evaluation of nine currently widely-used full-reference image quality assessment measures. The evaluation runs on two publicly available and subjectively rated image quality databases for color quantization degradation by considering their appropriate combinations and subparts. The evaluation results indicate what are the quality measures that have closer performances in terms of their correlation to the subjective human rating and prove that the selected image database significantly impacts the evaluation of the quality measures, although a similar trend on each database is maintained. The detected strong trend similarity, both on individual databases and databases obtained by a proper combination, provides the ability to validate the database combination process and consider the quantitative performance evaluation on each database as an indicator for performance on the other databases. The experimental results are useful to address the choice of appropriate quality measures for color quantization and to improve their future employment. [ABSTRACT FROM AUTHOR]

Over the last decades, hand-crafted feature extractors have been used to encode image visual properties into feature vectors. Recently, data-driven feature learning approaches have been successfully explored as alternatives for producing more representative visual features. In this work, we combine both research venues, focusing on the color quantization problem. We propose two data-driven approaches to learn image representations through the search for optimized quantization schemes, which lead to more effective feature extraction algorithms and compact representations. Our strategy employs Genetic Algorithm, a soft-computing apparatus successfully utilized in Information-retrieval-related optimization problems. We hypothesize that changing the quantization affects the quality of image description approaches, leading to effective and efficient representations. We evaluate our approaches in content-based image retrieval tasks, considering eight well-known datasets with different visual properties. Results indicate that the approach focused on representation effectiveness outperformed baselines in all tested scenarios. The other approach, which also considers the size of created representations, produced competitive results keeping or even reducing the dimensionality of feature vectors up to 25%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article presents a color quantization technique that combines two previously proposed approaches: the Binary splitting method and the Iterative ant-tree for color quantization method. The resulting algorithm can obtain good quality images with low time consumption. In addition, the iterative nature of the proposed method allows the quality of the quantized image to improve as the iterations progress, although it also allows a good initial image to be quickly obtained. The proposed method was compared to 13 other color quantization techniques and the results showed that it could generate better quantized images than most of the techniques assessed. The statistical significance of the improvement obtained using the new method is confirmed by applying a statistical test to the results of all the methods compared. [ABSTRACT FROM AUTHOR]

Color quantization (CQ) is an important operation with many applications in computer graphics and image processing and analysis. Clustering algorithms have been extensively applied to this problem. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much attention in the CQ literature because of its high computational requirements and sensitivity to initialization. In this paper, we propose a novel CQ method based on an online k-means formulation due to MacQueen. The proposed method utilizes adaptive and efficient cluster center initialization and quasirandom sampling to attain deterministic, high speed, and high-quality quantization. Experiments on a diverse set of publicly available images demonstrate that the proposed method is significantly faster than the more common batch k-means formulation due to Lloyd while delivering nearly identical results. [ABSTRACT FROM AUTHOR]

Reversible color-to-grayscale conversion (RCGC) is a method that embeds the chromatic information of a full color image into its grayscale version such that the original color image can be reconstructed in the future when necessary. In practical applications, it is required to provide a means to authenticate an information-embedded image such that its integrity can be guaranteed. However, none of the current RCGC algorithms take this factor into account. In this paper, to address this issue, we develop an information-embedding framework based on a vector quantization-based (VQ-based) RCGC algorithm recently proposed by us. Under this framework, we propose a RCGC algorithm that can embed both chromatic information and fragile watermark simultaneously into a grayscale image with the same technique to reduce the complexity and improve the efficiency. Like other VQ-based RCGC algorithms, the performance of the proposed RCGC algorithm highly relies on the palette it uses. We also propose a palette generation algorithm in this paper to support the information embedding process such that the visual quality of the color-embedded grayscale images and the reconstructed color images can be significantly improved. [ABSTRACT FROM AUTHOR]

This paper analyzes the effects of color quantization on standard JPEG compression. Optimized color palettes were used to quantize natural images, using dithering and chroma subsampling as optional. The resulting variations on file size and quantitative quality measures were analyzed. Preliminary results, using a small image database, show that file size suffered an average 20% increase and a concomitant loss in quality was perceived (− 6dB PSNR, − 0.16 SSIM and − 9.6 Butteraugli). Color quantization present itself as an ineffective tool on JPEG compression but if necessarily imposed, on high quality compressed images, it might lead to a negligible increase in data size and quality loss. In addition dithering seems to always decrease JPEG compression ratio. [ABSTRACT FROM AUTHOR]

This article proposes a color quantization strategy that combines two color quantization methods: Binary Splitting and Ant tree for Color Quantization. This solution combines a splitting method, which is faster, and a clustering-based method, which generates better quantized images. Given that time is a fundamental factor when considering a method for real-time applications, the proposed strategy attempts to exploit both of these methods for obtaining good quantized images with a low computational cost. The result of this approach not only generates better images than when Binary Splitting and Ant tree for Color Quantization are applied separately, but also helps to improve other methods frequently used for color quantization such as Wu's method, Octree, Variance-based method and Neuquant. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Color Quantization (CQ) is a process of reducing the number of colors in an image and has been widely used in image compression. Octree-based Color Quantization (OCQ) is considered to be one of the most popular CQ algorithms due to its high encoding efficiency, low memory usage, and good color palette selection. However, a serious challenge for OCQ applications is how to efficiently manage key local colors. In this paper, we propose an adaptive block-based octree color quantization (AB-OCQ) approach to overcome the challenge. Our results show that AB-OCQ can significantly improve the image quality in comparison to the traditional OCQ approach, owing to the proper treatment of some local colors. In image compression ratio, AB-OCQ has better comprehensive performance than OCQ. At the same time, compared with the popular image file format, AB-OCQ has the feature that it can randomly access the image pixel data while maintaining the compression ratio. This feature allows the application to store more image data under the same memory, which provides a way to increase the efficiency of application. [ABSTRACT FROM AUTHOR]

This paper proposes a color image segmentation method based on region salient color and the fuzzy C-means (FCM) algorithm. The method first uses the convex hull theory based on Harris corner detection to detect the object of the image. Thus, the object and the background can be separated. Then, the quantized color histogram can be studied in the HSV color space. By calculating the number of the peak values of both the object and the background histograms, the quantity of the regional salient colors can be obtained. The quantity is the number of the clustering centroids of FCM algorithm. Finally, the FCM algorithm and the noise correction algorithm can be used in the object and the background, respectively. The obtained segmented image consists of the object and the background segmentation. It proves that the method in this paper is an effective segmentation method based on the experiments made by use of Berkeley segmentation dataset. According to the experimental results, it can be concluded that the proposed algorithm has the highest segmentation accuracy and the shortest computing time among the algorithms mentioned in this paper. The algorithm can achieve high-quality, stable and accurate color image segmentation results. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A subset of colors often needs to be selected to represent a full set. In one such application, a multi‐band color sensor is used to measure reflective color samples, and a matrix transformation method is used to recover the reflectance spectrum of the measured sample. To achieve this, a group of training colors needs to be selected to calculate the transformation matrix. A genetic algorithm (GA) has been developed to optimize the selection of the subset of training colors, and the result is compared with those obtained using random selection or a traditional culling algorithm. In a simulation study, the GA gives better results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]