Color Quantization Research Articles

Color Quantization Based Artificial Bee Colony And Training Tools

The new method was proposed in this study, a new method was proposed. The method includes a series of steps. At the beginning of its steps creating k colors by using a clustering algorithm for determining a group of K colors starting from X colors, which are determined randomly by an algorithm k-means, and then comes the method of the artificial bee colony for finding the best colors by comparing the colors suggested in the previous step with the colors resulting from this algorithm for completing the replacement process and issuing the final color palette that depends on it to find the quantized image, and the third step involves using the hybrid approach (K-mean & ABC algorithm) to obtain quantitative images. Thus, It is necessary to have a very quality color palette to rely on it for extracting the desired image, it was found that the outcomes of presented method are able to create quantitative images in less time and of high quality in both metrics results and visual results

A Comparative Study of Evolutionary Computation and Swarm-Based Methods Applied to Color Quantization

A Comparative Study of Evolutionary Computation and Swarm-Based Methods Applied to Color Quantization

K-Means image segmentation using Mumford–Shah model

k-Means (KM) is well known for its ease of implementation as a clustering technique. It has been applied for color quantization in RGB, YUV, hyperspectral image, Lab, and other spaces, but this leads to fragmented segments as the pixels are clustered only in the color space without considering connectivity. The problem has been attacked by adding connectivity constraints, or using joint color and spatial features (r, g, b, x, y), which prevent fragmented and nonconvex segments. However, it does not take into account the complexity of the shape itself. The Mumford–Shah model has been earlier used to overcome this problem but with slow and complex mathematical optimization algorithms. We integrate Mumford–Shah model directly into KM context and construct a fast and simple implementation of the algorithm. The proposed approach uses standard KM algorithm with distance function derived from Mumford–Shah model so that it optimizes both the content and the shape of the segments jointly. We demonstrate by experiments that the proposed algorithm provides better results than comparative methods when compared using various error evaluation criteria. The algorithm is applied on 100 images in the Weizmann dataset and two remote sensing images.

Weighted PCA-LDA Based Color Quantization Method Suppressing Saturation Decrease

Weighted PCA-LDA Based Color Quantization Method Suppressing Saturation Decrease

Particulars and Pixels: Quantizing and Theorizing Color in William Blake's Illuminated Books

ABSTRACT William Blake printed and re-printed his illuminated books many times over his lifetime, using watercolors to make both slight and significant alterations to each of his plates. Built by the Yale DHLab, BlakeTint is a new digital humanities tool that traces changes in color across Blake's oeuvre, using color quantization to extract proportional color palettes for each of his plates and producing several visualizations of this data. Resting at the intersection of book history and digital humanities, this paper studies Blake's color theory from multiple angles—comparing his theoretical writings on color to his actual practice, scrutinizing color-terms in his poetry, and analyzing BlakeTint's data to understand Blake's evolving relationship with and mythology of color.

K-bMOM: A robust Lloyd-type clustering algorithm based on bootstrap median-of-means

The median-of-means is an estimator of the mean of a random variable that has emerged as an efficient and flexible tool to design robust learning algorithms with optimal theoretical guarantees. However, its use for the clustering task suggests dividing the dataset into blocks, which may provoke the disappearance of some clusters in some blocks and lead to bad performances. To overcome this difficulty, a procedure termed “bootstrap median-of-means” is proposed, where the blocks are generated with a replacement in the dataset. Considering the estimation of the mean of a random variable, the bootstrap median-of-means has a better breakdown point than the median-of-means if enough blocks are generated. A clustering algorithm called K-bMOM is designed, by performing Lloyd-type iterations together with the use of the bootstrap median-of-means strategy. Good performances are obtained on simulated and real-world datasets for color quantization and an emphasis is put on the benefits of our robust intialization procedure. On the theoretical side, K-bMOM is also proven to have a non-trivial probabilistic breakdown point in well-clusterizable situations.

Evaluation of quality measures for color quantization

Visual quality evaluation is one of the challenging basic problems in image processing. It also plays a central role in the shaping, implementation, optimization, and testing of many methods. The existing image quality assessment methods focused on images corrupted by common degradation types while little attention was paid to color quantization. This in spite there is a wide range of applications requiring color quantization assessment being used as a preprocessing step when color-based tasks are more efficiently accomplished on a reduced number of colors. In this paper, we propose and carry-out a quantitative performance evaluation of nine well-known and commonly used full-reference image quality assessment measures. The evaluation is done by using two publicly available and subjectively rated image quality databases for color quantization degradation and by considering suitable combinations or subparts of them. The results indicate the quality measures that have closer performances in terms of their correlation to the subjective human rating and show that the evaluation of the statistical performance of the quality measures for color quantization is significantly impacted by the selected image quality database while maintaining a similar trend on each database. The detected strong similarity both on individual databases and on databases obtained by integration provides the ability to validate the integration process and to 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 suitable quality measures for color quantization and to improve their future employment.

A hybrid method for reduction of size and number of hues in the color images used in a carpet map

AbstractThe carpet industry is no longer a small business done in the villages on a small scale; instead, it has carved an altogether different space, identity, and appreciation for itself in the cosmopolitan world. As computers are is becoming more and more ubiquitous, most industries, including the carpet industry, use computers for quality improvement, accuracy enhancement, speed development, and cost reduction purposes. Unlike traditional carpet maps, many modern maps include images of human faces for hand‐woven carpet tableau. These digital images comprise millions of colors and thousands of pixels, making it practically impossible to construct and weave the carpet in the same dimensions. Many weavers currently use manual and experience‐based methods for reducing the size and number of hues for making a hand‐woven carpet tableau map. Therefore, the outcomes are not the optimal results and can be improved. Also, many color reduction methods do not focus on the hand‐woven carpet tableau map. To overcome these problems and gaps, this research focuses on proposing a new automatic method for reducing the size of color images without compromising facial nuances, lessening the number of colors used while protecting the important areas of the images, and transforming those images into carpet tableau maps. The proposed approach inputs the original color image. It continuously detecting the face and specifying important areas, and finally, outputs carpet tableau map that is proportional to the given dimensions and color count. To evaluate the proposed method, MATLAB, as a powerful simulation tool, was employed. Final results are compared to the existing approaches in terms of face detection, size reduction, and color quantization. The obtained results have shown that the approach improves speed by 39% in face detection and increases the precision of size reduction and color quantization phases. The results have also confirmed that when images of human faces are reduced by a proposed method to form an appropriate image for tableau maps, they are nearly always perceived as more attractive than the reduced faces via traditional methods.

Analysis of Relation between the Purity of Honey and Its Physical Properties

Public awareness of the benefits of honey has increased the amount of honey consumption. This encourages the circulation of impure honey in the market by irresponsible traders. Therefore, characteristic information of honey is needed as a basis for honey purity testing methods. Physical characteristics analyzed in this study are the refractive index, permittivity, density and color quantization of honey. In general, it can be seen that the higher the concentration of honey, the higher the refractive index and its density and inversely proportional to the permittivity. Color analysis also shows that concentration is inversely proportional to the color intensity level.

Image Retrieval using Hybrid Order Dither Block Truncation Code

A new approach is proposed to index images in database using features generated from the HODBTC compressed data stream. This indexing technique can be extended for CBIR. HODBTC compresses an image into a set of color quantizers and a bitmap image. The proposed image retrieval system generates two image features namely CCF and BPF from the minimum quantizer, maximum quantizer and bitmap image respectively by involving the visual codebook.

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.

Classification of color features in butterflies using the Support Vector Machine (SVM)

Research in digital images is expanding widely and includes several sectors. One sector currently being carried out research is in insects; specifically, butterflies are used as a dataset. A total of 890 types of butterflies divided into ten classes were used as a dataset and classified based on color. Ten types of butterflies include Danaus plexippus, Heliconius charitonius, Heliconius erato, Junonia coenia, Lycaena phlaeas, Nymphalis antiopa, Papilio cresphontes, Pieris rapae, Vanessa atalanta, Vanessa cardui. The process of extracting color features on butterfly wings uses the RGB method to become HSV color space with color quantization (CQ). The purpose of adding CQ is that the computation process is carried out faster without reducing the image's information. In the color feature extraction process, the image is converted into 3-pixel sizes and normalized. The process of normalizing the dataset has the aim that the value ranges in the dataset have the same value. The 890 butterfly dataset was classified using the Support Vector Machine (SVM) method. Based on this research process, the accuracy of the 256x160 pixel size is 72%, the 420x315 pixel is 75%, and the 768x576 pixel is 75%. The test results on a system with a 768x576 pixel get the highest results with a precision value of 74.6%, a recall of 72%, and an f-measure of 73.2%
 Keywords—image processing; classification; butterflies; color features; features extraction

Skin Cancer Classification Through Quantized Color Features and Generative Adversarial Network

Early interpretation of skin cancer through computer-aided diagnosis (CAD) tools reduced the intricacy of the treatments as it can attain a 95% recovery rate. To frame up with computer-aided diagnosis system, scientists adopted various artificial intelligence (AI) designed to receive the best classifiers among these diverse features. This investigation covers traditional color-based texture, shape, and statistical features of melanoma skin lesion and contrasted with suggested methods and approaches. The quantized color feature set of 4992 traits were pre-processed before training the model. The experimental images have combined images of naevus (1500), melanoma (1000), and basal cell carcinoma (500). The proposed methods handled issues like class imbalanced with generative adversarial networks (GAN). The recommended color quantization method with synthetic data generation increased the accuracy of the popular machine learning models as it gives an accuracy of 97.08% in random forest. The proposed model preserves a decent accuracy with KNN, adaboost, and gradient boosting.

A Refined Structure Preserving Image Abstraction Framework as a Pre-Processing Technique for Desire Focusing on Prominent Structure and Artistic Stylization

This work identifies the strong dominant features by its location and extracts the image features for the purpose of automatic desire focusing on prominent structure and artistic stylization of images. At the pre-processing level, dataset image is treated using refined structure preserving image abstraction framework which can deliver the best effectual structure preserved abstracted results by utilizing visual attributes from 2D color image. The presented framework efficiently conserves the structural characteristics in the foreground of an input image by exhaustively amalgamate the series of non-photorealistic rendering image filters over meticulous investigational work and it also reduces the background substance of an image. The framework assesses image and object space details to generate structure preserved image abstraction thus distinguishing the accentuated elements of an enhanced structures using Harris key-point feature detector and chooses the 100 major unique dominant feature locations among available features. This work automatically selects the unique location from the extracted features using polynomial region of interest and unselected image regions and its background are blurred using Gaussian motion blurring with point spread function. Deblurring the selected region using wiener filtering to get the desire focusing on prominent structure followed by color quantization and flow-based bilateral filtering is applied over focused structural region to achieve artistic stylization. Efficiency of the framework has been validated by carrying out the trials on the selected Flickr repository, David Mould and Ruixing Wang dataset. In addition, user’s visual opinion and the image quality estimation methods were also utilized to appraise the proposed pre-processing framework. This work lists the structure preserving image abstraction framework applications, limitation, execution difficulties and future work in the field of Non-photorealistic rendering domain.

A genetic algorithm approach for image representation learning through color quantization

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%.

An Efficient Palette Generation Method for Color Image Quantization

This article describes an efficient method to generate a color palette for color image quantization. The method consists of two stages. In the first stage, the initial palette is generated. Initially, the color palette is an empty set. First, the N colors are generated according to the data distribution of the input image in the RGB (Red, Green, Blue) color space. Then, one color is selected from the N colors and this color is added to the initial palette, and the step is repeated until the color number of the initial palette is equal to K. In the second stage, the quantized image is generated using the fast K-means algorithm. There are many sampling rates used in this study. For each sampled pixel, a fast searching method is employed to efficiently determine the closest color in the palette. Experimental results show that the high-quality quantized images can be generated by the proposed method. When the sampling rate equals 0.125, the computation time of the proposed method is less than 0.3 s for all cases.

RETRACTED: Simulation of oil painting art design based on 5 G mobile network and dynamic image sampling

Color quantization is the first critical step in automatic generation of oil as the main research goals. Design and Application of oil which line of the display system, and constructing a set of oil-based platform must be adapted to the layout of the system rise before the mobile terminal device in response to the movement. Use MVC development framework. Front-end uses the latest technology HTMLS and Bootstrap framework, the system has an effective cross platform compatibility and provide useless consistent device experience. The system established control systems and content colleague role-based authority, assign the appropriate role of detailed character content, so the development of the business logic of the system more standardized. Finally, the feasibility of the system to test and demonstrate the development of the various functions and performance 5 G mobile network and a moving image is displayed in the sample line painting.

Gray Balance Adjusting in Electrophotography by Means of Discrete Geodesics of Gradation Surfaces

Gray balance is one of the key issues of color balance and color appearance adjustment during printing. It takes into account the overlap of two or more colorants to achieve a visually neutral tone. It works in parallel with the adjustment of the main color channels. In electrophotography, the setting of color channels separately is usually carried out using tone reproduction curves. However, this traditional approach does not take into account the effect of changes in shades caused by their overlapping, We previously suggested using gradation trajectories (an analogue of tone reproduction curves in CIE Lab space) as an alternative paradigm for color channel adjustment. Further, we expanded the idea to the gradation surfaces as an expression of binary overlays. We proposed both analytic and discrete approaches to the computation of gradation trajectories and surfaces. The computations use the mathematical apparatus of differential geometry of spatial curves and surfaces. Discretization uses color quantization in digital printing devices and means that color coordinates are considered as continuous functions of filling a discrete raster cell with dyes. The color space metric is determined by the value of the CIE dE color difference. As gradation trajectories, we apply the discrete type of geodesic lines on the gradation surfaces of the corresponding binaries. In this work, we suggest using gradation surfaces in discrete form as a mathematical model of double dye overlays to adjust the gray balance in electrophotography.

Improved Exemplar based Image Inpainting for Partial Instance Occlusion Handling with K-means Clustering and YCbCr Color Space

The images acquired in real time outdoor environment are often subject to uneven illumination conditions, cloudy weather, lighting conditions. The instances of partial occlusions deteriorate the background modeling of such scenes. Varying illumination outdoor scene set of images with partial occlusions are addressed through this investigative work. There is a need for a restoration method that finds improved subjective perception and execution time. The proposed work focuses on novel amended exemplar model to improve the subjective perception. The exemplar inpainting method is improved through the color quantization with K-means clustering approach in YCbCr color space. Experimental validations and proposed method results show better improvement in qualitative and objective measures than existing methods. The average “Peak Signal to Noise Ratio (PSNR)” as 28.2869 and “Structural SIMilarity index (SSIM)” as 0.9759 of the proposed method has shown better results respectively both visually and with a tradeoff in time.

Reduced Reference Perceptual Quality Model With Application to Rate Control for Video-Based Point Cloud Compression.

In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bitrate. One of the main challenges of this approach is to define a quality measure that can be computed with low computational cost and which correlates well with the perceptual quality. While several quality measures that fulfil these two criteria have been developed for images and videos, no such one exists for point clouds. We address this limitation for the video-based point cloud compression (V-PCC) standard by proposing a linear perceptual quality model whose variables are the V-PCC geometry and color quantization step sizes and whose coefficients can easily be computed from two features extracted from the original point cloud. Subjective quality tests with 400 compressed point clouds show that the proposed model correlates well with the mean opinion score, outperforming state-of-the-art full reference objective measures in terms of Spearman rank-order and Pearson linear correlation coefficient. Moreover, we show that for the same target bitrate, rate-distortion optimization based on the proposed model offers higher perceptual quality than rate-distortion optimization based on exhaustive search with a point-to-point objective quality metric. Our datasets are publicly available at https://github.com/qdushl/Waterloo-Point-Cloud-Database-2.0.