Color Quantization Research Articles

Local Parallel Cross Pattern: A Color Texture Descriptor for Image Retrieval.

Riding the wave of visual sensor equipment (e.g., personal smartphones, home security cameras, vehicle cameras, and camcorders), image retrieval (IR) technology has received increasing attention due to its potential applications in e-commerce, visual surveillance, and intelligent traffic. However, determining how to design an effective feature descriptor has been proven to be the main bottleneck for retrieving a set of images of interest. In this paper, we first construct a six-layer color quantizer to extract a color map. Then, motivated by the human visual system, we design a local parallel cross pattern (LPCP) in which the local binary pattern (LBP) map is amalgamated with the color map in “parallel” and “cross” manners. Finally, to reduce the computational complexity and improve the robustness to image rotation, the LPCP is extended to the uniform local parallel cross pattern (ULPCP) and the rotation-invariant local parallel cross pattern (RILPCP), respectively. Extensive experiments are performed on eight benchmark datasets. The experimental results validate the effectiveness, efficiency, robustness, and computational complexity of the proposed descriptors against eight state-of-the-art color texture descriptors to produce an in-depth comparison. Additionally, compared with a series of Convolutional Neural Network (CNN)-based models, the proposed descriptors still achieve competitive results.

QRCI: A new quantum representation model of color digital images

In this paper, a new quantum representation model of color digital images (QRCI) is investigated, in which the color information is encoded by the basis states of qubit sequences. QRCI model utilizes 2n+6 qubits to store a color digital image with size 2n×2n. Compared with the existing NCQI representation model, the storage capacity of QRCI improves 218 times. Moreover, some quantum color image processing operations concerning channels and bit-planes based on QRCI are discussed and their quantum circuits are designed. Comparison results of the quantum circuits indicate that these operations based on QRCI have lower quantum cost than NCQI. Therefore, the new proposed QRCI representation model can save more storage space and it is more convenient to conduct quantum color image processing operations concerning channels and bit-planes. This work will help the researchers to further investigate more complex quantum color image processing operations based on QRCI.

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.

Matrix Coding-Based Quantum Image Steganography Algorithm

Embedding secret information into quantum carrier image for covert communication is one of significant research fields of quantum secure communication. Using good imperceptibility and high embedding efficiency of matrix coding, this paper proposes a novel matrix coding-based quantum steganography algorithm for quantum color images. In order to better apply matrix coding in actual demand, two different embedding methods are proposed. One embedding method is single pixel-embedded (1, 3, 2) coding, called SPE (1, 3, 2) coding for short. This method embeds two qubits of secret information into three least significant qubits (LSQbs) of a single pixel of quantum carrier image, and at most only one LSQb would be changed. The other embedding method is the multiple pixels-embedded (1, 3, 2) coding, called MPsE (1, 3, 2) coding, in which three LSQbs of multiple carrier pixels are utilized to embed two secret qubits. On account of experimental simulation obtained in the MATLAB environment, it shows that the new algorithm has good performance in the imperceptibility, the security, the embedding efficiency, and the embedding capacity.

A Novel Color-Texture Descriptor Based on Local Histograms for Image Segmentation

In this paper, we propose a novel color-texture image segmentation method based on local histograms. Starting with clustering-based color quantization, we extract a sufficient number of representative colors. For each pixel, through counting the number of pixels with each representative color within a circular neighborhood, a local histogram is obtained. After the circular neighborhood is extended to several scales, a local histogram with an appropriate scale is adopted as a color-texture descriptor at the corresponding pixel for image segmentation. Further, we correct the color-texture features near boundaries and obtain a initial segmentation by a clustering method with the color-texture descriptors. Finally, in order to obtain a better segmentation result, we merge the over segmented regions guided by the obtained boundaries. Experiments are performed on both synthetic and natural color-texture images, and the results show that our proposed method performs much better compared with state-of-the-art methods on image segmentation, particularly in textured areas.

Contact-Less Real-Time Monitoring of Cardiovascular Risk Using Video Imaging and Fuzzy Inference Rules

Conventional methods for measuring cardiovascular parameters use skin contact techniques requiring a measuring device to be worn by the user. To avoid discomfort of contact devices, camera-based techniques using photoplethysmography have been recently introduced. Nevertheless, these solutions are typically expensive and difficult to be used daily at home. In this work, we propose an innovative solution for monitoring cardiovascular parameters that is low cost and can be easily integrated within any common home environment. The proposed system is a contact-less device composed of a see-through mirror equipped with a camera that detects the person’s face and processes video frames using photoplethysmography in order to estimate the heart rate, the breath rate and the blood oxygen saturation. In addition, the color of lips is automatically detected via clustering-based color quantization. The estimated parameters are used to predict a risk of cardiovascular disease by means of fuzzy inference rules integrated in the mirror-based monitoring system. Comparing our system to a contact device in measuring vital parameters on still or slightly moving subjects, we achieve measurement errors that are within acceptable margins according to the literature. Moreover, in most cases, the response of the fuzzy rule-based system is comparable with that of the clinician in assessing a risk level of cardiovascular disease.

Content-Based Color Image Retrieval Using Block Truncation Coding Based on Binary Ant Colony Optimization

In this paper, we propose a content-based image retrieval (CBIR) approach using color and texture features extracted from block truncation coding based on binary ant colony optimization (BACOBTC). First, we present a near-optimized common bitmap scheme for BTC. Then, we convert the image to two color quantizers and a bitmap image-utilizing BACOBTC. Subsequently, the color and texture features, i.e., the color histogram feature (CHF) and the bit pattern histogram feature (BHF) are extracted to measure the similarity between a query image and the target image in the database and retrieve the desired image. The performance of the proposed approach was compared with several former image-retrieval schemes. The results were evaluated in terms of Precision-Recall and Average Retrieval Rate, and they showed that our approach outperformed the referenced approaches.

Deep multispectral painting reproduction via multi-layer, custom-ink printing

We propose a workflow for spectral reproduction of paintings, which captures a painting's spectral color, invariant to illumination, and reproduces it using multi-material 3D printing. We take advantage of the current 3D printers' capabilities of combining highly concentrated inks with a large number of layers, to expand the spectral gamut of a set of inks. We use a data-driven method to both predict the spectrum of a printed ink stack and optimize for the stack layout that best matches a target spectrum. This bidirectional mapping is modeled using a pair of neural networks, which are optimized through a problem-specific multi-objective loss function. Our loss function helps find the best possible ink layout resulting in the balance between spectral reproduction and colorimetric accuracy under a multitude of illuminants. In addition, we introduce a novel spectral vector error diffusion algorithm based on combining color contoning and halftoning, which simultaneously solves the layout discretization and color quantization problems, accurately and efficiently. Our workflow outperforms the state-of-the-art models for spectral prediction and layout optimization. We demonstrate reproduction of a number of real paintings and historically important pigments using our prototype implementation that uses 10 custom inks with varying spectra and a resin-based 3D printer.

Evaluation of color differences in natural scene color images

Since there is a wide range of applications requiring image color difference (CD) assessment (e.g. color quantization, color mapping), a number of CD measures for images have been proposed. However, the performance evaluation of such measures often suffers from the following major flaws: (1) test images contain primarily spatial- (e.g. blur) rather than color-specific distortions (e.g. quantization noise), (2) there are too few test images (lack of variability in color content), and (3) test images are not publicly available (difficult to reproduce and compare). Accordingly, the performance of CD measures reported in the state-of-the-art is ambiguous and therefore inconclusive to be used for any specific color-related application.In this work, we review a total of twenty four state-of-the-art CD measures. Then, based on the findings of our review, we propose a novel method to compute CDs in natural scene color images. We have tested our measure as well as the state-of-the-art measures on three color related distortions from a publicly available database (mean shift, change in color saturation and quantization noise). Our experimental results show that the correlation between the subjective scores and the proposed measure exceeds 85% which is better than the other twenty four CD measures tested in this work (for illustration the best performing state-of-the-art CD measures achieve correlations with humans lower than 80%).

LSBs-based quantum color images watermarking algorithm in edge region

Based on the NEQR representation for quantum color and binary images, an enhanced quantum watermarking scheme is investigated through Gray code transform and least significant bit (LSB) steganography, which embeds a quantum binary image (i.e., watermark image) into the edge region of a quantum color image (i.e., carrier image) LSB and second LSB. The size of the carrier and watermark images are assumed to be $$ 2^{n} \times 2^{n} $$ and $$ 2^{n - 1} \times 2^{n - 1} $$, respectively. At first, the watermark image is resized into an appropriate size image with 4-qubit grayscale based on the nearest neighbor interpolation method, which is of the same size with the preselected edge region in carrier image. To enhance the security of the watermark image, the binary code of 4-qubit grayscale of watermark image is transformed into the corresponding Gray code, and one 3-Controlled-NOT gate is utilized to generate a quantum binary image $$ \left| {K1} \right\rangle $$. To further scatter the watermark image qubits that are embedded into the LSB and second LSB of carrier image, the quantum image $$ \left| {K1} \right\rangle $$ is employed to choose any two channels from the color image among the three channels of R, G and B (i.e., R, G or R, B channels would be chosen as the embedding channels). Furthermore, a quantum binary image $$ \left| {K2} \right\rangle $$ is generated through XOR operation decided by the quantum image $$ \left| {K1} \right\rangle $$, which is used to determine the embedding order of watermark image qubits. The extraction process is the inverse operation of embedding, which also needs the two quantum binary key images $$ \left| {K1} \right\rangle $$ and $$ \left| {K2} \right\rangle $$. Finally, the experiment results are simulated under the classical computer software MATLAB 2016(b), which illustrates that our investigated LSBs-based quantum watermarking has a better visual effect than some related works in terms of PSNR value.

Unsupervised segmentation of wear particle’s image using local texture feature

PurposeThe possibility of using a pattern recognition system for wear particle analysis without the need of a human expert holds great promise in the condition monitoring industry. Auto-segmentation of their images is a key to effective on-line monitoring system. Therefore, an unsupervised segmentation algorithm is required. The purpose of this paper is to present a novel approach based on a local color-texture feature. An algorithm is specially designed for segmentation of wear particles’ thin section images.Design/methodology/approachThe wear particles were generated by three kinds of tribo-tests. Pin-on-disk test and pin-on-plate test were done to generate sliding wear particles, including severe sliding ones; four-ball test was done to generate fatigue particles. Then an algorithm base on local texture property is raised, it includes two steps, first, color quantization reduces the total quantity of the colors without missing too much of the detail; second, edge image is calculated and by using a region grow technique, the image can be divided into different regions. Parameters are tested, and a criterion is designed to judge the performances.FindingsParameters have been tested; the scale chosen has significant influence on edge image calculation and seeds generation. Different size of windows should be applied to varies particles. Compared with traditional thresholding method along with edge detector, the proposed algorithm showed promising result. It offers a relatively higher accuracy and can be used on color image instead of gray image with little computing complexity. A conclusion can be drawn that the present method is suited for wear particles’ image segmentation and can be put into practical use in wear particles’ identification system.Research limitations/implicationsOne major problem is when small particles with similar texture are attached, the algorithm will not take them as two but as one big particle. The other problem is when dealing with thin particles, mainly abrasive particles, the algorithm usually takes it as a single line instead of an area. These problems might be solved by introducing a smaller scale of 9 × 9 window or by making use of some edge enhance technique. In this way, the subtle edges between small particles or thin particles might be detected. But the effectiveness of a scale this small shall be tested. One can also magnify the original picture to double or even triple its size, but it will dramatically increase the calculating time.Originality/valueA new unsupervised segmentation algorithm is proposed. Using the property of the edge image, we can get target out of its background, automatically. A rather complete research is done. The method is not only introduced but also completely tested. The authors examined parameters and found the best set of parameters for different kinds of wear particles. To ensure that the proposed method can work on images under different condition, three kinds of tribology tests have been carried out to simulate different wears. A criterion is designed so that the performances can be compared quantitatively which is quite valuable.

Quantum color image watermarking based on fast bit-plane scramble and dual embedded

Most of the studied quantum encryption algorithms are based on square images. In this paper, based on the improved novel quantum representation of color digital images model (INCQI), a quantum color image watermarking scheme is proposed. First, INCQI improved from NCQI is used to represent the carrier and watermark images with the size [Formula: see text] and [Formula: see text], respectively. Secondly, before embedding, the watermarking needs to be preprocessed. That is, the watermark image with the size of [Formula: see text] with 24-qubits color information is disordered by the fast bit-plane scramble algorithm, and then further expanded to an image with the size [Formula: see text] with 6-qubits pixel information by the nearest-neighbor interpolation method. Finally, the dual embedded algorithm is executed and a key image with 3-qubits information is generated for retrieving the original watermark image. The extraction process of the watermark image is the inverse process of its embedding, including inverse embedding, inverse expand and inverse scrambling operations. To show that our method has a better performance in visual quality and histogram graph, a simulation of different carrier and watermark images are conducted on the classical computer’s MATLAB.

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.

A two-stage method to improve the quality of quantized images

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.

Binary Plane Technique Based Color Quantization for Content Based Image Retrieval

Extracting accurate informative file from a high volume of graphic files is a challenging task. This paper focus on presenting a new color indexing approach using the histogram features. Two histogram features like maximum color histogram and minimum color histogram are computed and are vector quantized to constitute a feature vector. Bit plane technique is used to map these features based upon it value at the respective position. The ultimate goal of any retrieval method is to attain higher precision within a short span of time that could be achieved if the data is in compressed to accomplish this the image is compressed using binary plane technique. The result analysis depicts the performance of the proposed approach under lossy and lossless modes and found that when operated in lossy it attain effective precision rate in a speculated amount of time.

An Optimized Quantum Representation for Color Digital Images

With the continuous development of quantum computation, quantum mechanics has been widely exploited to meet the storage requirement of high definition image. In this paper, an optimized quantum representation for color digital images (OCQR) is proposed, which makes full use of quantum superposition characteristic to store the RGB value of every pixel. Compared with latest novel quantum representation of color digital images (NCQI), OCQR uses nearly one-third times the qubits to store the pixel value. Meanwhile, some image processing operations related to color information can be executed more simultaneously and conveniently based on OCQR. Therefore, the proposed OCQR model is better suited to represent the quantum color image.

A quantum color image encryption scheme based on coupled hyper-chaotic Lorenz system with three impulse injections

In this paper, a quantum color image encryption scheme based on coupled hyper-chaotic Lorenz system with three impulse injections is proposed. Firstly, in order to enhance the complexity of trajectory, three impulse signals values are injected into coupled hyper-chaotic Lorenz system during iterations. Then, six sequences generated from this system are used to encrypt red, green and blue components of the quantum color original image by XOR operations and right cyclic shift operations. Six initial values and three impulse signals values are used as keys, which could reduce the burden of keys transmission and make the cryptosystem own a key space large enough to resist exhaustive attack, even the attack from a quantum computer. Numerical simulations demonstrate that the proposed encryption scheme has a good feasibility and effectiveness for protecting quantum color images and is more secure in comparison with other encryption algorithms.

Convolutional neural network-based classification system design with compressed wireless sensor network images.

With the introduction of various advanced deep learning algorithms, initiatives for image classification systems have transitioned over from traditional machine learning algorithms (e.g., SVM) to Convolutional Neural Networks (CNNs) using deep learning software tools. A prerequisite in applying CNN to real world applications is a system that collects meaningful and useful data. For such purposes, Wireless Image Sensor Networks (WISNs), that are capable of monitoring natural environment phenomena using tiny and low-power cameras on resource-limited embedded devices, can be considered as an effective means of data collection. However, with limited battery resources, sending high-resolution raw images to the backend server is a burdensome task that has direct impact on network lifetime. To address this problem, we propose an energy-efficient pre- and post- processing mechanism using image resizing and color quantization that can significantly reduce the amount of data transferred while maintaining the classification accuracy in the CNN at the backend server. We show that, if well designed, an image in its highly compressed form can be well-classified with a CNN model trained in advance using adequately compressed data. Our evaluation using a real image dataset shows that an embedded device can reduce the amount of transmitted data by ∼71% while maintaining a classification accuracy of ∼98%. Under the same conditions, this process naturally reduces energy consumption by ∼71% compared to a WISN that sends the original uncompressed images.

Bit-level quantum color image encryption scheme with quantum cross-exchange operation and hyper-chaotic system

In order to obtain higher encryption efficiency, a bit-level quantum color image encryption scheme by exploiting quantum cross-exchange operation and a 5D hyper-chaotic system is designed. Additionally, to enhance the scrambling effect, the quantum channel swapping operation is employed to swap the gray values of corresponding pixels. The proposed color image encryption algorithm has larger key space and higher security since the 5D hyper-chaotic system has more complex dynamic behavior, better randomness and unpredictability than those based on low-dimensional hyper-chaotic systems. Simulations and theoretical analyses demonstrate that the presented bit-level quantum color image encryption scheme outperforms its classical counterparts in efficiency and security.

Quantum color image watermarking based on Arnold transformation and LSB steganography

In this paper, a quantum color image watermarking scheme is proposed through twice-scrambling of Arnold transformations and steganography of least significant bit (LSB). Both carrier image and watermark images are represented by the novel quantum representation of color digital images model (NCQI). The image sizes for carrier and watermark are assumed to be [Formula: see text] and [Formula: see text], respectively. At first, the watermark is scrambled into a disordered form through image preprocessing technique of exchanging the image pixel position and altering the color information based on Arnold transforms, simultaneously. Then, the scrambled watermark with [Formula: see text] image size and 24-qubit grayscale is further expanded to an image with size [Formula: see text] and 6-qubit grayscale using the nearest-neighbor interpolation method. Finally, the scrambled and expanded watermark is embedded into the carrier by steganography of LSB scheme, and a key image with [Formula: see text] size and 3-qubit information is generated at the meantime, which only can use the key image to retrieve the original watermark. The extraction of watermark is the reverse process of embedding, which is achieved by applying a sequence of operations in the reverse order. Simulation-based experimental results involving different carrier and watermark images (i.e. conventional or non-quantum) are simulated based on the classical computer’s MATLAB 2014b software, which illustrates that the present method has a good performance in terms of three items: visual quality, robustness and steganography capacity.