Color Quantization Research Articles

Color-texture segmentation using JSEG based on Gaussian mixture modeling

Abstract An improved approach for J-value segmentation (JSEG) is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift (AMS) based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling (GMM) of image data for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on AMS based clustering and GMM overcomes the limitations of JSEG successfully and is more robust.

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

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

Universal colour quantisation for different colour spaces

A universal colour quantisation for different colour spaces is proposed. First, the differences between colour spaces such as RGB, YCrCb, YIQ, I1I2I3, HSV, L*a*b* and L*u*v* are analysed. On the basis of analysis results, the relationship between RGB and HSV is derived, which in turn is used to design a universal colour quantisation for improving retrieval effectiveness. Moreover, colour distribution of real images is further considered so as to obtain more accurate retrieval results. The effectiveness and practicability of the proposed method have been demonstrated by various experiments.

A simulated annealing restoration algorithm for restoring halftoned color-quantized images

Restoration of color-quantized images is rarely addressed in the literature especially when the images are color-quantized with halftoning. Most existing restoration algorithms are generally inadequate to deal with this problem as they were proposed for restoring noisy blurred images. In this paper, a restoration algorithm based on simulated annealing is proposed to solve the problem. This algorithm makes a good use of the available color palette and the mechanism of a halftoning process to derive useful a priori information for restoration. Simulation results show that it can improve the quality of a halftoned color-quantized image remarkably in terms of both SNR and CIELAB color difference metric. The subjective quality of the restored images can also be improved.

Unsupervised color–texture segmentation based on soft criterion with adaptive mean-shift clustering

An improved approach for J value segmentation (JSEG) is presented for unsupervised color–texture segmentation. Instead of the color quantization algorithm used in JSEG, an automatic classification method using adaptive mean-shift (AMS) clustering is applied for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the improved method overcomes the limitations of JSEG successfully and is more robust.

Efficient Compression Method for Cell Animation Video

Animation video is becoming very popular with the availability of low cost computing resources. The cell animation is a popular method, used for producing animation video. In order to efficiently encode these videos, conventional video codecs, such as AutoDesk Animation Pro FLC, Intel Indeo 5, and MPEG-4 can be used to achieve high compression. However, when cell animation videos are compressed at very low bit rate by these traditional codecs, objectionable artifacts, e.g., false color, blurred contours, and blocking artifact, are severely occurred. In this paper, we propose an efficient compression method for cell animation images. The proposed method employs hybrid coding scheme which includes intraframe and interframe coding modes. The intraframe mode consists of color quantization, adaptive differential pulse code modulation, forward classification, and Golomb-Rice coding. The interframe coding consists of block-based techniques and exploits the characteristics of motion. Simulation results show that the proposed method provides superior performance over AutoDesk Animation Pro FLC, MPEG-1, Intel Indeo 5, and MPEG-4 standards.

DWT-based scene-adaptive color quantization

Color quantization is the process of reducing the number of colors in an image. That is, color quantization maps a large number of colors into a much smaller number of representative colors while keeping color distortion to an acceptable level. The reduction in the number of colors lowers computational complexity associated with color processing, and achieves higher color image compression for storage and transmission purposes. The existing color quantization methods require that the number of representative or prominent colors be specified by the user. This paper presents a scene-adaptive color quantization method which eases this constraint by determining the number of representative colors automatically. This method utilizes the discrete wavelet transform to achieve a computationally efficient implementation of the multi-scale clustering algorithm in a 3D color space. The performance is evaluated in terms of compression ratio or number of representative colors, color distortion, and computational complexity. It is shown that the developed method outperforms the popular color quantization methods in terms of color distortion.

A regularized constrained iterative restoration algorithm for restoring color-quantized images

This paper studies the restoration of color-quantized images. Restoration of color-quantized images is rarely addressed in literature, and direct applications of existing restoration techniques are generally inadequate to deal with this problem. In this paper, we propose a restoration algorithm for restoring color-quantized images. This algorithm makes good use of the available color palette to derive useful a priori information for restoration. Simulation results show that it can improve the quality of a color-quantized image remarkably in terms of both SNR and CIELAB color difference metric. Its performance is obviously better than that of other conventional algorithms in the simulation.

Skin segmentation using color pixel classification: analysis and comparison

This paper presents a study of three important issues of the color pixel classification approach to skin segmentation: color representation, color quantization, and classification algorithm. Our analysis of several representative color spaces using the Bayesian classifier with the histogram technique shows that skin segmentation based on color pixel classification is largely unaffected by the choice of the color space. However, segmentation performance degrades when only chrominance channels are used in classification. Furthermore, we find that color quantization can be as low as 64 bins per channel, although higher histogram sizes give better segmentation performance. The Bayesian classifier with the histogram technique and the multilayer perceptron classifier are found to perform better compared to other tested classifiers, including three piecewise linear classifiers, three unimodal Gaussian classifiers, and a Gaussian mixture classifier.

New Adaptive Color Quantization Method Based on Self-Organizing Maps

Color quantization (CQ) is an image processing task popularly used to convert true color images to palletized images for limited color display devices. To minimize the contouring artifacts introduced by the reduction of colors, a new competitive learning (CL) based scheme called the frequency sensitive self-organizing maps (FS-SOMs) is proposed to optimize the color palette design for CQ. FS-SOM harmonically blends the neighborhood adaptation of the well-known self-organizing maps (SOMs) with the neuron dependent frequency sensitive learning model, the global butterfly permutation sequence for input randomization, and the reinitialization of dead neurons to harness effective utilization of neurons. The net effect is an improvement in adaptation, a well-ordered color palette, and the alleviation of underutilization problem, which is the main cause of visually perceivable artifacts of CQ. Extensive simulations have been performed to analyze and compare the learning behavior and performance of FS-SOM against other vector quantization (VQ) algorithms. The results show that the proposed FS-SOM outperforms classical CL, Linde, Buzo, and Gray (LBG), and SOM algorithms. More importantly, FS-SOM achieves its superiority in reconstruction quality and topological ordering with a much greater robustness against variations in network parameters than the current art SOM algorithm for CQ. A most significant bit (MSB) biased encoding scheme is also introduced to reduce the number of parallel processing units. By mapping the pixel values as sign-magnitude numbers and biasing the magnitudes according to their sign bits, eight lattice points in the color space are condensed into one common point density function. Consequently, the same processing element can be used to map several color clusters and the entire FS-SOM network can be substantially scaled down without severely scarifying the quality of the displayed image. The drawback of this encoding scheme is the additional storage overhead, which can be cut down by leveraging on existing encoder in an overall lossy compression scheme.

Synthetic Image Sequence Compression.

This paper describes a technique for compressing computer screen shots into a GIF animation file. The goal is to distribute the animations, to a variety of browsers, without requiring a plug-in or helper application. We seek to minimize the size of the image sequence, while maximizing the signal to noise ratio of the sequence. The GIF animation format has several constraints; images may have a maximum of 256 colors, and the images must all be of the same size. Further, to minimize overhead, we seek to make use of a single color lookup table for the entire animation. Several color quantization algorithms are compared, using SNR (Signal to Noise Ratio) as the metric of quality (as well as subjective appearance). We present the design of an interface, written in Java, and distributed freely using Java Web Start, that employs a well know neural network program and a color quantization algorithm to capture screen shots and save them to the GIF animation. GIF animations represent silent movies, as they have no sound to accompany them. However, they are still in wide use and have applications in entertainment and education. The techniques described are a part of the JSnap project, a joint project between the skunk works of DocJava, Inc. and Fairfield University.

Integration of color and boundary information for improved region of interest identification in electron magnetic resonance images

A Windows-based, object-oriented application system for segmentation and analysis of electron magnetic resonance (EMR) images is described. The integrated system is developed for better recognition of regions of interest (ROI) in murine EMR images. The system combines the clustering method of color segmentation with boundary detection, for efficient segmentation of regions of interest in EMR images. Initially, the red/green/blue (RGB) color space is converted into spherical coordinates transform (SCT) space. Color quantization is then achieved by center split algorithm applied on the color dimensions of the SCT space. Subsequently, Laplacian boundary detection operator is used to extract the contours of the ROI from the variegated coloring information. The system is implemented in Visual C++ and tested on temporal EMR color images of mouse. The system performs well giving perceptually reasonable segmentation of tumor, kidney and bladder of the mouse image. Experimental results with extensive set of EMR color images demonstrate the efficacy of the system developed.

Novel fast color reduction algorithm for time-constrained applications

In this paper, we propose a new adaptive approach of color quantization. It can significantly reduce the time consumption during the process compared with available methods but still maintains a good quality (greater than 30 dB of PSNR). It is implemented as a part of the media stream compression algorithms for a True Color Signboard System. We adapt and create some techniques to speed up the process. We start with a sampling technique on an RGB color space before constructing the 3D histogram of color distribution, and then we use the dynamic programming based on Wu’s algorithm to construct the cumulative moment distribution. Then, we put the cutting plane through the centroid of that box. This plane is perpendicular to the axis, on which the sum of the squared Euclidean distances between the centroid of both of sub-boxes and the centroid of the box is the greatest. The sub-box, which contains the greatest value representing variance, is repeatedly sub-divided into the smaller sub-boxes until reaching the desired number of the representative colors. From our whole process, we gain approximately up to 50% less time consumption than Wu’s quantizer [ACM Trans. Graph. 11 (1992) 348] and it is significantly faster than existing algorithms as shown in the result.

An adjustable algorithm for color quantization

Color quantization is an important technique in digital image processing. Generally it involves two steps. The first step is to choose a proper color palette. The second step is to reconstruct an image by replacing original colors with the most similar palette colors. However a problem exists while choosing palette colors. That is how to choose the colors with different illumination intensities (we call them color layers) as well as the colors that present the essential details of the image. This is an important and difficult problem. In this paper, we propose a novel algorithm for color quantization, which considers both color layers and essential details by assigning weights for pixel numbers and color distances. Also this algorithm can tune the quantization results by choosing proper weights. The experiments show that our algorithm is effective for adjusting quantization results and it also has very good quality of quantization.

A Robust Color Object Analysis Approach to Efficient Image Retrieval

We describe a novel indexing and retrieval methodology integrating color, texture, and shape information for content-based image retrieval in image databases. This methodology, we call CLEAR, applies unsupervised image segmentation to partition an image into a set of objects. Fuzzy color histogram, fuzzy texture, and fuzzy shape properties of each object are then calculated to be its signature. The fuzzification procedures effectively resolve the recognition uncertainty stemming from color quantization and human perception of colors. At the same time, the fuzzy scheme incorporates segmentation-related uncertainties into the retrieval algorithm. An adaptive and effective measure for the overall similarity between images is developed by integrating properties of all the objects in every image. In an effort to further improve the retrieval efficiency, a secondary clustering technique is developed and employed, which significantly saves query processing time without compromising retrieval precision. A prototypical system of CLEAR, we developed, demonstrated the promising retrieval performance and robustness in color variations and segmentation-related uncertainties for a test database containing general-purpose color images, as compared with its peer systems in the literature.

Color image quantization using distances between adjacent colors along the color axis with highest color variance

This paper describes a simple but effective hierarchically divisive colormap design technique for color image quantization. By sorting colors based on their components along the principal axis, the one with the highest variance of color distribution, the Euclidean distances between any adjacent colors' along the axis are used to find the cutting plane that is perpendicular to the axis and divides a color cell into two subcells with approximately equal quantization errors with respect to their centroids. As a result, the total quantization error on both cells is minimal. The experimental results reveal that the proposed algorithm is effective and yields a better execution time when compared with others. Moreover the proposed method performs well on both pictures with 15-bit and 24-bit colors regardless of a number of colors in the colormap.

인간 시각 시스템의 공간 지각 특성을 이용한 개선된 이진트리 벡터양자화

칼라 양자화는 칼라 카메라로부터 입력된 자연 색의 칼라 영상을 컴퓨터 모니터에 표현하기 위해 더 적은 칼라 팔레트로 구성하는 것이다. 이것은 자연색 중에서 인간이 인식한 수 있는 색은 수 백가지 정도일 뿐 아니라 실제 칼라 영상에 나타나는 색은 이 보다는 훨씬 적다는 것을 바탕으로 한다. 본 논문에서는 인간의 시각 특성중 공간 지각 특성을 고려한 개선된 고유벡터를 이용한 이진 트리 벡터양자화 기법을 제안한다. 제안 방법은 고유벡터를 이용한 이진 트리 벡터 양자화의 두 노드로 분할하는 과정에 영상의 블록 내의 칼라의 변화에 따른 인간의 시각의 반응 정도를 가중피로 결합하여 양자화를 하였다. 제안 방법은 기존의 방법보다 색상이 선명해지며 유사한 영역의 분한에 뛰어난 성능을 보여 주었으며 주관적인 화질 평가와 PSNR에서도 좋은 결과를 보였다. Color image quantization is a process of selecting a set of colors to display an image with some representative colors without noticeable perceived difference. It is very important in many applications to display a true color image in a low cost color monitor or printer. The basic problem is how to display 256 colors or less colors, called color palette, In this paper, we propose improved binary tree vector quantization based on spatial sensitivity which is one of the human visual properties. We combine the weights based on the responsibility of human visual system according to changes of three Primary colors in blocks of images with the process of splitting nodes using eigenvector in binary tree vector quantization. The test results show that the proposed method generates the quantized images with fine color and performs better than the conventional method in terms of clustering the similar regions. Also the proposed method can get the better result in subjective quality test and WSNR.

An ASIC implementation of Kohonen's map based colour image compression

This paper presents a new hardware design for a neural network based colour image compression. The compressed image consists of a colour palette containing few best colours and the coded image. Kohonen's map neural network is applied to construct the colour palette and the coded image, both forming the compressed image. The Kohonen's map based compression results in linear time complexity (in the size of the image). It is advantageous over traditional JPEG in colour quantization applications and compression of images with limited colours. The architecture of the hardware unit is based on single instruction multiple data methodology. The architecture has been implemented in an application specific integrated circuit and results show that the proposed design achieves high speed allowing inputs at a video rate for compression of images up to size of 512×512 with low area requirement.

Colour quantisation technique based on image decomposition and its embedded system implementation

A new colour quantisation (CQ) technique and its corresponding embedded system realisation are introduced. The CQ technique is based on image split into sub-images and the use of Kohonen self-organised neural network classifiers (SONNC). Initially, the dominant colours of each sub-image are extracted through SONNCs and then are used for the quantisation of the colours of the entire image. The proposed CQ technique can use both colour components and spatial features, achieving better approximation of the final image to the spatial characteristics of the original one. In addition, for the estimation of the proper number of dominant image colours, a new algorithm based on the projection of the image colours into the first two principal components is proposed. The image split into sub-images offers reduction of the on-chip memory requirements and is suitable for embedded system (or system-on-chip) implementation of the most time-consuming part of the technique. Applying a systematic design methodology to the developed CQ algorithm, an efficient embedded architecture based on the ARM7 processor achieving high-speed processing and less energy consumption, is derived.

GENERAL IMAGE CLASSIFICATION USING ADAPTIVE CELLULAR COLOR DECOMPOSITION

In this paper, a coarse-to-fine hierarchical classification method based on the features derived from adaptive cellular color decomposition is proposed. The proposed method is general and can be applied to all kinds of color image databases as long as a sample set of images have been classified. In addition, the number of classes can be as versatile as required. To achieve the goal mentioned above, our method consists of two phases: color quantization and classification. In the color quantization step, cellular decomposition is used to adaptively quantize color images in the HSV color space since H and S components construct a hexagon structure that is same as the cellular pattern. In the classification step, a coarse-to-fine strategy is employed. In the coarse stage, five image-based features extracted directly from the quantization results of the query images are used to prune irrelevant database images. In the fine stage, two cluster-based features are extracted from a small set of candidate images using closest-cluster matching. On the other hand, according to feature evaluation, one image-based and two cluster-based features are selected to derive individual-based similarity measure, which, in turn, is used to measure image-to-image similarity. In addition, class-based similarity measure using class characteristics is proposed to evaluate image-to-class similarity. Candidate images are then sorted according to the similarity measure, which is a combination of individual-based and class-based similarity measures. Finally, k-NN rule is used to assign the query image to a single class according to the sorting results. The effectiveness and practicability of the proposed method have been demonstrated by various experimental results.