Fast Color Quantization Using Weighted Sort-Means Clustering

Color quantization is an important operation with numerous applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much respect in the color quantization literature because of its high computational requirements and sensitivity to initialization. In this paper, a fast color quantization method based on k-means is presented. The method involves several modifications to the conventional (batch) k-means algorithm including data reduction, sample weighting, and the use of triangle inequality to speed up the nearest neighbor search. Experiments on a diverse set of images demonstrate that, with the proposed modifications, k-means becomes very competitive with state-of-the-art color quantization methods in terms of both effectiveness and efficiency.Comment: 30 pages, 2 figures, 4 table

Improving the Performance of K-Means for Color Quantization

Color quantization is an important operation with many applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much respect in the color quantization literature because of its high computational requirements and sensitivity to initialization. In this paper, we investigate the performance of k-means as a color quantizer. We implement fast and exact variants of k-means with several initialization schemes and then compare the resulting quantizers to some of the most popular quantizers in the literature. Experiments on a diverse set of images demonstrate that an efficient implementation of k-means with an appropriate initialization strategy can in fact serve as a very effective color quantizer.Comment: 26 pages, 4 figures, 13 table

Efficient, edge-aware, combined color quantization and dithering

Abstract—In this paper we present a novel algorithm to simultaneously accomplish color quantization and dithering of images. This is achieved by minimizing a perception-based cost function which considers pixel-wise differences between filtered versions of the quantized image and the input image. We use edge aware filters in defining the cost function to avoid mixing colors on opposite sides of an edge. The importance of each pixel is weighted according to its saliency. To rapidly minimize the cost function, we use a modified multi-scale iterative conditional mode (ICM) algorithm which updates one pixel a time while keeping other pixels unchanged. As ICM is a local method, careful initialization is required to prevent termination at a local minimum far from the global one. To address this problem, we initialize ICM with a palette generated by a modified median-cut method. Compared to previous approaches, our method can produce high quality results with fewer visual artifacts but also requires significantly less computational effort. Index Terms—Color quantization, dithering, optimization-based image processing. I

Cluster compression algorithm: A joint clustering/data compression concept

The Cluster Compression Algorithm (CCA), which was developed to reduce costs associated with transmitting, storing, distributing, and interpreting LANDSAT multispectral image data is described. The CCA is a preprocessing algorithm that uses feature extraction and data compression to more efficiently represent the information in the image data. The format of the preprocessed data enables simply a look-up table decoding and direct use of the extracted features to reduce user computation for either image reconstruction, or computer interpretation of the image data. Basically, the CCA uses spatially local clustering to extract features from the image data to describe spectral characteristics of the data set. In addition, the features may be used to form a sequence of scalar numbers that define each picture element in terms of the cluster features. This sequence, called the feature map, is then efficiently represented by using source encoding concepts. Various forms of the CCA are defined and experimental results are presented to show trade-offs and characteristics of the various implementations. Examples are provided that demonstrate the application of the cluster compression concept to multi-spectral images from LANDSAT and other sources

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Implementasi Color Quantization pada Kompresi Citra Digital dengan Menggunakan Model Clustering Berdasarkan Nilai Max Variance pada Ruang Warna RGB

Kompresi citra dapat dilakukan dengan menggunakan color quantization di mana dengan mengurangi jumlah warna yang terdapat pada citra maka akan dapat mengurangi jumlah bit yang digunakan untuk merepresentasikan warna – warna tersebut. Semakin rendah jumlah warna yang dikurangi dalam rangka mencapai rasio kompresi yang optimal berdampak pada terdegradasinya kualitas dari citra. Secara umum color quantization menggunakan model clustering dalam proses pembentukan color palette yang akan digunakan sebagai referensi pada saat kuantisasi. Penelitian ini menggunakan model clustering berdasarkan nilai max variance pada channel RGB secara terpisah. Proses clustering dilakukan dengan membelah populasi cluster sebelumnya menggunakan nilai mean dari channel RGB yang memiliki nilai variance tertinggi. Color palette kemudian dibentuk menggunakan centroid hasil dari proses clustering. Percobaan pada beberapa citra uji dengan format 32bpp yang kemudian dikompresi menggunakan kuantisasi warna pada format 8bpp dan 4bpp memberikan kualitas dan rasio kompresi yang cukup baik yang diukur menggunakan ukuran MSE, PSNR dan CR di mana nilai MSE yang diperoleh berkisar 3.87 sampai 6.3 pada kuantisasi 8bpp dan 13.39 sampai 19.62 pada kuantisasi 4bpp. Sedangkan rasio kompresi yang diperoleh adalah sebesar 1.44 sampai 2.09 pada kuantisasi 8bpp dan 2.87 sampai 4.23 pada kuantisasi 4bpp. AbstractImage compression can be done by using color quantization where by reducing the number of colors contained in the image it can reduce the number of bits used to represent the colors. The lower the number of colors reduced in order to achieve the optimal compression ratio has an impact on the quality of the image. In general, color quantization uses clustering models in the process of constructing color palette that will be used as a reference during quantization. This study uses a clustering model based on the max variance value on the RGB channel separately. The clustering process is done by dividing the previous cluster population using the mean value of the RGB channel which has the highest variance value. The color palette is then formed using centroids resulting from the clustering process. Experiments on some test images in 32bpp format which are then compressed using color quantization in 8bpp and 4bpp formats provide a fairly good quality and compression ratio with MSE, PSNR and CR assessment where the MSE values obtained ranged from 3.87 to 6.3 at 8bpp quantization and 13.39 to 19.62 at 4bpp quantization. While the compression ratio obtained is 1.44 to 2.09 at 8bpp quantization and 2.87 to 4.23 at 4bpp quantizatio

Color Reduction in Hand-drawn Persian Carpet Cartoons before Discretization using image segmentation and finding edgy regions

In this paper, we present a method for color reduction of Persian carpet cartoons that increases both speed and accuracy of editing. Carpet cartoons are in two categories: machine-printed and hand-drawn. Hand-drawn cartoons are divided into two groups: before and after discretization. The purpose of this study is color reduction of hand-drawn cartoons before discretization. The proposed algorithm consists of the following steps: image segmentation, finding the color of each region, color reduction around the edges and final color reduction with C-means. The proposed method requires knowing the desired number of colors in any cartoon. In this method, the number of colors is not reduced to more than about 1.3 times of the desired number. Automatic color reduction is done in such a way that final manual editing to reach the desired colors is very easy

The Role of Ecological Interactions in Polymicrobial Biofilms and their Contribution to Multiple Antibiotic Resistance

The primary objectives of this research were to demonstrate that: 1.) antibiotic resistant bacteria can promote the survival of antibiotic sensitive organisms when grown simultaneously as biofilms in antibiotics, 2.) community-level multiple antibiotic resistance of polymicrobial consortia can lead to biofilm formation despite the presence of multiple antibiotics, and 3.) biofilms may benefit plasmid retention and heterologous protein production in the absence of selective pressure. Quantitative analyses of confocal data showed that ampicillin resistant organisms supported populations of ampicillin sensitive organisms in steady state ampicillin concentrations 13 times greater than that which would inhibit sensitive cells inoculated alone. The rate of reaction of the resistance mechanism influenced the degree of protection. Spectinomycin resistant organisms did not support their sensitive counterparts, although flow cytometry indicated that GFP production by the sensitive strain was improved. When both organisms were grown in both antibiotics, larger numbers of substratum-attached pairs at 2 hours resulted in greater biofilm formation at 48 hours. For biofilms grown in both antibiotics, a benefit to spectinomycin resistant organism’s population size was detectable, but the only benefit to ampicillin resistant organisms was in terms of GFP production. Additionally, an initial attachment ratio of 5 spectinomycin resistant organisms to 1 ampicillin resistant organism resulted in optimal biofilm formation at 48 hours. Biofilms also enhanced the stability of high-copy number plasmids and heterologous protein production. In the absence of antibiotic selective pressure, plasmid DNA was not detected after 48 hours in chemostats, where the faster growth rate of plasmid-free cells contributed to the washout of plasmid retaining cells. The plasmid copy number per cell in biofilms grown without antibiotic selective pressure steadily increased over a six day period. Flow cytometric monitoring of bacteria grown in biofilms indicated that 95 percent of the population was producing GFP at 48 hours. This research supports the idea that ecological interactions between bacteria contribute to biofilm development in the presence of antibiotics, and demonstrates that community-level multiple antibiotic resistance is a factor in biofilm recalcitrance against antibiotics. Additionally, biofilms may provide an additional tool for stabilizing high copy number plasmids used for heterologous protein production