Color Quantization Research Articles

An Application of Palette Based Steganography

Steganography is the art of writing hidden messages in such a way that no one; apart from the sender and intended recipient even understand there is a hidden message. Steganography includes the concealment of information within computer files. One of the most common methods of implementation is Least Significant Bit Insertion, in which the least significant bit of every byte is altered to form the bit-string representing the embedded file. Altering the LSB will only cause minor changes in color. While this technique works well for 24-bit color image files, steganography has not been as successful when using an 8-bit color image file, due to limitations in color variations and the use of a color table. Color table is organized asthe first three bytes correspond to RGB components and the last byte is reserved or unused. The proposed technique is to generate the image from a 24-bit bitmap to an 8-bit bitmap using color quantization resulted in minor variations in the image, which are barely noticeable to the human eye. However, these slight variations aid in hiding the data.

Color Quantization Based on Hierarchical Frequency Sensitive Competitive Learning

In this paper, we propose a Hierarchical Frequency Sensitive Competitive Learning (HFSCL) method to achieve Color Quantization (CQ). In HFSCL, the appropriate number of quantized colors and the palette can be obtained by an adaptive procedure following a binary tree structure with nodes and layers. Starting from the root node that contains all colors in an image until all nodes are examined by split conditions, a binary tree will be generated. In each node of the tree, a Frequency Sensitive Competitive Learning (FSCL) network is used to achieve two-way division. To avoid over-split, merging condition is defined to merge the clusters that are close enough to each other at each layer. Experimental results show that the proposed HFSCL has desired ability for CQ.

Real-time coherent stylization for augmented reality

This paper presents a new stylized augmented reality (AR) framework which can generate line drawing and abstracted shading styles. In comparison with the state-of-art work, our framework can significantly improve both the visual immersion of a single frame and the temporal coherence of augmented video streams in real time. In our framework, we first render virtual objects over the input camera images and then uniformly process the combined contents with stylization techniques. For generating line drawing stylization, we first propose a specially designed shading method to render the virtual objects, and then use an adapted Flow-based anisotropic Difference-of-Gaussion (FDoG) filter to yield the high-quality line drawing effect. For generating the abstracted stylization, a focus-guided diffusion filter and a soft color quantization operator are sequentially applied to the augmented image, and then the processed result is combined with the detected edges to produce the final abstraction effect. The presented algorithms are all sympathetic to highly parallel processing, allowing a real-time performance on contemporary graphics hardware.

An effective image retrieval scheme using color, texture and shape features

In this paper, we present a new and effective color image retrieval scheme for combining all the three i.e. color, texture and shape information, which achieved higher retrieval efficiency. Firstly, the image is predetermined by using fast color quantization algorithm with clusters merging, and then a small number of dominant colors and their percentages can be obtained. Secondly, the spatial texture features are extracted using a steerable filter decomposition, which offers an efficient and flexible approximation of early processing in the human visual system. Thirdly, the pseudo-Zernike moments of an image are used for shape descriptor, which have better features representation capabilities and are more robust to noise than other moment representations. Finally, the combination of the color, texture and shape features provide a robust feature set for image retrieval. Experimental results show that the proposed color image retrieval is more accurate and efficient in retrieving the user-interested images.

Adaptive Extraction of Principal Colors Using an Improved Self-Growing Network

This paper aims to solve the two major issues existing in current color quantization algorithms. The first one is to require users to specify the number of representative colors in advance; the other is that it is difficult in choosing the colors to describe accurately the essential details represented by small groups of pixels isolated in the color space. Based on the growing mechanism of the Growing When Required neural network, a novel algorithm is proposed to adaptively extract the prominent colors of an image. A number of criteria are introduced that have an effect on controlling of the number and topology of neurons in the output layer. A global permutation method to rearrange the input sample order is presented based on Linear Pixels Shuffling in order to improve the performance of the network. The experiments show that the proposed method can automatically estimate the number of colors to efficiently represent an original image, meanwhile capable of retaining important isolated colors even when the number of the representative colors is low. It is also shown that the algorithm outperforms the popular ones in terms of color distortion.

Optimal image colour extraction by differential evolution

Differential evolution is an optimisation technique that has been successfully employed in various applications. In this paper, we apply differential evolution to the problem of extracting the optimal colours of a colour map for quantised images. The choice of entries in the colour map is crucial for the resulting image quality as it forms a look-up table that is used for all pixels in the image. We show that differential evolution can be effectively employed as a method for deriving the entries in the map. In order to optimise the image quality, our differential evolution approach is combined with a local search method that is guaranteed to find the local optimal colour map. This hybrid approach is shown to outperform various commonly used colour quantisation algorithms on a set of standard images.

Compress Compound Images in H.264/MPGE-4 AVC by Exploiting Spatial Correlation

Compound images are a combination of text, graphics and natural image. They present strong anisotropic features, especially on the text and graphics parts. These anisotropic features often render conventional compression inefficient. Thus, this paper proposes a novel coding scheme from the H.264 intraframe coding. In the scheme, two new intramodes are developed to better exploit spatial correlation in compound images. The first is the residual scalar quantization (RSQ) mode, where intrapredicted residues are directly quantized and coded without transform. The second is the base colors and index map (BCIM) mode that can be viewed as an adaptive color quantization. In this mode, an image block is represented by several representative colors, referred to as base colors, and an index map to compress. Every block selects its coding mode from two new modes and the previous intramodes in H.264 by rate-distortion optimization (RDO). Experimental results show that the proposed scheme improves the coding efficiency even more than 10 dB at most bit rates for compound images and keeps a comparable efficient performance to H.264 for natural images.

Derivation of the analytical solution of Color2Gray algorithm and its application to fast color removal based on color quantization

In order to convert a color image into a monochrome one, the lightness components of pixels have to date been used as gray-levels for the representation of the monochrome image. However, saliencies of an image embedded only in the chrominance components are eliminated in such conversion. To cope with this problem, “Color2Gray” algorithm, which excels in the color removal of digital images, has been proposed by Gooch et al. [ACM Trans. Graphics 24 (2005) 634]. In this paper, the algorithm is first analyzed and its mathematical property is revealed. Then a fast Color2Gray algorithm is proposed by using the mathematical property. Finally, the validity and the effectiveness of the proposed algorithm are proven by some experiments.

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 the 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.

Automatic textile image annotation by predicting emotional concepts from visual features

This paper presents an emotion prediction system that can automatically predict certain human emotional concepts from a given textile. The main application motivating this study is textile image annotation, which has recently rapidly expanded in relation to the Web. In the proposed method, color and pattern are used as cues to predict the emotional semantics associated with an image, where these features are extracted using a color quantization and a multi-level wavelet transform, respectively. The extracted features are then applied to three representative classifiers: K-means clustering, Naïve Bayesian, and a multi-layered perceptron (MLP), all of which are widely used in data mining. When evaluating the proposed emotion prediction method using 3600 textile images, the MLP produces the best performance. Thereafter, the proposed MLP-based method is compared with other methods that only use color or pattern, and the proposed method shows the best performance with an accuracy of above 92%. Therefore, the results confirm that the proposed method can be effectively applied to the commercial textile industry and image retrieval.

Content-based retrieval of logo and trademarks in unconstrained color image databases using Color Edge Gradient Co-occurrence Histograms

In this paper, we present an algorithm that extends the Color Edge Co-occurrence Histogram (CECH) object detection scheme on compound color objects, for the retrieval of logos and trademarks in unconstrained color image databases. We introduce more accurate information to the CECH, by virtue of incorporating color edge detection using vector order statistics. This produces a more accurate representation of edges in color images, as compared to the simple color pixel difference classification of edges seen with the CECH. Our proposed method is thus reliant on edge gradient information, and so we call it the Color Edge Gradient Co-occurrence Histogram (CEGCH). We also introduce a color quantization method based in the hue–saturation–value (HSV) color space, illustrating that it is a more suitable scheme of quantization for image retrieval, compared to the color quantization scheme introduced with the CECH. Experimental results demonstrate that the CEGCH and the HSV color quantization scheme is insensitive to scaling, rotation, and partial deformations, and outperforms the use of the CECH in image retrieval, with higher precision and recall. We also perform experiments on a closely related algorithm based on the Color Co-occurrence Histogram (CCH) and demonstrate that our algorithm is also superior in comparison, with higher precision and recall.

Automatic Image Segmentation by Dynamic Region Growth and Multiresolution Merging

Image segmentation is a fundamental task in many computer vision applications. In this paper, we propose a new unsupervised color image segmentation algorithm, which exploits the information obtained from detecting edges in color images in the CIE L *a *b * color space. To this effect, by using a color gradient detection technique, pixels without edges are clustered and labeled individually to identify some initial portion of the input image content. Elements that contain higher gradient densities are included by the dynamic generation of clusters as the algorithm progresses. Texture modeling is performed by color quantization and local entropy computation of the quantized image. The obtained texture and color information along with a region growth map consisting of all fully grown regions are used to perform a unique multiresolution merging procedure to blend regions with similar characteristics. Experimental results obtained in comparison to published segmentation techniques demonstrate the performance advantages of the proposed method.

Hierarchical Error Diffusion

This paper develops a distinctive class of color error diffusion algorithm, called hierarchical error diffusion (HED). It aims to achieve perceptually pleasing color halftone through neither conventional joint quantization nor interchannel error diffusion. Instead, it explicitly controls three critical factors sequentially to yield high-quality color halftone: dot-overlapping control, dot-positioning control, and dot-coloring control. A specific implementation of HED is presented with the objective of minimum brightness variation rendering (MBVR). First, an optimal color transform is derived for dot-overlapping control to achieve minimum brightness variation color density (MBVCD). Then, the embedded monochrome error diffusion is employed in dot-positioning control. By sequentially thresholding the elements in partial density sum vector, better dot-positioning is encouraged for more visible color dots. The "blue noise" characteristics of dot-positioning from the monochrome error diffusion are inherited by the color halftone. The simple density priority strategy is applied in dot-coloring control. The pixel color error is diffused channel-independently with a single error filter in halftone dot color space. A comparison with the state-of-the-art color error diffusion algorithms demonstrates excellent halftone quality of HED, while without the typical artifacts of vector error diffusion. Evidence also shows that HED is closer to achieve MBVR than the minimum brightness variation quantization (MBVQ) color diffusion algorithm proposed in .

Color Texture Segmentation Based on the Modal Energy of Deformable Surfaces

This paper presents a new approach for the segmentation of color textured images, which is based on a novel energy function. The proposed energy function, which expresses the local smoothness of an image area, is derived by exploiting an intermediate step of modal analysis that is utilized in order to describe and analyze the deformations of a 3-D deformable surface model. The external forces that attract the 3-D deformable surface model combine the intensity of the image pixels with the spatial information of local image regions. The proposed image segmentation algorithm has two steps. First, a color quantization scheme, which is based on the node displacements of the deformable surface model, is utilized in order to decrease the number of colors in the image. Then, the proposed energy function is used as a criterion for a region growing algorithm. The final segmentation of the image is derived by a region merge approach. The proposed method was applied to the Berkeley segmentation database. The obtained results show good segmentation robustness, when compared to other state of the art image segmentation algorithms.

Multiscale Probabilistic Dithering for Suppressing Contour Artifacts in Digital Images

A method is proposed for reducing the visibility of "contour artifacts," i.e., false contours resulting from color quantization in digital images. The method performs a multiscale analysis on the neighborhood of each pixel, determines the presence and scale of contour artifacts, and probabilistically dithers (perturbs) the color of the pixel. The overall effect is to "break down" the false contours, making them less visible. The proposed method may be used to reduce contour artifacts at the same bit depth as the input image or at higher bit depths. The contour artifact detection mechanism ensures that artifact-free regions remain unaffected during the process.

New method for obtaining proper initial clusters to perform FCM algorithm for colour image clustering

To print a colour image on a fabric, colour image clustering is an important step to reduce the number of colours and separate the coloured pattern. Therefore, the performance of colour-clustering algorithm can strongly affect the quality of printing process. Fuzzy c-mean (FCM) clustering is a known clustering algorithm for colour image quantization but the results of FCM depend on the choice of initialization. Therefore, the problem is to find the appropriate initial centres, which are commonly chosen randomly. This paper introduces a novel initialization method for FCM algorithm for clustering colour images. We use the probability density function (pdf) of the colour image to estimate the initial centres. Considering that the three-dimensional computations are complicated and time consuming, we apply principal component analysis (PCA) to find an appropriate direction. Firstly, the three-dimensional colour points should be mapped on the first PCA vector of the colour image data. Then, to obtain the dominant colours, the pdf of the new data is calculated, the points with the highest pdf values have more chance to be initial centres. By selecting a centre, the neighbourhood data in a diameter of σ are eliminated. The number of clusters estimates the value of σ. The process is continued until approaching the desired number of colours. The experimental results show that the proposed method performs well for clustering colour images.

Colour palette generation schemes for colour image quantization

Two palette generation schemes are proposed for colour image quantization in this paper. The first scheme is a splitting-based scheme that can be used generate colour palettes with low computational complexity. By combing the first scheme with the k-means algorithm, the second scheme is obtained. Compared to the first scheme, the second scheme requires more execution time and designs better colour palettes. According to the results, the proposed schemes provide good image quality of the compressed images while consuming little computational cost. These two schemes are quite suitable for real-time multimedia applications based on colour image quantization.

Fuzzy clustering for colour reduction in images

The aim of colour quantisation is to reduce the number of distinct colour in images while preserving a high colour fidelity as compared to the original images. The choice of a good colour palette is crucial as it directly determines the quality of the resulting image. Colour quantisation can also be seen as a clustering problem where the task is to identify those clusters that best represent the colours in an image. In this paper we investigate the performance of various fuzzy c-means clustering algorithms for colour quantisation of images. In particular, we use conventional fuzzy c-means as well as some more efficient variants thereof, namely fast fuzzy c-means with random sampling, fast generalised fuzzy c-means, and a recently introduced anisotropic mean shift based fuzzy c-means algorithm. Experimental results show that fuzzy c-means performs significantly better than other, purpose built colour quantisation algorithms, and also confirm that the fast fuzzy clustering algorithms provide similar quantisation results to the full conventional fuzzy c-means approach.

Comparison on Color Quantization Techniques

Comparison on Color Quantization Techniques

A Robust Color Image Quantization Algorithm Based on Knowledge Reuse of K-Means Clustering Ensemble

This paper presents a novel color image quantization algorithm. This algorithm improves color image quantization stability and accuracy using clustering ensemble. In our approach, we firstly adopt manifold single k-means clusterings for the color image to form a preliminary ensemble committee. Then, in order to avoid inexplicit correspondence among clustering groups, we use the original color values of each clustering centroid directly to construct a final ensemble committee. A mixture model based on the expectation-maximization (EM) algorithm is used as a consensus function to combine the clustering groups of the final ensemble committee to obtain color quantization results. Experimental results reveal that the proposed color quantization algorithm is more stable and accurate than k-means clustering. The preprocessing step of the algorithm, k-means clustering, can be implemented and executed in parallel to improve processing speed.