A Simple Method for Color Quantization: Octree Quantization

Gervautz, Michael; Purgathofer, Werner

doi:10.1007/978-3-642-83492-9_20

Cited by 161 publications

(58 citation statements)

References 5 publications

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“…However, the selection of the optimal colour palette is known to be an np-hard problem [12]. In the image processing literature many different algorithms have been introduced that aim to find a palette that allows for good image quality of the quantised image [9,11,12]. Soft computing techniques such as genetic algorithms have also been employed to extract a suitable palette [16,18].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy clustering for colour reduction in images

Schaefer

Zhou

2008

Telecommun Syst

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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.

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Section: Introductionmentioning

confidence: 99%

Fuzzy clustering for colour reduction in images

Schaefer

Zhou

2008

Telecommun Syst

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“…The splitting approach [10,[17][18][19][20][21][22][23] divides the color space into a number of disjoint regions. It starts with a single region that contains all the original image colors.…”

Section: Introductionmentioning

confidence: 99%

Self-organizing map-based color palette for high-dynamic range texture compression

Xiao

Leung

Lam

et al. 2011

Neural Comput & Applic

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High-dynamic range (HDR) images are commonly used in computer graphics for accurate rendering. However, it is inefficient to store these images because of their large data size. Although vector quantization approach can be used to compress them, a large number of representative colors are still needed to preserve acceptable image quality. This paper presents an efficient color quantization approach to compress HDR images. In the proposed approach, a 1D/2D neighborhood structure is defined for the self-organizing map (SOM) approach and the SOM approach is then used to train a color palette. Afterward, a virtual color palette that has more codevectors is simulated by interpolating the trained color palette. The interpolation process is hardware supported in the current graphics hardware. Hence, there is no need to store the virtual color palette as the representative colors are constructed on the fly. Experimental results show that our approach can obtain good image quality with a moderate color palette.Keywords Self-organizing map (SOM) Á Color quantization Á High-dynamic range (HDR) Á Large color palette virtualization (LCPV)

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“…When initialized with a suitable preclustering method, WSM has been shown to outperform a large number of classic and state-of-the-art quantization methods including median-cut [11], octree [12], variance-based method [13], binary splitting method [14], greedy orthogonal bipartitioning method [15], neuquant [33], split and merge method [18], adaptive distributing units method [23,26], finite-state HCM method [19], and stable-flags HCM method [20].…”

Section: Hard C-means (Hcm) Clustering Algorithmmentioning

confidence: 99%

“…• Octree (OCT) [12]: This two-phase method first builds an octree (a tree data structure in which each internal node has up to eight children) that represents the color distribution of the input image and then, starting from the bottom of the tree, prunes the tree by merging its nodes until C colors are obtained. In the experiments, the tree depth was limited to 6.…”

Section: Comparison Of Hcm and Fcmmentioning

confidence: 99%

“…This initial cluster is recursively subdivided until C clusters are obtained. Well-known divisive methods include median-cut [11], octree [12], variancebased method [13], binary splitting method [14], and greedy orthogonal bipartitioning method [15]. On the other hand, agglomerative preclustering methods [16][17][18] start with N' singleton clusters each of which contains one image color.…”

Section: Introductionmentioning

confidence: 99%

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Hard versus fuzzy c-means clustering for color quantization

Wen

Çelebi

2011

EURASIP J. Adv. Signal Process.

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Color quantization is an important operation with many applications in graphics and image processing. Most quantization methods are essentially based on data clustering algorithms. Recent studies have demonstrated the effectiveness of hard c-means (k-means) clustering algorithm in this domain. Other studies reported similar findings pertaining to the fuzzy c-means algorithm. Interestingly, none of these studies directly compared the two types of c-means algorithms. In this study, we implement fast and exact variants of the hard and fuzzy c-means algorithms with several initialization schemes and then compare the resulting quantizers on a diverse set of images. The results demonstrate that fuzzy c-means is significantly slower than hard c-means, and that with respect to output quality, the former algorithm is neither objectively nor subjectively superior to the latter.

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A Simple Method for Color Quantization: Octree Quantization

Cited by 161 publications

References 5 publications

Fuzzy clustering for colour reduction in images

Fuzzy clustering for colour reduction in images

Self-organizing map-based color palette for high-dynamic range texture compression

Hard versus fuzzy c-means clustering for color quantization

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