A Simple Method for Color Quantization: Octree Quantization

Gervautz, Michael; Purgathofer, Werner

doi:10.1016/b978-0-08-050753-8.50061-9

Cited by 128 publications

(99 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The octree quantization algorithm [249], [250] and the center-cut algorithm [251] are two simple color quantization schemes motivated by the idea of limiting the maximum error. The center-cut algorithm is a minor modification of the median-cut algorithm, in which the cluster with the largest dimension along a coordinate axis is split along the center at each step.…”

Section: A Color Quantizationmentioning

confidence: 99%

Digital color imaging

Sharma

Trussell

1997

IEEE Trans. on Image Process.

367

200

View full text Add to dashboard Cite

Abstract-This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented using vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided.

show abstract

Section: A Color Quantizationmentioning

confidence: 99%

Digital color imaging

Sharma

Trussell

1997

IEEE Trans. on Image Process.

367

200

View full text Add to dashboard Cite

show abstract

“…Through psychovisual experiment, we found that the visual quality of the BTC coded images were not greatly affected by color quantizing the mean colors [19]. Therefore, for each BTC coded image, a color table (palette) [20] consists of 256 colors are constructed and stored. The mean colors are coded (8 bits per mean color) as the indices of the colors in the color table that are the closest to the mean colors.…”

Section: Btc For Color Image Codingmentioning

confidence: 99%

Color image indexing using BTC

Qiu

2003

IEEE Trans. on Image Process.

127

View full text Add to dashboard Cite

Abstract-This paper presents a new application of a wellstudied image coding technique, namely block truncation coding (BTC). It is shown that BTC can not only be used for compressing color images, it can also be conveniently used for content-based image retrieval from image databases. From the BTC compressed stream (without performing decoding), we derive two image content description features, one termed the block color co-occurrence matrix (BCCM) and the other block pattern histogram (BPH). We use BCCM and BPH to compute the similarity measures of images for content-based image retrieval applications. Experimental results are presented which demonstrate that BCCM and BPH are comparable to similar state of the art techniques.Index Terms-Block truncation coding (BTC), color quantization, content-based image retrieval, image coding, image database.

show abstract

“…Color quantization plays a very important part in image compression, image segmentation and image restoration. In recent years, several color image quantization algorithms have been put forward and applied, such as c-means algorithm [1] and fuzzy c-means algorithm [2] based on clustering, and median-cut quantization algorithm [3] and octree quantization algorithm [4] based on cutting. Both octree algorithm and fuzzy c-means algorithm have achieved satisfactory results when they are applied to color quantization under certain circumstance, but they can reach such satisfactory result only when the colors of an quantized image is known or the color number is determined previously.…”

Section: Introductionmentioning

confidence: 99%

A New Color Image Quantization Algorithm Based on Fuzzy Kernel Clustering

MA¹

2011

IJEME

View full text Add to dashboard Cite

A new color image quantization algorithm based on fuzzy kernel clustering is studied in this paper. Firstly, the original image is quantized to 256 colors using the octree algorithm. Secondly, based on the quantitative relation of the NBS distance and the color difference of human vision, the initial clustering centers and number are determined automatically. Thirdly, the clustering center color values are modified by use of the fuzzy kernel clustering algorithm in the Munsell space. And then, a color image quantization effect is achieved. At last, simulations are performed on the presented algorithm, and the simulation result shows that the presented algorithm not only can solve the problem of giving the number of quantization in advance but also has better quantization effect than the octree algorithm and fuzzy c-means algorithm in the same quantization number.

show abstract

A Simple Method for Color Quantization: Octree Quantization

Cited by 128 publications

References 0 publications

Digital color imaging

Digital color imaging

Color image indexing using BTC

A New Color Image Quantization Algorithm Based on Fuzzy Kernel Clustering

Contact Info

Product

Resources

About