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.

Digital color imaging

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 us-ing 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.

Digital Color Imaging

Improving the performance of k-means for color quantization

When data categories have strong color associations, it is useful to use these semantically meaningful concept-color associations in data visualizations. In this paper, we explore how linguistic information about the terms defining the data can be used to generate semantically meaningful colors. To do this effectively, we need first to establish that a term has a strong semantic color association, then discover which color or colors express it. Using co-occurrence measures of color name frequencies from Google n-grams, we define a measure for colorability that describes how strongly associated a given term is to any of a set of basic color terms. We then show how this colorability score can be used with additional semantic analysis to rank and retrieve a representative color from Google Images. Alternatively, we use symbolic relationships defined by WordNet to select identity colors for categories such as countries or brands. To create visually distinct color palettes, we use k-means clustering to create visually distinct sets, iteratively reassigning terms with multiple basic color associations as needed. This can be additionally constrained to use colors only in a predefined palette.

/pdf/a-linguistic-approach-to-categorical-color-assignment-for-2bmtp3pjqj.pdf

A Linguistic Approach to Categorical Color Assignment for Data Visualization

One of the numerical criteria for color image quantization is to minimize the maximum discrepancy between original pixel colors and the corresponding quantized colors. This is typically carried out by first grouping color points into tight clusters and then finding a representative for each cluster. In this article we show that getting the smallest clusters under a formal notion of minimizing the maximum intercluster distance does not guarantee an optimal solution for the quantization criterion. Nevertheless our use of an efficient clustering algorithm by Teofilo F. Gonzalez, which is optimal with respect to the approximation bound of the clustering problem, has resulted in a fast and effective quantizer. This new quantizer is highly competitive and excels when quantization errors need to be well capped and when the performance of other quantizers may be hindered by such factors as low number of quantized colors or unfavorable pixel population distribution. Both computer-synthesized and photographic images are used in experimental comparison with several existing quantization methods.

Color image quantization by minimizing the maximum intercluster distance

Three simple and effective modifications made to the median-cut algorithm lead to a new and useful tool for color-image quantization. The first is to select the color box with the longest longest-dimension instead of the highest pixel count. The second is to cut through the center of the color box instead of bisecting the box's pixel count. The third is to use 3-2-4 instead of 3-3-3 bit-cutting for color reduction.

Center-cut for color-image quantization

We present a simple and "well-behaved" agglomerative clustering approach to color image quantization, along with results of tests using synthesized and real-life images. >

Color image quantization by agglomerative clustering

Reducing false contours in quantized color images

Minimizing visible distortion in a quantized color image is context-dependent. Our feedback- based strategy for color image quantization looks at the quantized image as well as the original. This comparison yields useful information to guide the embedded quantization algorithm to devote, during re-quantization of the original image, more resources to areas where the most offensive distortion occurred. Our current implementation of this new strategy uses an edge detector in a scaled RGB space to reveal the location and severeness of false contours, which appear in the quantized image but not in the original. The result of this false- contour detection step is used to identify uniformly colored regions in the quantized image that are along side of significant false contours. These regions correspond directly to areas in the original image that need to be better preserved during re-quantization. A well-known divisive method and our own agglomerative method are adapted separately as the embedded quantization algorithm to demonstrate the applicability and effectiveness of this feedback-based approach.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Feedback-based quantization of color images

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Papers

Center-cut for color-image quantization

Color image quantization by agglomerative clustering

Reducing false contours in quantized color images

Feedback-based quantization of color images