Color image compression using quantization, thresholding, and edge detection techniques all based on the moment-preserving principle

Yang, Chen-Kuei; Tsai, Wen‐Hsiang

doi:10.1016/s0167-8655(97)00166-9

Cited by 42 publications

(13 citation statements)

References 22 publications

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“…However, color quantization is still an important problem in the fields of image processing and computer graphics [2]. It can be used in mobile and hand-held devices where memory is usually small [25], it can be used for low-cost display and printing devices where only a small number of colors can be displayed or printed simultaneously [27], it also has been used as a preprocessing step for many applications such as object recognition [33], image compression [41], and contentbased image retrieval (CBIR) [34]. Another aspect of importance of color quantization is that the human visual system cannot perceive a large number of colors at the same time, nor is it able to distinguish close colors well [21] even though under appropriate adaptation, it cannot distinguish more than two million colors [18] while a full color image may contain up to 16 million different colors.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Image Quality Assessment Metrics: Color Quantization Noise

Hassan¹,

Bhagvati²

2015

IJAIS

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Although color quantization noise is frequently met in practice, it has not been given too much attention in color image visual quality assessment. In this paper, a new image database for the evaluation of image quality metrics over color quantization noise is described. It contains 25 reference images and 875 test images produced by five popular quantization algorithms. Each of the quantized images was evaluated by 22 human subjects and more than 19200 individual human quality judgments were carried out to obtain the final mean opinion scores. A comparative analysis of several well-known image quality metrics is presented and their correlation with the human opinion scores is evaluated. This image database has been made freely available for downloading for research in image quality assessment and other applications [10]. General Terms:Subjective image quality assessment, Objective image quality assessment

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Section: Introductionmentioning

confidence: 99%

Evaluation of Image Quality Assessment Metrics: Color Quantization Noise

Hassan¹,

Bhagvati²

2015

IJAIS

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“…It can be used in mobile and hand-held devices where memory is usually small [26], it can be used for low-cost display and printing devices where only a small number of colors can be displayed or printed simultaneously [29]. It also has been used as a preprocessing step for many applications such as object recognition [31], image compression [35], and content-based image retrieval (CBIR) [32].…”

Section: Introductionmentioning

confidence: 99%

Joint Color Quantization and Dithering Techniques

Ahmed¹

2015

CAE

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Color quantization process is considered in two stages: the selection of an optimal color palette and the mapping of each pixel of the image to a color from the color palette. Since the color palette is limited, some disturbing degradations such as false contours are visible on delivered color quantized images. A common way to overcome this problem is the use of dithering techniques. In this paper, two methods for color quantization are proposed for the use with color dithering techniques in a way that better results will be obtained after dithering. The results show that the proposed methods, when used with dithering techniques, significantly improve the visual quality of the resulting color quantized images compared to the traditional color quantization algorithms.

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“…Although 24-bit display hardware has become more common, color quantization still maintains its practical value [1]. Modern applications of color quantization in graphics and image processing include: (i) compression [2], (ii) segmentation [3], (iii) text localization/detection [4], (iv) color-texture analysis [5], (v) watermarking [6], (vi) non-photorealistic rendering [7], (vii) and contentbased retrieval [8].…”

Section: Introductionmentioning

confidence: 99%

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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Color image compression using quantization, thresholding, and edge detection techniques all based on the moment-preserving principle

Cited by 42 publications

References 22 publications

Evaluation of Image Quality Assessment Metrics: Color Quantization Noise

Evaluation of Image Quality Assessment Metrics: Color Quantization Noise

Joint Color Quantization and Dithering Techniques

Hard versus fuzzy c-means clustering for color quantization

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