Region-Based Coding of Color Images Using Karhunen–Loeve Transform

Carević, Dragana; Caelli, Terry

doi:10.1006/gmip.1996.0402

Cited by 20 publications

(14 citation statements)

References 32 publications

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“…Applications of colour segmentation range from adaptive image compression [6,33] over object location [33,5], skin colour detection [25], image retrieval [53] and multimedia applications [23] to biomedicine [48,47] to name but a few. In the following a short overview can be given only as far as there is a close connection to the paper.…”

Section: Colour Segmentation Algorithmsmentioning

confidence: 99%

Region saliency as a measure for colour segmentation stability

Heidemann

2008

Image and Vision Computing

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Section: Colour Segmentation Algorithmsmentioning

confidence: 99%

Region saliency as a measure for colour segmentation stability

Heidemann

2008

Image and Vision Computing

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“…The table also provides a brief description as well as symbols for each feature. It is important to emphasize that the performance of each individual feature has been studied previously either in [8], [12], or [13]. Here, more [3] reliable and robust image quality estimators are proposed by combining multiple features to form a composite metric.…”

Section: A Feature Setmentioning

confidence: 99%

Improving Robustness of Image Quality Measurement with Degradation Classification and Machine Learning

Falk

Guo

Chan

2007

2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers

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Abstract-Image quality metrics can be classified as generic or degradation specific. Degradation specific measures perform poorly under "mismatched" conditions. Generic measures, on the other hand, may compromise quality measurement accuracy while gaining robustness to variation in distortion conditions. To improve the accuracy-robustness tradeoff, we employ supportvector degradation classification and machine learning tools to judiciously combine generic and degradation specific measures. To test our algorithm, composite quality metrics are optimized for five different distortion classes. Experiment results show that the proposed algorithm achieves improved performance and robustness relative to two benchmark generic quality metrics.

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“…44 Edge maps at different scales have been obtained from the images using the Canny 43 operator for different scale parameters ͑with standard deviation of the Gaussian filter assuming values of m ϭ1.19, 1.44, 1.68, 2.0, and 2.38͒. The output of this operator at scale m is decided at the threshold T m , where T m ϭ0.1(C max ϪC min ) ϩC min .…”

Section: C2 Edge Stability Measurementioning

confidence: 99%

Statistical evaluation of image quality measures

Avcibaş

Sankur

Sayood

2002

J. Electron. Imaging

524

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Abstract. In this work we comprehensively categorize image quality measures, extend measures defined for gray scale images to their multispectral case, and propose novel image quality measures. They are categorized into pixel difference-based, correlation-based, edge-based, spectral-based, context-based and human visual system (HVS)-based measures. Furthermore we compare these measures statistically for still image compression applications. The statistical behavior of the measures and their sensitivity to coding artifacts are investigated via analysis of variance techniques. Their similarities or differences are illustrated by plotting their Kohonen maps. Measures that give consistent scores across an image class and that are sensitive to coding artifacts are pointed out. It was found that measures based on the phase spectrum, the multiresolution distance or the HVS filtered mean square error are computationally simple and are more responsive to coding artifacts. We also demonstrate the utility of combining selected quality metrics in building a steganalysis tool.

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Region-Based Coding of Color Images Using Karhunen–Loeve Transform

Cited by 20 publications

References 32 publications

Region saliency as a measure for colour segmentation stability

Region saliency as a measure for colour segmentation stability

Improving Robustness of Image Quality Measurement with Degradation Classification and Machine Learning

Statistical evaluation of image quality measures

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