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Computing contrast ratio in images using local content information

Benhur Ortiz Jaramillo (UGent) , Asli Kumcu (UGent) , Ljiljana Platisa (UGent) and Wilfried Philips (UGent)
Author
Organization
Project
Panorama project
Abstract
It is well know that a measure of contrast in images is not yet fully defined. The conventional measures of contrast consist of global computations and therefore they have a poor performance. At the same time image quality assessment is often based on quantifying the visibility between a structure of interest or foreground and its surrounding background, i.e., the contrast ratio. Then, a high quality image is the one in which structures of interest are well distinguishable from the background. Therefore, the computation of contrast ratio is important in automatic image quality assessment and it should be computed locally taking into account the local distribution of pixel values. We estimate the contrast ratio by using Weber contrast in local image patches. The main contribution of this work lies in the characterization of local distribution of pixel values which is used for computing the contrast ratio. Here, local image patches are characterized by bimodal histograms representing a set of pixels which are likely to be inside the foreground and another set likely to be in the background. The local contrast ratio is estimated using the ratio between mean intensity values of each mode of the histogram. Our experimental results over two public image databases show that the proposed method is able to accurately predict changes of quality due to contrast decrements (Pearson correlations higher than 90%).
Keywords
Weber's law, local content information, Contrast ratio, image quality

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Citation

Please use this url to cite or link to this publication:

Chicago
Ortiz Jaramillo, Benhur, Asli Kumcu, Ljiljana Platisa, and Wilfried Philips. 2015. “Computing Contrast Ratio in Images Using Local Content Information.” In 2015 20TH SYMPOSIUM ON SIGNAL PROCESSING, IMAGES AND COMPUTER VISION (STSIVA), 1–6.
APA
Ortiz Jaramillo, B., Kumcu, A., Platisa, L., & Philips, W. (2015). Computing contrast ratio in images using local content information. 2015 20TH SYMPOSIUM ON SIGNAL PROCESSING, IMAGES AND COMPUTER VISION (STSIVA) (pp. 1–6). Presented at the 20th SYMPOSIUM ON SIGNAL PROCESSING, IMAGES AND COMPUTER VISION (STSIVA).
Vancouver
1.
Ortiz Jaramillo B, Kumcu A, Platisa L, Philips W. Computing contrast ratio in images using local content information. 2015 20TH SYMPOSIUM ON SIGNAL PROCESSING, IMAGES AND COMPUTER VISION (STSIVA). 2015. p. 1–6.
MLA
Ortiz Jaramillo, Benhur, Asli Kumcu, Ljiljana Platisa, et al. “Computing Contrast Ratio in Images Using Local Content Information.” 2015 20TH SYMPOSIUM ON SIGNAL PROCESSING, IMAGES AND COMPUTER VISION (STSIVA). 2015. 1–6. Print.
@inproceedings{6936926,
  abstract     = {It is well know that a measure of contrast in images is not yet fully defined. The conventional measures of contrast consist of global computations and therefore they have a poor performance. At the same time image quality assessment is often based on quantifying the visibility between a structure of interest or foreground and its surrounding background, i.e., the contrast ratio. Then, a high quality image is the one in which structures of interest are well distinguishable from the background. Therefore, the computation of contrast ratio is important in automatic image quality assessment and it should be computed locally taking into account the local distribution of pixel values. 

We estimate the contrast ratio by using Weber contrast in local image patches. The main contribution of this work lies in the characterization of local distribution of pixel values which is used for computing the contrast ratio. Here, local image patches are characterized by bimodal histograms representing a set of pixels which are likely to be inside the foreground and another set likely to be in the background. The local contrast ratio is estimated using the ratio between mean intensity values of each mode of the histogram. Our experimental results over two public image databases show that the proposed method is able to accurately predict changes of quality due to contrast decrements (Pearson correlations higher than 90\%).},
  author       = {Ortiz Jaramillo, Benhur and Kumcu, Asli and Platisa, Ljiljana and Philips, Wilfried},
  booktitle    = {2015 20TH SYMPOSIUM ON SIGNAL PROCESSING, IMAGES AND COMPUTER VISION (STSIVA)},
  isbn         = {978-1-4673-9461-1},
  keyword      = {Weber's law,local content information,Contrast ratio,image quality},
  language     = {eng},
  location     = {Bogota, Colombia},
  pages        = {1--6},
  title        = {Computing contrast ratio in images using local content information},
  year         = {2015},
}

Web of Science
Times cited: