An Objective No-Reference Measure of Illumination Assessment

Abstract Illumination problems have been an important concern in many image processing applications. An important issue in the field of illumination enhancement is absence of a quantitative measure for assessment of illumination of an image. In this research work a quantitative measure indicating the illumination state, i.e. contrast level and brightness of an image is also proposed. The measure utilises the estimated Gaussian distribution of the input image and the Kullback-Leibler Divergence between the estimated Gaussian distribution and the desired Gaussian distribution to calculate the quantitative measure. The experimental results show the effectiveness and the reliability of proposed illumination assessment measure.

[1]  Manjunatha Mahadevappa,et al.  Brightness preserving dynamic fuzzy histogram equalization , 2010, IEEE Transactions on Consumer Electronics.

[2]  Weisi Lin,et al.  Perceptual visual quality metrics: A survey , 2011, J. Vis. Commun. Image Represent..

[3]  Nor Ashidi Mat Isa,et al.  Fuzzy image enhancement for low contrast and non-uniform illumination images , 2013, 2013 IEEE International Conference on Signal and Image Processing Applications.

[4]  Gholamreza Anbarjafari,et al.  Satellite Image Contrast Enhancement Using Discrete Wavelet Transform and Singular Value Decomposition , 2010, IEEE Geoscience and Remote Sensing Letters.

[5]  Michel Herbin,et al.  No-reference Image Semantic Quality Approach using Neural Network , 2011, 2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[6]  James M. Joyce Kullback-Leibler Divergence , 2011, International Encyclopedia of Statistical Science.

[7]  Mohammed Ghanbari,et al.  Scope of validity of PSNR in image/video quality assessment , 2008 .

[8]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[9]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[10]  M. Gaata,et al.  No-reference quality metric based on fuzzy neural network for subjective image watermarking evaluation , 2012, 2012 IEEE International Conference on Imaging Systems and Techniques Proceedings.

[11]  Gholamreza Anbarjafari Face recognition using color local binary pattern from mutually independent color channels , 2013, EURASIP J. Image Video Process..

[12]  Jeng-Shyang Pan,et al.  No-Reference Image Quality Assessment in Spatial Domain , 2014, ICGEC.

[13]  Rabab Kreidieh Ward,et al.  Adaptive Region-Based Image Enhancement Method for Robust Face Recognition Under Variable Illumination Conditions , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[14]  Thirimachos Bourlai,et al.  Quality metrics for practical face recognition , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).