Data Content Weighing for Subjective versus Objective Picture Quality Assessment of Natural Pictures

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Author(s)

Suresha D 1,* Prakash H N 2

1. Visvesvaraya Technological University, Belagavi, 590018, India

2. Department of Computer Science and Engineering, Rajeev Institute of Technology, Hassan, 573201, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2017.02.04

Received: 13 Oct. 2016 / Revised: 25 Nov. 2016 / Accepted: 23 Dec. 2016 / Published: 8 Feb. 2017

Index Terms

Gaussian, Local Variance, Poisson, Salt and Pepper, Speckle, Structural SIMilarity, Mean Squared Error, Root Mean Squared Error, Peak Signal to Noise Ratio, Entropy

Abstract

Estimating the visual quality of picture is a real challenge for various picture and video frame applications. The aim is to evaluate the quality of picture automatically in both subjective (human visual frame work) and objectively. The quality of picture is evaluated by comparing precision and closeness of a picture with reference or error free picture. The quality estimation can be done to achieve consistency in desired quality of picture with help of modeling remarkable physiological, psycho visual components framework and picture fidelity measure methods. In this article, the picture quality is evaluated by analyzing loss of picture information of the distortion system using differing noise models and examine the relationship between picture data, visual quality and error metric. The quality of picture & video frame assessment is really important that, every human can judge the visual quality of natural picture. The subjective quality of picture is assessed by using structural similarity metric, objective quality of picture is computed by root means squared error, mean squared error and peak signal to noise ratio and data content in picture is weighted through entropy.

Cite This Paper

Suresha D, H N Prakash,"Data Content Weighing for Subjective versus Objective Picture Quality Assessment of Natural Pictures", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.2, pp.27-36, 2017. DOI: 10.5815/ijigsp.2017.02.04

Reference

[1]Ajay Kumar Boyat and Brijendra Kumar Joshi, "A Review Paper: Noise Models in Digital Image Processing," Signal & Image Processing : An International Journal (SIPIJ) Vol.6, No.2, pp. 63–75, 2015.

[2]Nikolay Ponomarenko, Vladimir Lukin, Mikhail Zriakhov,Karen Egiazarian, and Jaakko Astola, "Lossy Compression of Images with Additive Noise," 7th International Conference, ACIVS 2005, Antwerp, Belgium, September 20-23, pp. 381–386, 2005, DOI: 10.1007/11558484_48

[3]David S. Lalush, "Binary Encoding of Multiplexed Images in Mixed Noise," IEEE Transactions on Medical Imaging, vol. 27, no. 9, pp. 1323–1332, 2008, DOI: 10.1109/TMI.2008.922697.

[4]Claude E. Shannon, "Communication in the Presence of Noise," Proceedings of the IEEE, vol. 86, no. 2, February 1998.

[5]Aria Nosratinia, "Post-Processing of JPEG-2000 Images to Remove Compression Artifacts," IEEE Signal Processing Letters,vol. XX, pp. 1-1, 2002.

[6]R. Ramani,N.Suthanthira Vanitha,S. Valarmathy,"The Pre-Processing Techniques for Breast Cancer Detection in Mammography Images", International Journal of Image, Graphics and Signal Processing(IJIGSP), vol.5, no.5, pp.47-54, 2013.DOI: 10.5815/ijigsp.2013.05.06.

[7]Yubing Wang, "Survey of Objective Video Quality Measurements," The Institute for Telecommunication Science, pp. 1–7.

[8]Stefan Winkler and Praveen Mohandas, "The Evolution of Video Quality Measurement : From PSNR to Hybrid Metrics The Evolution of Video Quality Measurement : From PSNR to Hybrid Metrics," IEEE transactions on Broadcasting, vol. 54, no. 3, September 2008 

[9]M. Kudelka, "Image Quality Assessment," WDS'12 Proceedings of Contributed Papers, Part I, 94–99, ISBN 978-80-7378-224-5, 2012,

[10]Hasan Demirel and Gholamreza Anbarjafari, "Satellite Image Resolution Enhancement Using Complex Wavelet Transform," IEEE geoscience and remote sensing letters, vol. 7, no. 1, pp. 123–126, 2010.

[11]Jincy.C and Rini.M.E, "Performance Analysis of Target Recognition in Synthetic Aperture Radar Images," International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 5, Special Issue 2, pp. 171–175, 2016.

[12]S. Sakamoto and H. Mitsuoka, "Totally Mechanized Construction System for High - Rise Buildings (T-UP System)," Proceedings of the 11th ISARC, Brighton, United Kingdom, no. ii, pp. 465–472, 1994.

[13]"Add noise to image - MATLAB imnoise - MathWorks India." [Online]. Available: http://in.mathworks.com/help/images/ref/imnoise.html.

[14]R. Gonzalez and R. Woods, "Digital image processing," Prentice Hall, 2002.

[15]Samuel W. Hasinoff, "Photon, Poisson Noise," Computer Vision. A Reference Guide, Springer Science Business Media New York, pp. 608–610, 2014, DOI:10.1007/978-0-387-31439-6_482.

[16]R. H. Chan, C. W. Ho, and M. Nikolova, "Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization," IEEE Transactions on Image Processing, vol. 14, no. 10, pp. 1479–1485, 2005.

[17]Jyoti Jaybhay and Rajveer Shastri, "a Study of Speckle Noise Reduction Filters," Signal & Image Processing : An International Journal (SIPIJ), vol. 6, no. 3, pp. 71-80, 2015, DOI : 10.5121/sipij.2015.6306.

[18]Zhou Wang, Alan C. Bovik, Hamid R. Sheikh and Eero P. Simoncelli, "Image Quality Assessment : From Error Visibility to Structural Similarity," IEEE transactions on image processing, vol. 13, no. 4, pp. 1-14, April 2004.

[19]Je-Ho Park and Changwon Kang, "Entropy Based Image Identifier Generation," in 2014 International Conference on IT Convergence and Security (ICITCS), 2014, pp. 1–2, DOI: 10.1109/ICITCS.2014.7021766.

[20]"Entropy of grayscale image - MATLAB entropy - MathWorks India." [Online]. Available: http://in.mathworks.com/help/images/ref/entropy.html.

[21]Suresha D, Prakash H N,"Natural Image Super Resolution through Modified Adaptive Bilinear Interpolation Combined with Contra Harmonic Mean and Adaptive Median Filter", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.2, pp.1-8, 2016.DOI: 10.5815/ijigsp.2016.02.01 

[22]G.Rohith and A. Vasuki, "A Novel Approach to Super Resolution Image Reconstruction Algorithm from Low Resolution Panchromatic Images," in 3rd International Conference on Signal Processing, Communication and Networking(ICSCN), 2015, no. x.

[23]Hasan Demirel and Gholamreza Anbarjafari, "IMAGE resolution enhancement by using discrete and stationary wavelet decomposition.," IEEE Transactions on Image Processing, vol. 20, no. 5, pp. 1458–1460, 2011.

[24]"SIPI Image Database - Misc." [Online]. Available: http://sipi.usc.edu/database/database.php?volume=misc. [Accessed: 25-Feb-2016].