We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

Use of Gamma Encoder for Image Processing considering Human Visualization

by Md. Zahid Hasan, T. M. Shahriar Sazzad, Md. Hasibur Rahman
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 58 - Number 10
Year of Publication: 2012
Authors: Md. Zahid Hasan, T. M. Shahriar Sazzad, Md. Hasibur Rahman
10.5120/9315-3547

Md. Zahid Hasan, T. M. Shahriar Sazzad, Md. Hasibur Rahman . Use of Gamma Encoder for Image Processing considering Human Visualization. International Journal of Computer Applications. 58, 10 ( November 2012), 1-5. DOI=10.5120/9315-3547

@article{ 10.5120/9315-3547,
author = { Md. Zahid Hasan, T. M. Shahriar Sazzad, Md. Hasibur Rahman },
title = { Use of Gamma Encoder for Image Processing considering Human Visualization },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 58 },
number = { 10 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume58/number10/9315-3547/ },
doi = { 10.5120/9315-3547 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:02:03.721844+05:30
%A Md. Zahid Hasan
%A T. M. Shahriar Sazzad
%A Md. Hasibur Rahman
%T Use of Gamma Encoder for Image Processing considering Human Visualization
%J International Journal of Computer Applications
%@ 0975-8887
%V 58
%N 10
%P 1-5
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image processing is a promptly developing field which finds more and more application in various information and technical systems such as: radar-tracking, communications, televisions, Biomedical image, etc. The RGB color model is standard design of computer graphics system is not ideal for all of its applications. The red, green and blue colors are highly correlated. This makes it difficult to execute the image processing algorithm. Gamma encoding of images is required to compensate for properties of human vision, to maximize the use of the bits or bandwidth relative to how humans perceive light and color. Human vision under common illumination conditions follows an approximate gamma or power function. If images are not gamma encoded, they allocate too many bits or too much bandwidth to highlights that humans cannot differentiate, and too few bits/bandwidth to shadow values that humans are sensitive to and would require more bits/bandwidth to maintain the same visual quality. Image enhancement is another technique to improve the image quality for human visualization but sometimes it does not improve the quality when the images need to be darkened or brighten. Hence, this is not a good idea to brighten images all the time when better human visualization can be obtained while darkening the images. Better human visualization is important for manual image processing which leads to compare the outcome with the semi-automated or automated one. Considering the importance of gamma encoding in image processing we propose a new method of image analysis approach which will improve visual quality for manual processing as well as will lead analyzers to analyze images automatically for comparison and testing purpose.

References
  1. C. Solomon, T. Breckon. 2011. Fundamentals of Digital Image Processing.
  2. Nishu, Sunil A. 2012, Quantifying the defect visibility in digital images by proper color space selection, International journal of engineering research and applications, vol. 2, Issue 3, pp. 1764-1767.
  3. Raunaq M. and Utkarsh U. , 2008, Hus-preserving color image enhancement without gamut problem, Term paper, pp. 1-6.
  4. Yusuf Abu S. , Nijad Al-Najdawi, Sara T. , 2011, Exploiting Hybrid methods for enhancing digital X-Ray Images, International Arab journal of information technology, vol. 8.
  5. Umesh R. , Zhou W. , Eero P. S. , 2009, Quantifying color image distortions based on adaptive spatio-chromatic signal decompositions, IEEE international conference on image processing.
  6. Hana Al-Nuaim, Nouf A. , 2011, A user perceived quality assessment of lossy compressed images, International journal of computer graphics, vol. 2, No. 2, pp. 23-36.
  7. R. C. Gonzalez and R. E. woods, 2007, Digital Image Processing, 3rd Edition, Prentice Hall, Upper Saddle River, NJ.
  8. Jian-feng Li, Kaun-Quan Wang, David Zhang, 2002, A New equation of saturation in RGB-TO-HIS conversion for more rapidity of computing, Proceedings of the international conference on machine learning and cybernertics, pp. 1493-1497.
  9. Papoulis, A. , 1968, Systems and Transforms with Applications in Optics, New York: McGraw-Hill.
  10. Russ, J. C. , 1995, the Image Processing Handbook. Second ed. , Boca Raton, Florida: CRC Press.
  11. R. E. Blake, 1999, Partitioning Graph Matching with Constraints, Pattern Recognition, Vol 27, No. 3, pp 439-446.
  12. J. Foley, A. van Dam, S. Feiner and J. Hughes, 1990, Computer Graphics: Principles and Practice, Second Edition, Addison-Wesley, Reading, MA.
  13. R. E. Blake and P. Boros, 1995, The Extraction of Structural Features for Use in Computer Vision. Proceedings of the Second Asian Conference on Computer Vision, Singapore.
Index Terms

Computer Science
Information Sciences

Keywords

HSI color model Human Visualization Gamma Encoder Image processing RGB Color model