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A Histogram based Image Quality Index

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PL
Indeks jakości obrazu bazujący na histogramie
Języki publikacji
EN
Abstrakty
EN
Image quality evaluation plays a very important role in any image processing application. A number of efforts have been made in the last decade to develop generalized image quality metrics. However, a common and easily applicable image quality measure is yet to be developed. In this paper, a new Histogram-based Image Quality Index (HQI) is proposed for use in place of traditional error summation methods. It can be calculated easily and performs significantly better than the widely used image distortion quality metric Mean Squared Error (MSE). A software release of the proposed image quality measure HQI and examples of its usage on different test images have been made available online: http://www.turgutozal.edu.tr/yyalman/contents/yyalman/files/HQI.zip.
PL
W artykule zaproponowano nową metodę poprawy jakości obrazu nazwaną HQI – Histogram based Image Quality Index. Metoda może zastąpić dotychczas stosowaną metodę sumowania błędów.
Rocznik
Strony
126--129
Opis fizyczny
Bibliogr. 16 poz., il., tab., wykr.
Twórcy
autor
Bibliografia
  • [1] Wang Z., Bovik A. C., Sheikh H. D. Simoncelli E. P., Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Transactions on Image Processing, vol. 13 (2004), 600–612.
  • [2] Sheikh H. D., Bovik A. C., Image Information and Visual Quality, IEEE Transactions on Image Processing, vol. 15 (2006), 430–444.
  • [3] Wang Z., Bovik A. C., A Universal Image Quality Index, IEEE Signal Processing Letters, vol. 9 (2002), 81–84.
  • [4] Cetin O., Ozcerit A.T., A New Steganography Algorithm Based on Color Histograms for Data Embedding into Raw Video Streams, Computers & Security, (2009), No. 28, 670−682.
  • [5] Egiazarian K., Astola J., Ponomarenko N., Lukin V., Battisti F., Carli M. New Full-reference Quality Metrics based on HVS, 2nd Int. Workshop on Video Processing and Quality Metrics, USA, (2006), 4 p.
  • [6] Ponomarenko N., Silvestri F., Egiazarian K., Carli M., Lukin V., On Between-Coefficient Contrast Masking of DCT Basis Functions, 3rd Int. Workshop on Video Processing and Quality Metrics, (2007), 4 p.
  • [7] Moorthy A. K., Bovik A. C., A Two-Step Framework for Constructing Blind Image Quality Indices, IEEE Signal Processing Letters, 2010, No 17(5), 513–516.
  • [8] Girod B. What’s Wrong With Mean-Squared Error in Digital Images and Human Vision, A. B. Watson, Ed. Cambridge, MA: MIT Press, (1993), 207–220.
  • [9] Teo P. C., Heeger D. J., Perceptual Image Distortion, in Proc. SPIE, (1994), No. 2179, 127–141.
  • [10] Eskicioglu A. M., Fisher P. S., Image Quality Measures and Their Performance, IEEE Transactions on Communications, (1995), No. 43, 2959–2965.
  • [11] Eckert M. P., Bradley A. P., Perceptual Quality Metrics Applied to Still Image Compression, Signal Processing, Vol. 70 (1998), 177–200.
  • [12] Winkler S. A. Perceptual Distortion Metric for Digital Color Video, in Proc. SPIE., Vol. 3644 (1999), 175–184.
  • [13] Gilewska, G., Matching Algorithms During Analysis of Medical Imaging Parameters: Possibilities and Constrains, Przegląd Elektrotechniczny, Vol. 10 (2010), 190–192.
  • [14] Vishwakarma V. P., Pandey S., Gupta M. N., Adaptive Histogram Equalization and Logarithm Transform with Rescaled Low Frequency DCT Coefficients for Illumination Normalization, International Journal of Recent Trends in Engineering, vol. 1 (2009), 318–322.
  • [15] Stangor C., Research Methods for the Behavioral Sciences, University of Maryland, 2011.
  • [16] http://www.cns.nyu.edu/~zwang/files/research/quality_index/demo_lena.html, Accessed Date: October, 2011.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPOH-0066-0002
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