The International Arab Journal of Information Technology (IAJIT)


Image Quality Assessment Employing RMS

This paper presents a new approach for evaluating image quality. The method is based on the histogram similarity computation between images and is organized with assessing quality index factors due to the contributions of correlation coefficient, average luminance distortion and rms contrast measurement. The effectiveness of this proposed RMS Contrast and Histogram Similarity (RCHS) based hybrid quality index has been justified over Lena images under different well known distortions and standard image databases. Experimental results demonstrate that this image quality assessment method performs better than those of widely used image distortion quality metric Mean Squared Error (MSE), Structural SIMilarity (SSIM) and Histogram based Image Quality (HIQ).

[1] Betrabet S. and Bhogayta C., Structural Similarity Based Image Quality Assessment Using Full Reference Method, International Journal of Scientific Engineering and Technology, vol. 4, no. 4, pp. 252-255, 2015. 988 The International Arab Journal of Information Technology, Vol. 15, No. 6, November 2018

[2] Bhuiyan A., Ampornaramveth V., Muto S., and Ueno H., On Tracking of Eye for Human-Robot Interface, International Journal of Robotics and Automation, vol. 19, no. 1, pp. 42-54, 2004.

[3] Bhuiyan A. and Liu C., Intelligent Vision System for Human-Robot Interface, International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol. 1, no. 4, pp. 862-868, 2007.

[4] Bhuiyan A., Liu C., and Ueno H., On Pose Estimation for Human-Robot Symbiosis, International Journal of Advanced Robotic Systems, vol. 5, no. 1, pp. 19-30, 2008.

[5] Carnec M., Callet P., and Barba D., Objective Quality Assessment of Color Images Based on A Generic Perceptual Reduced Reference, Signal Processing: Image Communication, vol. 23, no. 4, pp. 239-256, 2008.

[6] Celik T., Two-Dimensional Histogram Equalization and Contrast Enhancement, Pattern Recognition, vol. 45, no. 10, pp. 3810- 3824, 2012.

[7] Chen S., A Histogram Equalization-Based Contrast Enhancement Technique for Image Quality Assessment, Digital Signal Processing, vol. 22, no. 4, pp. 640-647, 2012.

[8] Chen Y. and Blum R., A New Automated Quality Assessment Algorithm for Image Fusion, Image and Vision Computing, vol. 27, no. 10, pp. 1421-1432, 2009.

[9] Colombo C. and Bimbo A., Color-Induced Image Representation and Retrieval, Pattern Recognition, vol. 32, no. 10, pp. 1685-1695, 1999.

[10] Eskicioglu A. and Fisher P., Image Quality Measures and their Performance, IEEE Transactions on Communications, vol. 43, no. 12, pp. 2959-2965, 1995.

[11] Eskicioglu A. and Fisher P., A survey of Quality Measures for Gray Scale Image Compression, in Proceedings of the Workshop on Space and Earth Science Data Compression, San Diego, pp. 304, 1993.

[12] Gonzalez R. and Woods R., Digital Image Processing, Prentice Hall, 2008.

[13] Kovaleski R. and Oliveira M., High-Quality Brightness Enhancement Functions for Real- Time Reverse Tone Mapping, The Visual Computer, vol. 25, pp. 539-547, 2009.

[14] Lee J. and Park R., Image Quality Assessment of Tone Mappled Images, International Journal of Computer Graphics and Animation, vol. 5, no. 2, pp. 9-20, 2015.

[15] Li H., Manjunath B., and Mitra S., Multisensor Image Fusion using the Wavelet Transform, Graphical Models and Image Processing, vol. 57, no. 3, pp. 235-245, 1995.

[16] Li J., Wang C., Li M. and Guo P., An Image Quality Assessment Algorithm on the Basis of Edge Information and Singular Value Decomposition, International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 8, no. 6, pp. 283-288, 2015.

[17] Moorthy A. and Bovik A., A Two-Step Framework for Constructing Blind Image Quality Indices, IEEE Signal Processing Letters, vol. 17, no. 5, pp. 513-516, 2010.

[18] Motwakel A. and Shaout A., Fingerprint Image Quality Fuzzy System, The International Arab Journal of Information Technology, vol. 13, no. 1A, pp. 171-177, 2016.

[19] Nakarnae E., Kaneda K., Harada K., Miwa T., Nishita T., and Saiki R., Reliability of Computer Graphic Images for Visual Assessment, The Visual Computer, vol. 7, no. 2-3, pp. 138-148, 1991.

[20] Peli E., Contrast in Complex Images, Journal of Optical Society, vol. 7, no. 10, pp. 2032-2040, 1990.

[21] Piella G., A General Framework for Multiresolution Image Fusion: From Pixels to Regions, Information Fusion, vol. 4, no. 4, pp. 259-280, 2003.

[22] Qu G., Zhang D., and Yan P., Information Measure for Performance of Image Fusion, Electronic Letters, vol. 38, no. 7, pp. 313-315, 2002.

[23] Rockinger O., Image Sequence Fusion Using A Shift Invariant Wavelet Transform, in Proceedings of International Conference Image Processing, Santa Barbara, pp. 288-291, 1997.

[24] Saad M., Bovik A., and Charrier C., A DCT Statistics-Based Blind Image Quality Index, IEEE Signal Processing Letters, vol. 17, no. 6, pp. 583-586, 2010.

[25] Seghir Z. and Hachou F., Edge-region Information Measure Based on Deformed and Displaced Pixel for Image Quality Assessment, Signal Processing Image Communication, vol. 26, no. 8, pp. 534-549, 2011.

[26] Sheikh H. and Bovik A., Image Information and Visual Quality, IEEE Transactions on Image Processing, vol. 15, no. 2, pp. 430-444, 2006.

[27] Teo P. and Heeger D., Perceptual Image Distortion, in Proceedings of 1st International Conference on Image Processing, Austin, pp. 127-141, 1994.

[28] Wang Z. and Bovik A., A Universal Image Quality Index, IEEE Signal Processing Letters, vol. 9, no. 3, pp. 81-84, 2002.

[29] Wang Z. and Bovik A., Mean Squared Error: Love it or Leave it?-A new Look at Signal Fidelity Measures, IEEE Signal Processing Magazine, vol. 26, no. 1, pp. 98-117, 2009. Image Quality Assessment Employing RMS Contrast and Histogram Similarity 989

[30] Wang Z., Bovik A., and Lu L., Why Is Image Quality Assessment So Difficult, in Proceedings of IEEE International Conference on Acoustics Speech and Signal Processing, Orlando, pp. 3313-3316, 2002.

[31] Wang Z., Bovik A., Sheikh H., and Simoncelli E., Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, 2004.

[32] Xydeas C. and Petrovic V., Objective Pixel- Level Image Fusion Performance Measure, Architectures, Algorithms and Applications, Orlando, pp. 88-99, 2000.

[33] Yalman Y., A Histogram based Image Quality Index, Electrical Review, vol. 88, no. 7, pp. 126- 129, 2012.

[34] Yuan T., Zheng X., Hu X., Zhou W., and Wang W., A Method for the Evaluation of Image Quality According to the Recognition Effectiveness of Objects in the Optical Remote Sensing Image Using Machine Learning Algorithm, PLOS Journal, vol. 9, no. 1, pp. 1-7, 2014.

[35] Zhang J. and Lee T., Kurtosis-based No- Reference Quality Assessment of JPEG2000 Images, Signal Processing Image Communication, vol. 26, no. 1, pp.13-23, 2011.

[36] Zhang Q., Eagleson R., and Peter T., High- Quality Cardiac Image Dynamic Visualization with Feature Enhancement and Virtual Surgical Tool Inclusion, The Visual Computer, vol. 25, no. 11, pp. 1019-1035, 2009. Al-Amin Bhuiyan graduated from University of Dhaka, Bangladesh and received his Ph. D from Osaka City University, Japan. He is a faculty member at the Department of Computer Engineering, King Faisal University, Saudi Arabia and under lien leave at Jahangirnagar University, Bangladesh. Prior to joining at King Faisal University, Dr. Bhuiyan lent his teaching and research experiences at several Universities in Japan, Bangladesh and UK. His research interests include image processing, computer graphics, pattern recognition, artificial intelligence, neural networks, robotic vision, and so on. He has published numerous articles in international refereed journals. Abdul Raouf Khan did his Masters and Ph.D from University of Kashmir. He served in University fo Kashmir for almost 13 years before joining Alzaytoonah University, Jordan in 2001. Presently, he is a teaching faculty member in the department of computer sciences, King Faisal University, Saudi Arabia. Dr. Khan has published various articles and research papers in the field of theory and applications of cellular automata, image processing, data security and computer architecture. Recently he received the best paper award in Korea.