Abstract Prior to this work, Human Visual Perception (HVP)-based Image Quality Analyzer (IQA) has been proposed. The HVP-based IQA correlates with human judgment better than the existing IQAs which are commonly used for the assessment of contrast enhancement techniques. This paper highlights the shortcomings of the HVP-based IQA such as high computational complexity, excessive (six) threshold parameter tuning and high performance sensitivity to the change in the threshold parameters’ value. In order to overcome the aforementioned problems, this paper proposes several enhancements such as replacement of local entropy with edge magnitude in sub-image texture analysis, down-sampling of image spatial resolution, removal of luminance masking and incorporation of famous Weber-Fechner Law on human perception. The enhanced HVP- based IQA requires far less computation (>189 times lesser) while still showing excellent correlation (Pearson Correlation Coefficient, PCC > 0.90, Root Mean Square Error, RMSE<0.3410) with human judgment. Besides, it requires fewer (two) threshold parameter tuning while maintaining consistent performance across wide range of threshold parameters’ value, making it feasible for real-time video processing.

References

[1] Arici T., Dikbas S., and Altunbasak Y., “A Histogram Modification Framework and its Application for Image Contrast Enhancement,” (8) 231 4 5() 11()21b x bR x b b x be     46 The International Arab Journal of Information Technology, Vol. 16, No. 1, January 2019 IEEE Transactions on Image Processing, vol. 18, no. 9, pp. 1921-1935, 2009.

[2] Chen S., “Human Visual Perception-based Image Quality Analyzer for Assessment of Contrast Enhancement Methods,” The International Arab Journal of Information Technology, vol. 13, no. 2, pp. 238-245, 2016.

[3] Chen S. and Ramli A., “Contrast Enhancement using Recursive Mean-Separate Histogram Equalization for Scalable Brightness Preservation,” IEEE Transactions on Consumer Electronics, vol. 49, no. 4, pp. 1301-1309, 2003.

[4] Chen S. and Ramli A., “Minimum Mean Brightness Error Bi-Histogram Equalization in Contrast Enhancement,” IEEE Transactions on Consumer Electronics, vol. 49, no. 4, pp. 1310- 1319, 2003.

[5] Chen S. and Sidhu M., “Re-Evaluation of Automatic Global Histogram Equalization-based Contrast Enhancement Methods,” Electronic Journal of Computer Science and Information Technology, vol. 1, no. 1, pp. 13-17, 2009.

[6] Chen S. and Suleiman A., “Scalalable Global Histogram Equalization with Selective Enhancement for Photo Processing,” in Proceedings of the 4th International Conference on Information Technology and Multimedia, Malaysia, pp. 744-752, 2008.

[7] Hyvärinen A., Hurri J., and Hoyer P., Natural Image Statistic-A probability Approach to Early Computation Vision, Springer, 2009.

[8] Ibrahim H. and Kong N., “Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement,” IEEE Transactions on Consumer Electronics, vol. 53, no. 4, pp. 1752- 1758, 2007.

[9] Ibrahim H. and Kong N., “Image Sharpening using Sub-Regions Histogram Equalization,” IEEE Transactions on Consumer Electronics, vol. 55, no. 2, pp. 891-895, 2009.

[10] Kim M. and Chung M., “Recursively Separated and Weighted Histogram Equalization for Brightness Preservation and Contrast Enhancement,” IEEE Transactions on Consumer Electronics, vol. 54, no. 3, pp. 1389-1397, 2008.

[11] Liu L., Hua Y., Zhao Q., Huang H., and Bovik A., “Blind Image Quality Assessment by Relative Gradient Statistics and Adaboosting Neural Network,” Signal Processing: Image Communication, vol. 40, pp. 1-15, 2016.

[12] Menotti D., Najman L., Facon J., and Araujo A., “Multi-Histogram Equalization Methods for Contrast Enhancement and Brightness Preserving,” IEEE Transactions on Consumer Electronics, vol. 53, no. 3, pp. 1186-1194, 2007.

[13] Mittal A., Moorthy A., and Bovik A., “No- Reference Image Quality Assessment in the Spatial Domain,” IEEE Transactions on Image Processing, vol. 21, no. 12, pp. 4695-4708, 2012.

[14] Narwaria M., Lin W., and Liu A., “Low- Complexity Video Quality Assessment using Temporal Quality Variations,” IEEE Transactions on Multimedia, vol. 14, no. 3, pp. 525-535, 2012.

[15] Ooi C., Kong N., and Ibrahim H., “Bi-Histogram with A Plateau Limit for Digital Image Enhancement,” IEEE Transactions on Consumer Electronics, vol. 55, no. 4, pp. 2072-2080, 2009.

[16] Pichai S., Krishnasamy B., and Karuppanagounder S., “Bi-Level Weighted Histogram Equalization for Scalable Brightness Preservation and Contrast Enhancement for Images,” The International Arab Journal of Information Technology, vol. 10, no. 6, pp. 603- 609, 2013.

[17] Reichl P., Egger S., Schatz R., and D'Alconzo A., “The Logarithmic Nature of QoE and the Role of the Weber-Fechner Law in QoE Assessment,” in Proceedings of IEEE International Conference on Communications, Cape Town, pp. 1-5, 2010.

[18] Sengee N., Sengee A., and Choi H., “Image Contrast Enhancement using Bi-Histogram Equalization with Neighbourhood Metrics,” IEEE Transactions on Consumer Electronics, vol. 56, no. 4, pp. 2727-2734, 2010.

[19] Sheet D., Garud H., Suveer A., Mahadevappa M., and Chatterjee J., “Brightness Preserving Dynamic Fuzzy Histogram Equalization,” IEEE Transactions on Consumer Electronics, vol. 56, no. 4, pp. 2475-2480, 2010.

[20] Sheikh H., Sabir M., and Bovik A., “A statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms,” IEEE Transactions on Image Processing, vol. 15, no. 11, pp. 3440-3451, 2006.

[21] VQEG “VQEG Final Report of FR-TV Phase II Validation Test,” 2003.

[22] Wang C. and Ye Z., “Brightness Preserving Histogram Equalization with Maximum Entropy: A Variational Perspective,” IEEE Transactions on Consumer Electronics, vol. 51, no. 4, pp. 1326-1334, 2005.

[23] Wang Q. and Ward R., “Fast Image/Video Contrast Enhancement Based on Weighted Thresholded Histogram Equalization,” IEEE Transactions on Consumer Electronics, vol. 53, no. 2, pp. 757-764, 2007.

[24] Yun S., Kim J., and Kim S., “Image Enhancement using a Fusion Framework of Histogram Equalization and Laplacian Pyramid,” IEEE Transactions on Consumer Electronics, vol. 56, no. 4, pp. 2763-2771, 2010. Enhancement of Human Visual Perception-Based Image Quality Analyzer... 47 Soong Chen is an Associate Professor in the College of Information Technology, Universiti Tenaga Nasional, Putrajaya, Malaysia. He received his BE (1997), M.Sc (2000) and Ph.D (2008) from the Universiti Putra Malaysia, Serdang, Malaysia. His research interest includes image quality assessment, image enhancement, computer vision and image compression. Tiagrajah Janahiraman is a Senior Lecturer in the College of Engineering, Universiti Tenaga Nasional, Putrajaya, Malaysia. He received his B.E (2000) and M.Eng (2002) from Universiti Teknologi Malaysia and Phd in Electrical Engineering from Universiti Tenaga Nasional, Malaysia, in 2012. His research interest includes image processing, face recognition, computer vision and video motion analysis. Azizah Suliman is an Associate Professor at College of Information Technology, Universiti Tenaga Nasional, Malaysia. She received her BCS from Southern Illinois University, USA in 1985, MCS from Universiti Malaya in 1990 and PhD in Computer Science (AI) from Universiti Putra Malaysia in 2011. Her interests includes Image Processing, Soft Computing and Embedded Systems.