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Improving Performance of IQA Algorithm: A Simplified Approach to SSIM based on Image Gradients


  • UKF College of Engineering and Technology, Parippally Kollam, Kerala – 691302, India
  • Departmentof Electronics and Communication Engineering. Karpagam Academy of Higher Education Coimbatore - 641 021,Tamil Nadu, India


Objectives: To propose a fast and efficient algorithm for Full Reference Image Quality Assessment (FR-IQA) that can evaluate the quality of a distorted image accurately. Methods/Statistical Analysis: The proposed approach uses SSIM (Structural Similarity Index ) algorithm based on image gradients. SSIM algorithm has been widely accepted as an effective tool for estimating image quality. The quality estimation capability of SSIM has been enhanced by using image gradients. In this paper, the SSIM based on image gradients has been simplified and optimized to reduce the computational complexity, thereby to improve the execution speed and to improve the quality estimation capability. Findings: The proposed algorithm is computationally simple compared to SSIM and Gradient based SSIM. There has been significant improvement in the execution speed and quality prediction capability of the proposed algorithm compared to SSIM and Gradient based SSIM for various types of distortions. This simplified version of SSIM based on gradients has been extensively tested on popular image databases. The results confirm its effectiveness, efficiency and consistency in estimating the image quality. Applications/Improvements: The proposed algorithm is suitable in IQA applications where improved prediction accuracy and execution speed are important.


Full Reference Image Quality (FR-IQA), Gradient based SSIM, Image Quality Assessment(IQA),(FR-IQA), Structural Similarity Index (SSIM)

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  • Chandler DM. Seven challenges in image quality assessment: past, present, and future research. ISRN Signal Processing.2013(2013);1–53.
  • Liu TJ, Chieh Y, Lin W, Kuo CCJ. Visual Quality Assessment: recent developments, coding, applications and future trends. SPSIPA Transactions on Signal and Information Processing, 2013.
  • Wang Z, Bovik AC. Reduced-and no-reference image quality assessment. IEEE Signal Processing Magazine. 2011; 28(6):29–40.
  • Joy KR, Sarma EG. Recent developments in Image quality assessment algorithms: A review. Journal of Theoretical and Applied Information Technology. 2014 Jul; 65(1):192–201.
  • Gonzalez RC, Woods RE. Digital Image Processing, 3rd (edn)., Pearson Education Inc: New York, 2008.
  • Wang Z, Bovik AC. Mean squared error: love it or leave it?A new look at signal fidelity measures. IEEE Signal Processing Magazine. 2009; 26(1):98–117.
  • Wang Z , Bovik AC, Sheik HR, Simocelli EP. Image quality assessment: From Error Visibility to Structural Similarity, IEEE Transaction on Image Processing. 2004 Apr; 13(4):1– 14.
  • Wang Z, Simoncelli EP, Bovik AC. Multiscale structural similarity for image quality assessment, Conference Record of the 37 thAsilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, IEEE, 2004, 2. p. 1398–402.
  • Chen GH, Yang CL, Xie SL. Gradient-based structural similarity for image quality assessment, IEEE International Conference on Image Processing, 2006. p. 2929–32.
  • Chen GH, Yang CL, Po LM, Xie SL. Edge-based structural similarity for image quality assessment, IEEE International Conference on Acoustics, Speech and Signal Processing, 2006 Proceedings, 2006, 2, p. II-II.
  • Rouse DM, Hemami SS. Understanding and simplifying the structural similarity metric, 15th IEEE International Conference on Image Processing, 2008. p. 1188–91.
  • Chen MJ, Bovik AC. Fast structural similarity index algorithm.Journal of Real-Time Image Processing, 6(4), 2011.p. 281–87.
  • Sheikh HR, Wang Z, Cormack L, Bovik AC. LIVE image quality assessment database release2”, 2005,
  • Ponomarenko N, Lukin V, Zelensky A, Egiazarian K, Carli M, Battisti F. TID2008 - A Database for Evaluation of Full-Reference Visual Quality Assessment Metrics, Advances of Modern Radioelectronics, 2009, 10. p. 30–45.
  • Ponomarenko N, Jin L, Ieremeiev O, Lukin V, Egiazarian K, Astola J, Vozel B, Chehdi K, Carli M, KuoB F. Image database TID2013: Peculiarities, results and perspectives, Signal Processing: Image Communication, 2015 Jan, p.57–77.
  • Larson EC, Chandler DM. Most apparent distortion: full-reference image quality assessment and the role of strategy, Journal of Electronic Imaging, 2010 Mar; 19 (1):1– 21.
  • Final Report From the Video Quality Experts Group on the validation of Objective Models of Video Quality Assessment. Date Accessed: 2003.
  • Sheikh HR, Sabir MF, Bovik AC. A statistical evaluation of recent full reference image quality assessment algorithms.IEEE Transaction on IP, 2006; 15(11):3440–51.
  • Zhang L, Zhang L. Research on Image Quality Assessment, Available online at Date Accessed: 30 /08 /2014.
  • Zhang L, Mou X, Zhang D. A comprehensive evaluation of full reference image quality assessment algorithms, 19th IEEE International Conference on Image Processing, 2012 Sep. p. 1477–80.


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