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A Comparative Analysis on Image Quality Assessment for Real Time Satellite Images


  • School of Computing Science and Engineering, VIT University, Chennai - 600127, Tamil Nadu, India


Objectives: The objective of this paper is to analyze the different image quality metrics by testing and comparing with different distorted set of satellite images. Methods/Statistical Analysis: In this paper, we propose the methods for analyzing the quality of real time images that are corrupted due to different distortions. The several quality metrics are applied and ultimately the best metrics are derived based on the type of degradation. Different metrics such as metric based on single image and metric based on two images have been tested with different real time satellite images from NASA data sets. Findings: This framework will help to identify the metrics in order to prove the proposed filtering schemes that are applied to the corrupted images. Based on the results, we have concluded the characteristics of different quality metrics and further we successfully identified the quality metric appropriate to various distortions. Application/Improvements: The proposed quality metric analysis is used to estimate the performance of any filtering schemes which are used to enhance the quality of any real time images such as remote sensing field.


Assessment, Blur, Gaussian, Metrics, Quality, Satellite Images, Salt and Pepper.

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  • Zhang Y, Chandler DM. 3D-MAD: A Full Reference Stereoscopic Image Quality Estimator Based on Binocular Lightness and Contrast Perception. IEEE Transactions on Image Processing. 2015; 24(11):3810–25.
  • Lee J, Park R-H. Image Quality Assessment of Tone mapped Image. International Journal on Computer Graphics and Animation (IJCGA). 2015; 5(2):9–20.
  • Liu X, Zhang L, Li H et al. Integrating visual saliency information into objective quality assessment of tone-mapped images. 10th Proceedings International Conference on Intelligent Computing, Taiyuan, China. 2014. p. 376–86.
  • Li J, Wang C, Li M, 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. 2015; 8(6):283–88.
  • Jagalingam P, Hegde AV. A Review of Quality Metrics for Fused Image. International Conference on Water Resources, Coastal and Ocean Engineering (ICWRCOE-2015). 2015. p. 133–42.
  • Ding Y, Zhang Y et al. Perceptual Image Quality assessment metric using mutual information of Gabor features. Springer Journal. 2014; 57(3):1–9.
  • Galbally J, Marcel S, Fierraz J. Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition. IEEE Transactions on Image Processing. 2014; 23(2):710–24.
  • Xue W, Mou X, Zhang L et al. Blind Image Quality Assessment using Joint Statistics of Gradient Magnitude and Laplacian Features. IEEE Transactions on Image Processing. 2014; 23(11):4850–62.
  • Chandler DM. Seven Challenges in Image Quality Assessment: Past, Present, and Future Research. ISRN Signal Processing. 2013; 2013:1–54.
  • Thung KH, Raveendran P. A Survey of Image quality measures, IEEE International Conference for Technical Post Graduates (TECHPOS), Kuala Lumpur. 2009. p. 1–4.
  • Damera-Venkata N, Kite TD, Geisler WS, Evans BL, Bovik AC. Image Quality Assessment based on a Degradation Model. IEEE Transactions on Image Processing. 2000; 9(4):639–50.
  • Wang Z, Bovik AC. A Universal Image Quality Index. IEEE Signal Processing Letters. 2002; 9(3):81–4.
  • Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions of Image Processing. 2004; 13(4):600–12.
  • Silva EA, Panetta K, Agaian SS. Quantify similarity with measurement of enhancement by entropy, Proceedings: Mobile Multimedia/Image Processing for Security Applications, SPIE Security Symposium, 2007.
  • David S. Data Compression: The Complete Reference, (4 ed) Springer-Verlag: London, 2007.
  • Huynh-Thu Q, Ghanbari M. Scope of validity of PSNR in image/video qualityassessment. Electronics Letters. 2008; 44(13):800–1.
  • Naveen Kumar N, Ramakrishna S. An Impressive Method to Get Better Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE) Values Using Stationary Wavelet Transform (SWT). Global Journal of Computer Science and Technology Graphics andVision. 2012; 12(12):35–40.
  • Wang Z, Sheikh HR, Bovet AC. Objective video quality assessment, in the Handbook of Video Databases: Design and Applications, B. Furht and O. Marques, Eds. CRC Press: Boca Raton, FL. 2003; 1041–78.
  • Peli E. Contrast in complex images. J Opt Soc Amer A. 1990; 7(10):2032–40.
  • Kumar R, Rattan M. Analysis of Various Quality Metrics for Medical Image Processing. International Journal of Advanced Research in Computer Sciences and Software Engineering. 2012; 2(11):137–44.
  • Rajkumar S, Malathi G. A Novel approach for the recovery of de-focused color Images. International Journal on Recent Researches in Science, Engineering and Technology (IJRRSET). 2015; 3(5).
  • Rajkumar S. A Novel Approach for the Recovery of ill conditioned color images. Proceedings 6th National Conference on Convergence of Computer and Information Engineering, (NCCCIE’06), PSG College of Technology, Coimbatore, India. 2006; 12 pp.
  • Rajkumar S. An Advanced Technique for the Analysis of Image Quality Assessment for Distorted Images in the Field of Image Processing Application, Karunya University, Coimbatore, India. 2008; 84–8.
  • Janani P, Premaladha J, Ravichandran KS. Image Enhancement Techniques: A Study. Indian Journal of Science and Technology. 2015; 8(22):1–12.


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