Abstract
The degradation of the image at the acquisition and transmission severely affects the various stages of the image processing and leads to the unexpected results. So, it is necessary to be aware of the source and reason of the various noises that are incorporated with the original image. The image quality measures compute the quality of the corrupted or degraded image with or without reference (input) image. The comparison of the degraded and reference image produces numerical score that decides the quality of the image. The proposed work tests the images incorporated the various noises such as salt and pepper, Poisson, Gaussian and speckle. Blind (no reference) and full reference quality measures investigate the amount of degradation in images. Full reference quality measures are very simple to compute but do not correlate with human perception, whereas blind reference measures prove its supremacy in this aspect.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
P. Ganesan, V. Rajini, Assessment of satellite image segmentation in RGB and HSV color space using image quality measures, in 2014 International Conference on Advances in Electrical Engineering (ICAEE), (2014), pp. 1–5
H.R. Sheikh, M.F. Sabir, A.C. Bovik, A statistical evaluation of recent full recent full reference image quality assessment algorithms. IEEE Trans. Image Process. 15(11), 3441–3456 (2006)
P. Ganesan, K.B. Shaik, HSV color space based segmentation of region of interest in satellite images, in 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), (2014), pp. 101–105
V. Kalist, P. Ganesan, B.S. Sathish, J.M.M. Jenitha, Possiblistic-fuzzy C-means clustering approach for the segmentation of satellite images in HSL color space. Procedia Comput. Sci. 57, 49–56 (2015)
I. Avcıbas, B. Sankur, K. Sayood, Statistical evaluation of image quality measures. J. Electron. Imaging 11(2), 206–223 (2002)
Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4) (2004)
A.K. Moorthy, A.C. Bovik, Blind image quality assessment: from scene statistics to perceptual quality. IEEE Trans. Image Process. 20(12), 3350–3364 (2011)
A.K. Mittal, R. Soundararajan, A.C. Bovik, Making a completely blind image quality analyzer. IEEE Signal Process. Lett. 22(3), 209–212 (2013)
A. Mittal, A.K. Moorthy, A.C. Bovik, No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ganesan, P., Sathish, B.S., Vasanth, K., Vadivel, M., Sivakumar, V.G., Thulasiprasad, S. (2020). Color Image Quality Assessment Based on Full Reference and Blind Image Quality Measures. In: Saini, H.S., Singh, R.K., Tariq Beg, M., Sahambi, J.S. (eds) Innovations in Electronics and Communication Engineering. Lecture Notes in Networks and Systems, vol 107. Springer, Singapore. https://doi.org/10.1007/978-981-15-3172-9_43
Download citation
DOI: https://doi.org/10.1007/978-981-15-3172-9_43
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3171-2
Online ISBN: 978-981-15-3172-9
eBook Packages: EngineeringEngineering (R0)