Abstract
Image quality assessment traditionally means the comparison of original image with its distorted version using conventional methods like Mean Square Error (MSE) or Peak Signal to Noise Ratio (PSNR). In case of Blind Quality Evaluation with no prior knowledge about the image, a single parameter becomes insufficient to define the overall image quality. This paper proposes a quality metric based on sharpness of the image, presence of noise, overall contrast and luminance of the image and the detection of the eyes. Experimental results reveal that the proposed metric has strong relevance with human quality perception.
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Bhattacharjee, D., Prakash, S., Gupta, P. (2012). No-Reference Image Quality Assessment for Facial Images. In: Huang, DS., Gan, Y., Gupta, P., Gromiha, M.M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2011. Lecture Notes in Computer Science(), vol 6839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25944-9_77
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DOI: https://doi.org/10.1007/978-3-642-25944-9_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25943-2
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