Abstract
No-Reference (NR) quality metric for monitoring video image is a challenging and meaningful research. In this paper, we propose a novel objective NR video blurriness metric for monitoring surveillance tapes based on human visual system (HVS) characteristics and the local structure similarity of gradient images. Firstly, according to the low-pass filter (LPF) characteristics of optical imaging system, we construct a reference frame image by passing an original (to be tested) frame through a LPF; secondly, weight the gradient images of reference frame and original frame respectively with contrast sensitivity function (CSF) of HVS, followed by extracting gradient information-rich blocks in the reference gradient image and then computing the local gradient structure similarity between the corresponding blocks of the original frame image and the reference one to assess the blurriness of single frame of original sequence; finally the blur quality of overall original video sequence is evaluated by employing a weighting method which calculates the quality of each frame with different weights. Experimental results show that the proposed metric model has a good correlation with subjective scores and achieves valid blurriness evaluation effects.
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Chen, S., Jiao, H. (2011). No-Reference Video Monitoring Image Blur Metric Based on Local Gradient Structure Similarity. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7003. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23887-1_41
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DOI: https://doi.org/10.1007/978-3-642-23887-1_41
Publisher Name: Springer, Berlin, Heidelberg
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