1 October 2011 Blurriness measurement in frequency domain for image quality assessment
Author Affiliations +
Proceedings Volume 8285, International Conference on Graphic and Image Processing (ICGIP 2011); 82853X (2011); doi: 10.1117/12.914629
Event: 2011 International Conference on Graphic and Image Processing, 2011, Cairo, Egypt
DCT based digital image and video compression leads to visible distortions like blockiness and blurriness, however this paper mainly focuses blurriness artifact. Subjective quality assessments are reliable but they are very costly and can't be computerized. This paper proposes three different objective quality assessment methods for blurriness estimation using full reference, reduced reference and no reference approaches. The distortion is measured in frequency domain by comparing the high frequency coefficients of the coded image. Before blurriness estimation in frequency domain, the property of Human Visual System is implemented by applying the spatial masking in spatial domain. Since the distortion is not likely to be in same amount in every part of the coded image therefore the coded image is divided into blocks and the distortion is calculated locally for each block and accumulated in the end for a single quality metric. The results show that the full and reduced reference meters are more reliable due to the availability of some reference information at receiver end. The work is tested on different set of blurred images from LIVE image database and the Pearson's correlation coefficient of 94.43% is obtained for full reference mode while it is 94.20% and 82.03% for reduced reference and no reference respectively.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qadri Muhammad Tahir, Mehmood Syed Noman, Aisha Tahir, "Blurriness measurement in frequency domain for image quality assessment", Proc. SPIE 8285, International Conference on Graphic and Image Processing (ICGIP 2011), 82853X (1 October 2011); doi: 10.1117/12.914629; https://doi.org/10.1117/12.914629

Image quality

Image processing

Edge detection



Image compression

Visual system


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