A no-reference perceptual blur metric

We present a no-reference blur metric for images and video. The blur metric is based on the analysis of the spread of the edges in an image. Its perceptual significance is validated through subjective experiments. The novel metric is near real-time, has low computational complexity and is shown to perform well over a range of image content. Potential applications include optimization of source coding, network resource management and autofocus of an image capturing device.

[1]  A. Carasso.  Linear and Nonlinear Image Deblurring: A Documented Study , 1999 .

[2]  Deepa Kundur,et al.  Blind image deconvolution , 1996, IEEE Signal Process. Mag..

[3]  Stefan Winkler,et al.  Vision models and quality metrics for image processing applications , 2001 .

[4]  Steven W. Zucker,et al.  Local Scale Control for Edge Detection and Blur Estimation , 1996, ECCV.

[5]  Zhou Wang,et al.  Blind measurement of blocking artifacts in images , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[6]  Michael W. Marcellin,et al.  JPEG2000 - image compression fundamentals, standards and practice , 2002, The Kluwer International Series in Engineering and Computer Science.