Title :
A no-reference sharpness metric sensitive to blur and noise
Author :
Zhu, Xiang ; Milanfar, Peyman
Author_Institution :
Electr. Eng. Dept., Univ. of California at Santa Cruz, Santa Cruz, CA, USA
Abstract :
A no-reference objective sharpness metric detecting both blur and noise is proposed in this paper. This metric is based on the local gradients of the image and does not require any edge detection. Its value drops either when the test image becomes blurred or corrupted by random noise. It can be thought of as an indicator of the signal to noise ratio of the image. Experiments using synthetic, natural, and compressed images are presented to demonstrate the effectiveness and robustness of this metric. Its statistical properties are also provided.
Keywords :
data compression; gradient methods; image coding; random noise; blur detection; image compression; local gradient method; no-reference objective sharpness metric; noise detection; statistical property; Covariance matrix; Image analysis; Image edge detection; Image processing; Image quality; Matrix decomposition; Pixel; Signal to noise ratio; Singular value decomposition; Testing; Sharpness metric; blur; covariance; gradient; noise; singular value;
Conference_Titel :
Quality of Multimedia Experience, 2009. QoMEx 2009. International Workshop on
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4244-4370-3
Electronic_ISBN :
978-1-4244-4370-3
DOI :
10.1109/QOMEX.2009.5246976
