A Real-Time Technique for Spatio–Temporal Video Noise Estimation

This paper proposes a spatio-temporal technique for estimating the noise variance in noisy video signals, where the noise is assumed to be additive white Gaussian noise. The proposed technique utilizes domain-wise (spatial, temporal, and spatio-temporal) video information independently for improved reliability. It divides the video signal into cubes and measures their homogeneity using Laplacian of Gaussian based operators. Then, the variances of homogeneous cubes are selected to estimate the noise variance. A least median of squares robust estimator is used to reject outliers and produce domain-wise noise variance estimates which are adaptively integrated to obtain the final frame-wise estimate. The proposed technique estimates the noise variance reliably in video sequences with both low and high video activities (e.g., fast motion or high spatial structure) and it produces a maximum estimation error of 1.7-dB peak signal-to-noise ratio. The proposed method is fast when compared to referenced methods.

[1]  Eric Dubois,et al.  Fast and reliable structure-oriented video noise estimation , 2005, IEEE Transactions on Circuits and Systems for Video Technology.

[2]  Byung Cheal Song,et al.  NOISE ESTIMATION FOR EFFICIENT PRE-FILTERING IN A VIDEO ENCODER , 2003 .

[3]  Seungjoon Yang,et al.  Block-based noise estimation using adaptive Gaussian filtering , 2005, 2005 Digest of Technical Papers. International Conference on Consumer Electronics, 2005. ICCE..

[4]  Søren I. Olsen,et al.  Estimation of Noise in Images: An Evaluation , 1993, CVGIP Graph. Model. Image Process..

[5]  Jean-Luc Starck,et al.  Image Processing and Data Analysis: Multiresolution support and filtering , 1998 .

[6]  T. G. Kwaaitaal-Spassova,et al.  TELEVISION NOISE REDUCTION IC , 1996, 1996. Digest of Technical Papers., International Conference on Consumer Electronics.

[7]  G. Shevlyakov,et al.  Robustness in Data Analysis: Criteria and Methods , 2001 .

[8]  Charles V. Stewart,et al.  Robust Parameter Estimation in Computer Vision , 1999, SIAM Rev..

[9]  Fionn Murtagh,et al.  Image Processing and Data Analysis - The Multiscale Approach , 1998 .

[10]  Yuan F. Zheng,et al.  Combined spatial and temporal domain wavelet shrinkage algorithm for video denoising , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[11]  R. Unbehauen,et al.  Estimation of image noise variance , 1999 .

[12]  Aleksandra Pizurica,et al.  Noise estimation for video processing based on spatio-temporal gradients , 2006, IEEE Signal Processing Letters.

[13]  Christine Connolly,et al.  Handbook of Image and Video Processing 2nd Edition (Hardback) , 2006 .

[14]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[15]  Michael Spann,et al.  A quad-tree approach to image segmentation which combines statistical and spatial information , 1985, Pattern Recognit..

[16]  Roberto Rinaldo,et al.  Model-based global and local motion estimation for videoconference sequences , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[17]  G. de Haan,et al.  Memory integrated noise reduction IC for television , 1996 .

[18]  Mohammed Ghazal,et al.  Structure-Oriented Spatio-Temporal Video Noise Estimation , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[19]  W. B. Collis,et al.  Training Methods for Image Noise Level Estimation on Wavelet Components , 2004, EURASIP J. Adv. Signal Process..

[20]  Byung Cheol Song,et al.  Noise power estimation for effective de-noising in a video encoder , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..