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.