Color Video Denoising Based on Combined Interframe and Intercolor Prediction

Abstract

An advanced color video denoising scheme which we call CIFIC based on combined interframe and intercolor prediction is proposed in this paper. CIFIC performs the denoising filtering in the RGB color space, and exploits both the interframe and intercolor correlation in color video signal directly by forming multiple predictors for each color component using all three color components in the current frame as well as the motion-compensated neighboring reference frames. The temporal correspondence is established through the joint-RGB motion estimation (ME) which acquires a single motion trajectory for the red, green, and blue components. Then the current noisy observation as well as the interframe and intercolor predictors are combined by a linear minimum mean squared error (LMMSE) filter to obtain the denoised estimate for every color component. The ill condition in the weight determination of the LMMSE filter is detected and remedied by gradually removing the “least contributing” predictor. Furthermore, our previous work on the LMMSE filter applied in the adaptive luminance-chrominance space (LAYUV for short) is revisited. By reformulating LAYUV and comparing it with CIFIC, we deduce that LAYUV is a restricted version of CIFIC, and thus CIFIC can theoretically achieve lower denoising error. Experimental results verify the improvement brought by the joint-RGB ME and the integration of the intercolor prediction, as well as the superiority of CIFIC over LAYUV. Meanwhile, when compared with other state-of-the-art algorithms, CIFIC provides competitive performance both in terms of the color peak signal-to-noise ratio and in perceptual quality.