A filtering method for color videos contaminated by additive noise is presented. The proposed framework employs three filtering stages: spatial similarity filtering, neighboring frame denoising, and spatial post-processing smoothing. The difference with other state-of- the-art filtering methods, is that this approach, based on fuzzy logic, analyses basic and related gradient values between neighboring pixels into a 7 fi 7 sliding window in the vicinity of a central pixel in each of the RGB channels. Following, the similarity measures between the analogous pixels in the color bands are taken into account during the denoising. Next, two neighboring video frames are analyzed together estimating local motions between the frames using block matching procedure. In the final stage, the edges and smoothed areas are processed differently in a current frame during the post-processing filtering. Numerous simulations results confirm that this 3D fuzzy filter perform better than other state-of-the- art methods, such as: 3D-LLMMSE, WMVCE, RFMDAF, FDARTF G, VBM3D and NLM, in terms of objective criteria (PSNR, MAE, NCD and SSIM) as well as subjective perception via human vision system in the different color videos. An efficiency analysis of the designed and other mentioned filters have been performed on the DSPs TMS320 DM642 and TMS320DM648 by Texas Instruments through MATLAB and Simulink module showing that the novel 3D fuzzy filter can be used in real-time processing applications.