In this paper we propose a method that simultaneously performs image denoising and compression by using multiscale tensor voting. Given a real color image, the pixels are first converted into a set of tokens to be grouped by tensor voting, where optimal scales are automatically selected among others for perceptual grouping and faithful reconstruction. Tensor voting at multiple scales are performed at all input tokens to infer the feature grouping attributes such as region-ness, curve-ness, and junction-ness with their optimal scales. We perform experiments on complex real images to demonstrate the robustness of our method.