Recursive bilateral filter for encoder-integrated video denoising

Video denoising based on temporal or spatiotemporal filtering is highly effective but computationally expensive due to the requirement of motion estimation. Encoder-integrated denoising is an efficient framework that embeds the filtering process into the encoding pipeline so that motion estimation for denoising can be avoided. State-of-the-arts encoder-integrated methods use Least Minimum Mean Square Error (LMMSE) optimal filters to reduce noise based on additive noise models. However, in practice, the LMMSE filters could result in blurred edges and visual artifacts due to inaccurate image signal estimation caused by outlier input samples and non-adaptivity within macroblocks. This paper presents new recursive bilateral filters that can be easily integrated into video encoders to tackle the limitations of the LMMSE filters. The robustness of the bilateral filters results in substantially better objective and subjective quality with marginal computation cost increase.