Film grain noise removal and synthesis in video coding

In this paper, we propose new techniques for film grain noise removal and synthesis, which can be applied in video coding. Film grain noise is clearly noticeable in high-definition video, and should be preserved for the sake of natural look. However, film grain noise tends to reduce the coding efficiency because of its random nature. In our work, prior to video encoding, essential parameters of film grain noise are estimated and the noise is removed by temporal filtering; at the decoder size, film grain noise is modeled by an autoregressive (AR) model, synthesized using the estimated parameters, and added back to the decoded video. Simulation results show that the proposed algorithms can considerably reduce the bitrate and at the same time achieve good subjective quality.