Dynamic background frame based video coding using mixture of Gaussian (MoG) based background modelling has achieved better rate distortion performance compared to the H264 standard. However, they suffer from high computation time, low coding efficiency for dynamic videos, and prior knowledge requirement of video content. In this paper, we introduce the application of the non-parametric (NP) background modelling approach for video coding domain. We present a novel background modelling technique, called weighted non-parametric (WNP) which balances the historical trend and the recent value of the pixel intensities adaptively based on the content and characteristics of any particular video. WNP is successfully embedded into the latest HEVC video coding standard for better rate-distortion performance. Moreover, a novel scene adaptive non-parametric (SANP) technique is also developed to handle video sequences with high dynamicbackground. Being non-parametric, the proposed techniques naturally exhibitsuperior performance in dynamic background modelling without a prioriknowledge of video data distribution.
Chakraborty, S., Paul, M., Murshed, M., & Ali, M. (2017). Adaptive weighted non-parametric background model for efficient video coding. Neurocomputing, 226, 34-45. https://doi.org/10.1016/j.neucom.2016.11.016