Adaptive weighted non-parametric background model for efficient video coding

NEUROCOMPUTING

卷:226

Article

Chakraborty, Subrata¹  Paul, Manoranjan²  Murshed, Manzur³  Ali, Mortuza³ 

[1] Univ Southern Queensland, Sch Management & Enterprise, Sinnathamby Blvd,POB 4196, Springfield Cent, Qld 4300, Australia

[2] Charles Sturt Univ, Sch Comp & Math, Panorama Ave, Bathurst, NSW 2795, Australia

[3] Federat Univ, Sch Informat Technol, Northways Rd,POB 3191, Gippsland Mail Ctr, Vic 3841, Australia

关键词: Background model;    Coding efficiency;    Coding performance;    HEVC;    Non-parametric model;    Video Coding;   

DOI  :  10.1016/j.neucom.2016.11.016

来源: Elsevier

【 摘 要 】

Dynamic background frame based video coding using mixture of Gaussian (MoG) based background modelling has achieved better rate distortion performance compared to the H.264 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 dynamic background. Being non-parametric, the proposed techniques naturally exhibit superior performance in dynamic background modelling without a priori knowledge of video data distribution.

【 授权许可】

Free