Adaptive weighted non-parametric background model for efficient video coding

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• 作者：Subrata Chakraborty^a ; ^{subrata.chakraborty@usq.edu.au}Author Vitae ; Manoranjan Paul^b ; ^{mpaul@csu.edu.au}Author Vitae ; Manzur Murshed^c ; ^{manzur.murshed@federation.edu.au}Author Vitae ; Mortuza Ali^c ; ^{mortuza.ali@federation.edu.au}Author Vitae
• 关键词：Background model ; Coding efficiency ; Coding performance ; HEVC ; Non-parametric model ; Video Coding
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：22 February 2017
• 年：2017
• 卷：226
• 期：Complete
• 页码：35-45
• 全文大小：2084 K
• 卷排序：226

文摘

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.