Adaptive weighted non-parametric background model for efficient video coding

Article


Chakraborty, Subrata, Paul, Manoranjan, Murshed, Manzur and Ali, Mortuza. 2017. "Adaptive weighted non-parametric background model for efficient video coding." Neurocomputing. 226, pp. 35-45. https://doi.org/10.1016/j.neucom.2016.11.016
Article Title

Adaptive weighted non-parametric background model for efficient video coding

ERA Journal ID18092
Article CategoryArticle
AuthorsChakraborty, Subrata (Author), Paul, Manoranjan (Author), Murshed, Manzur (Author) and Ali, Mortuza (Author)
Journal TitleNeurocomputing
Journal Citation226, pp. 35-45
Number of Pages11
Year2017
PublisherElsevier
Place of PublicationNetherlands
ISSN0925-2312
1872-8286
Digital Object Identifier (DOI)https://doi.org/10.1016/j.neucom.2016.11.016
Web Address (URL)http://ac.els-cdn.com/S0925231216313467/1-s2.0-S0925231216313467-main.pdf?_tid=1d9dd09a-f272-11e6-82fe-00000aacb35f&acdnat=1487048441_3bb72a9b10b935e113821a8e4e3e868a
Abstract

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.

Keywordsbackground model; coding efficiency; coding performance; HEVC; non-parametric model; video coding
ANZSRC Field of Research 2020460306. Image processing
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Byline AffiliationsSchool of Management and Enterprise
Charles Sturt University
Federation University
Institution of OriginUniversity of Southern Queensland
Funding source
Australian Research Council (ARC)
Grant ID
DP130103670
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