Adaptive weighted non-parametric background model for efficient video coding

Chakraborty, Subrata and Paul, Manoranjan and Murshed, Manzur and Ali, Mortuza (2017) Adaptive weighted non-parametric background model for efficient video coding. Neurocomputing, 226. pp. 35-45. ISSN 0925-2312

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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published version cannot be displayed due to copyright restrictions.
Faculty / Department / School: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise
Date Deposited: 14 Feb 2017 05:07
Last Modified: 14 Feb 2017 05:15
Uncontrolled Keywords: background model; coding efficiency; coding performance; HEVC; non-parametric model; video coding
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Funding Details:
Identification Number or DOI: 10.1016/j.neucom.2016.11.016
URI: http://eprints.usq.edu.au/id/eprint/30038

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