Adaptive weighted non-parametric background model for efficient video coding
Article
Article Title | Adaptive weighted non-parametric background model for efficient video coding |
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ERA Journal ID | 18092 |
Article Category | Article |
Authors | Chakraborty, Subrata (Author), Paul, Manoranjan (Author), Murshed, Manzur (Author) and Ali, Mortuza (Author) |
Journal Title | Neurocomputing |
Journal Citation | 226, pp. 35-45 |
Number of Pages | 11 |
Year | 2017 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0925-2312 |
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. |
Keywords | background model; coding efficiency; coding performance; HEVC; non-parametric model; video coding |
ANZSRC Field of Research 2020 | 460306. Image processing |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Management and Enterprise |
Charles Sturt University | |
Federation University | |
Institution of Origin | University of Southern Queensland |
Funding source | Australian Research Council (ARC) Grant ID DP130103670 |
https://research.usq.edu.au/item/q3qwq/adaptive-weighted-non-parametric-background-model-for-efficient-video-coding
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