relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4025/
title: Improving TDWZ Correlation Noise Estimation: A Deep Learning based Approach
creator: Vu Huu, Tien
creator: Thao, Nguyen Thi Huong
creator: Vu van, San
creator: Hoang, Van Xiem
subject: Electronics and Communications
subject: Electronics and Computer Engineering
description: Transform domain Wyner-Ziv video coding (TDWZ) has shown its benefits in compressing video applications with limited resources such as visual surveillance systems, remote sensing and wireless sensor networks. In TDWZ, the correlation noise model (CNM) plays a vital role since it directly affects to the number of bits needed to send from the encoder and thus the overall TDWZ compression performance. To achieve CNM with high accurate for TDWZ, we propose in this paper a novel CNM estimation approach in which the CNM with Laplacian distribution is adaptively estimated based on a deep learning (DL) mechanism. The proposed DL based CNM includes two hidden layers and a linear activation function to adaptively update the Laplacian parameter. Experimental results showed that the proposed TDWZ codec significantly outperforms the relevant benchmarks, notably by around 35% bitrate saving when compared to the DISCOVER codec and around 22% bitrate saving when compared to the HEVC Intra benchmark while providing a similar perceptual quality.
publisher: Radio and Electronics Association of Vietnam
date: 2020-06
type: Article
type: PeerReviewed
format: application/pdf
language: en
identifier: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/4025/1/254_HVXiem_30_6.pdf
identifier:   Vu Huu, Tien and Thao, Nguyen Thi Huong and Vu van, San and Hoang, Van Xiem  (2020) Improving TDWZ Correlation Noise Estimation: A Deep Learning based Approach.  REV Journal on Electronics and Communications, 10  (1-2).   pp. 1-10.  ISSN 1859 - 378X    (In Press)  
relation: https://www.rev-jec.org/index.php/rev-jec
relation: http://dx.doi.org/10.21553/rev-jec.254