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Compressive sensing depth video coding via gaussian mixture models and object edges
Wang, Kang1; Lan, Xuguang1; Li, Xiangwei(李翔伟)2; Yang, Meng1; Zheng, Nanning1; Lan, Xuguang (xglan@mail.xjtu.edu.cn)
2018
会议名称18th Pacific-Rim Conference on Multimedia, PCM 2017
会议录名称Advances in Multimedia Information Processing – PCM 2017 - 18th Pacific-Rim Conference on Multimedia, Revised Selected Papers
卷号10735 LNCS
页码96-104
会议日期2017-09-28
会议地点Harbin, China
出版者Springer Verlag
产权排序2
摘要

In this paper, we propose a novel compressive sensing depth video (CSDV) coding scheme based on Gaussian mixture models (GMM) and object edges. We first compress several depth videos to get CSDV frames in the temporal direction. A whole CSDV frame is divided into a set of non-overlap patches in which object edges is detected by Canny operator to reduce the computational complexity of quantization. Then, we allocate variable bits for different patches based on the percentages of non-zero pixels in every patch. The GMM is used to model the CSDV frame patches and design product vector quantizers to quantize CSDV frames. The experimental results show that our compression scheme achieves a significant Bjontegaard Delta (BD)-PSNR improvement about 2–10 dB when compared to the standard video coding schemes, e.g. Uniform Scalar Quantization-Differential Pulse Code Modulation (USQ-DPCM) and H.265/HEVC. © Springer International Publishing AG, part of Springer Nature 2018.

作者部门光电测量技术实验室
DOI10.1007/978-3-319-77380-3_10
收录类别EI
ISBN号9783319773797
语种英语
ISSN号03029743
引用统计
文献类型会议论文
条目标识符http://ir.opt.ac.cn/handle/181661/30321
专题光电测量技术实验室
通讯作者Lan, Xuguang (xglan@mail.xjtu.edu.cn)
作者单位1.Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, China
2.Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China
推荐引用方式
GB/T 7714
Wang, Kang,Lan, Xuguang,Li, Xiangwei,et al. Compressive sensing depth video coding via gaussian mixture models and object edges[C]:Springer Verlag,2018:96-104.
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