An efficient coding scheme for surveillance videos based on high efficiency video coding

Jianfu Wang, Lanfang Dong
{"title":"An efficient coding scheme for surveillance videos based on high efficiency video coding","authors":"Jianfu Wang, Lanfang Dong","doi":"10.1109/ICNC.2014.6975958","DOIUrl":null,"url":null,"abstract":"As the latest coding standard, High Efficiency Video Coding (HEVC) has an obvious advantage in coding efficiency. Compared to H.264 Advanced Video Coding (H.264/AVC), HEVC can achieve about 50% bitrate reduction at the same subjective video quality. However, the enhancement in compression efficiency has been achieved at the cost of large increase in computational complexity. In this paper, to reduce the computational complexity, we propose a new coding scheme for surveillance videos using inter-frame difference to encode different image areas with different encoder options. The scheme is implemented through the proposed fast Coding Unit (CU) size decision algorithm. With using the luma component of difference image, the proposed algorithm can segment out moving objects from background, and then select proper CU size for different areas. Experimental results show that the encoding complexity can be reduced by an average of 45% with small increment in bitrate and negligible loss in Peak Signal to Noise Ratio (PSNR) compared to the High efficiency video coding test Model (HM) 9.2 reference software. Furthermore, the proposed scheme is not only applied to surveillance videos recorded by static cameras, but also applied to regular videos with excellent coding performance.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 10th International Conference on Natural Computation (ICNC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2014.6975958","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

As the latest coding standard, High Efficiency Video Coding (HEVC) has an obvious advantage in coding efficiency. Compared to H.264 Advanced Video Coding (H.264/AVC), HEVC can achieve about 50% bitrate reduction at the same subjective video quality. However, the enhancement in compression efficiency has been achieved at the cost of large increase in computational complexity. In this paper, to reduce the computational complexity, we propose a new coding scheme for surveillance videos using inter-frame difference to encode different image areas with different encoder options. The scheme is implemented through the proposed fast Coding Unit (CU) size decision algorithm. With using the luma component of difference image, the proposed algorithm can segment out moving objects from background, and then select proper CU size for different areas. Experimental results show that the encoding complexity can be reduced by an average of 45% with small increment in bitrate and negligible loss in Peak Signal to Noise Ratio (PSNR) compared to the High efficiency video coding test Model (HM) 9.2 reference software. Furthermore, the proposed scheme is not only applied to surveillance videos recorded by static cameras, but also applied to regular videos with excellent coding performance.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
一种基于高效视频编码的高效监控视频编码方案
高效视频编码(High Efficiency Video coding, HEVC)作为最新的编码标准,在编码效率方面具有明显的优势。与H.264高级视频编码(H.264/AVC)相比,在相同的主观视频质量下,HEVC可以实现约50%的比特率降低。然而,压缩效率的提高是以计算复杂度的大幅增加为代价的。为了降低计算复杂度,本文提出了一种新的监控视频编码方案,利用帧间差分对不同的图像区域使用不同的编码器选项进行编码。该方案通过提出的快速编码单元(CU)大小决策算法实现。该算法利用差分图像的亮度分量,从背景中分割出运动目标,然后在不同区域选择合适的CU大小。实验结果表明,与高效视频编码测试模型(HM) 9.2参考软件相比,编码复杂度平均降低45%,比特率增量很小,峰值信噪比(PSNR)损失可以忽略。此外,该方案不仅适用于静态摄像机录制的监控视频,也适用于编码性能优异的普通视频。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Graph based K-nearest neighbor minutiae clustering for fingerprint recognition Applications of artificial intelligence technologies in credit scoring: A survey of literature Construction of linear dynamic gene regulatory network based on feedforward neural network A new dynamic clustering method based on nuclear field A multi-objective ant colony optimization algorithm based on the Physarum-inspired mathematical model
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1