吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (5): 1441-1446.doi: 10.7964/jdxbgxb201405035

• • 上一篇    下一篇

基于上下文感知的内容适应算法及其在UPnP AV中的应用

唐瑞春1, 2, 冯骁1, 丁香乾1, 徐惠敏1   

  1. 1.中国海洋大学 信息科学与工程学院,山东 青岛 266100;
    2.海尔数字化家电国家重点实验室,山东 青岛 266101
  • 收稿日期:2013-02-21 出版日期:2014-09-01 发布日期:2014-09-01
  • 作者简介:唐瑞春(1968), 女, 教授, 博士.研究方向:网络流媒体.E-mail:tangruichun@ouc.edu.cn
  • 基金资助:
    国家科技重大专项项目(2013ZX03005011).

Contest-based content adaptation algorithm and its application in UPnP AV

TANG Rui-chun1,2, FENG Xiao1, DING Xiang-qian1, XU Hui-min1   

  1. 1.College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China;
    2.State Key Laboratory of Digital Appliances, Qingdao 266101, China
  • Received:2013-02-21 Online:2014-09-01 Published:2014-09-01

摘要: 针对UPnP AV如何根据AV播放环境进行内容适应的问题,提出一种基于上下文感知的内容适应算法CBCAA。首先获取AV播放环境上下文信息并进行分类;然后根据不同类型的上下文信息构建约束模型,利用约束满足求解方法得到相应的媒体内容描述决策集MDDS;最后将媒体源码按MDDS的描述转码,得到适应上下文要求的媒体内容目标码。CBCAA算法能够实现UPnP AV 对AV播放环境的内容适应,从而提供智能多媒体服务。仿真实验表明了该算法的有效性。

关键词: 计算机应用, 上下文感知, 内容适应, 约束满足问题

Abstract: The content adaptation based on context in UPnP AV is investigated. A Context-based Content Adaptation Algorithm (CBCAA) is proposed. First, the algorithm obtains and classifies the context information. Then it builds the constraint model according to the different types of information, and the constraint satisfaction method is used to acquire Media Description Decision Set (MDDS). Finally, a bit-stream adaptation engine transcodes the media from source media to object media based on MDDS. CBCAA enables UPnP AV adapt the content to environment and provides intelligent services. Simulation results demonstrate the effectiveness of proposed algorithm.

Key words: computer application, context awareness, content adaptation, constraint satisfaction problem

中图分类号: 

  • TP391
[1] Ritchie J, Kuhnel T, Kang J, et al. UPnP AV Architecture:1[EB/OL].[ 2008-09-30]. http://upnp.org/sdcps-and-certi-fication/-standards/device-architecture-documents/
[2] Sung Jongwoo, Kim Daeyoung, Song Hyungjoo, et al. UPnP based intelliget multimedia service architecture for digital home network[J]. Software Technologies for Future Embedded and Ubiquitous Systems, 2006, 3(4): 521-526.
[3] Kang Dong-Oh, Kang Kyuchang, Choi Sunggi, et al. UPnP AV Architectural multimedia system with a home gateway powered by the OSGi platform[J]. IEEE Transactions on Consumer Electronics, 2005, 51(1): 87-93.
[4] Mets K, Nelis J, Verslype D, et al. Design of a context aware multimedia management system for home environments[J]. Computation World: Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns, 2009, 111(1): 49-54.
[5] Pereira F, Vetro A, Sikora T. Multimedia retrieval and delivery: essential metadata challenges and standards[J]. Proceedings of the IEEE, 2008, 96(4): 721-744.
[6] Feng Yu-qing, Tang Rui-chun, Zhai Yi-li, et al. Personalized media recommendation algorithm based on smart home[C]∥The Second International Conference on e-Technologies and Networks for Development(ICeND), Malaysia, 2013: 63-67.
[7] 孙吉贵,高健,张永刚. 一个基于最小冲突修补的动态约束满足求解算法[J]. 计算机研究与发展, 2007, 44(12): 2078-2084.Sun Ji-gui, Gao Jian, Zhang Yong-gang. A mini-conflict repair based algorithm for solving dynamic constraint satisfaction problems[J]. Computer Research and Development, 2007, 44(12): 2078-2084.
[8] Jannach D, Leopold K, Timmerer C, et al. A knowledge-based framework for multimedia adaptation[J].Applied Intelligence, 2006, 24(2): 109-125.
[9] Kofler I, Seidl J, Timmerer C, et al. Using MPEG-21 for cross-layer multimedia content adaptation[J]. Journal on Signal, Image and Video Processing, 2008, 2(4): 355-370.
[10] Sofokleous A A, Angelides M C. DCAF: an MPEG-21 dynamic content adaptation framework[J]. Multimedia Tools and Applications, 2008, 40(2):151-182.
[11] Deursen D V, Lancker W V. NinSuna:a format-independent, semantic-aware multimedia content adaptation platform[C]∥Proceedings of the 10th IEEE International Symposium on Multimedia, Orlando, USA, 2008: 491-492.
[1] 刘富,宗宇轩,康冰,张益萌,林彩霞,赵宏伟. 基于优化纹理特征的手背静脉识别系统[J]. 吉林大学学报(工学版), 2018, 48(6): 1844-1850.
[2] 王利民,刘洋,孙铭会,李美慧. 基于Markov blanket的无约束型K阶贝叶斯集成分类模型[J]. 吉林大学学报(工学版), 2018, 48(6): 1851-1858.
[3] 金顺福,王宝帅,郝闪闪,贾晓光,霍占强. 基于备用虚拟机同步休眠的云数据中心节能策略及性能[J]. 吉林大学学报(工学版), 2018, 48(6): 1859-1866.
[4] 赵东,孙明玉,朱金龙,于繁华,刘光洁,陈慧灵. 结合粒子群和单纯形的改进飞蛾优化算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1867-1872.
[5] 刘恩泽,吴文福. 基于机器视觉的农作物表面多特征决策融合病变判断算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1873-1878.
[6] 欧阳丹彤, 范琪. 子句级别语境感知的开放信息抽取方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1563-1570.
[7] 刘富, 兰旭腾, 侯涛, 康冰, 刘云, 林彩霞. 基于优化k-mer频率的宏基因组聚类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1593-1599.
[8] 桂春, 黄旺星. 基于改进的标签传播算法的网络聚类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1600-1605.
[9] 刘元宁, 刘帅, 朱晓冬, 陈一浩, 郑少阁, 沈椿壮. 基于高斯拉普拉斯算子与自适应优化伽柏滤波的虹膜识别[J]. 吉林大学学报(工学版), 2018, 48(5): 1606-1613.
[10] 车翔玖, 王利, 郭晓新. 基于多尺度特征融合的边界检测算法[J]. 吉林大学学报(工学版), 2018, 48(5): 1621-1628.
[11] 赵宏伟, 刘宇琦, 董立岩, 王玉, 刘陪. 智能交通混合动态路径优化算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1214-1223.
[12] 黄辉, 冯西安, 魏燕, 许驰, 陈慧灵. 基于增强核极限学习机的专业选择智能系统[J]. 吉林大学学报(工学版), 2018, 48(4): 1224-1230.
[13] 傅文博, 张杰, 陈永乐. 物联网环境下抵抗路由欺骗攻击的网络拓扑发现算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1231-1236.
[14] 曹洁, 苏哲, 李晓旭. 基于Corr-LDA模型的图像标注方法[J]. 吉林大学学报(工学版), 2018, 48(4): 1237-1243.
[15] 侯永宏, 王利伟, 邢家明. 基于HTTP的动态自适应流媒体传输算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1244-1253.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!