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IEEE Fellow林楠教师科研团队系列讲座

  发表日期:2017年4月21日    阅读:661 

IEEE Fellow 林楠教授科研团队系列讲座

 

 

报告日期 Date:    2017424

报告时间 Time:    1430

报告地点 Location:  物信学院祥联厅

 

报告人:

        Dr. Minqiang Jiang 蒋敏强 博士

Research Assistant Professor, Department of Computer Engineering, Santa Clara University, U.S.A

美國 加州 圣塔克拉拉大学 计算机工程系 研究助理教授 

 

      林楠 Nam Ling, Ph.D., IEEE Fellow, IET Fellow

      美國 加州 圣塔克拉拉大学 圣菲利波家族讲教授  计算机工程系系主任

      中国 福州大学 讲座教授

       Sanfilippo Family Chair Professor and Chair, Department of Computer Engineering,

       Santa Clara   University, U.S.A

       Chair Professor, Fuzhou University, China.

 

Lecture 1 报告 

 

Title:   An Approach to Image Compression using R-D Optimal OMP Selection

报告标题:   一种使用率失真优化进行正交匹配追踪选择的图像压缩方法

Abstract Transform-based coding is a technique that is widely used in image and video compression, where compression is achieved via decomposing each component block or patch over a complete dictionary known to provide compaction. Recently, there has been a growing interest in using basis selection algorithms for signal approximation and compression. Signal approximation using a linear combination of basis functions from an over-complete dictionary has proven to be an NP-hard problem. To solve this problem, Orthogonal Matching Pursuit (OMP) algorithm is often used to select dictionary elements and their coefficients. Based on its iterative nature, this report discusses a Rate-Distortion Optimization (RDO) method to select the number of nonzero coefficients assuming that a sparsity constraint is given. Experimental results demonstrate a very good improvement by our proposed method over conventional DCT based scheme.

 

摘要 基于变换的编码技术广泛地应用于图像和视频压缩领域。其通过解压每一个编码块或者形成一个超完备字典的方式来实现压缩。目前,使用基向量选择进行信号近似和压缩的算法日益成为热点。用超完备字典生成的基本函数的线性组合来进行信号近似是一个NP-hard问题。为了解决这个问题,正交匹配追踪选择(OMP)算法被用做基向量及其相关系数选择。由于其迭代性,本文假设在稀疏性有约束的情况下,讨论了一种使用率失真优化(RDO)来进行选择非零系数的方法。实验结果表明相对常规的DCT变换,本方法有着明显的性能提升。

 

Lecture 2 报告

 

Title:    Enhanced Intra Prediction Mode Coding by using Reference Samples

报告标题:    利用参考像素来提高帧内预测模式编码

 

Abstract HEVC utilizes 35 intra prediction modes to predict each luma block. To ensure the selected mode for a luma block is signaled with minimal overhead, HEVC defines a set of three most probable modes (MPM), which are derived based on the modes of its two neighbors. If the intra prediction mode is not one of these three most probable modes, it is coded as one of the remaining modes using longer codeword. To improve the efficiency of intra prediction mode coding further, this report presents a method to derive a second set of most probable modes, by using neighboring reconstructed samples. One line of reference samples is used as the original pixels and predicted by another line of reference samples. The sum of absolute difference (SAD) is employed as a measurement to select several modes with minimum SADs as the second MPM set.  Experimental results show that for all intra configurations, the proposed method achieves on average 0.54%, 0.34%, and 0.34% BD-rate reductions for Y, U and V, respectively.

 

摘要 高效率视频编码(HEVC)使用35个帧内预测模式来预测每一个亮度块。为了确保亮度块所选择的预测模式使用最少的信号编码,HEVC定义了一组最有可能模式(MPM)。通过当前亮度块的两个邻居块可以生成一组三个MPM模式。如果预测模式不是这三个MPM模式其中任何一个,那么这个预测模式将用一个较长的码字进行表示。为了进一步提高帧内预测模式编码的效率,本文提出了一种利用相邻的参考像素生成第二组MPM的方法。一行(或一列)参考像素被用做原始像素来预测另一行(或一列)参考像素。使用最小绝对和差(SAD) 来选择并生成第二组MPM模式。实验结果显示本方法在Y, UV三个分量上BD-rate分别平均降低了0.54%, 0.34%, 0.34%

 

Biography 

 

Minqiang Jiang is a Research Assistant Professor at Santa Clara University (SCU), U.S.A. He received his B.S. degree in Electrical Engineering from Xidian University (China), M.S. degree in Electrical Engineering from Tsinghua University (China), and Ph.D. degree in Computer Engineering from Santa Clara University (USA) in 2006. His research interests are in the field of video coding, specifically in the areas of rate control, motion estimation, intra prediction, development of video coding standards, and image/video sparse coding. He has 12 publications and an adopted standard contribution. Before joining SCU in 2015, he worked as a software engineer in developing h.264/h.265 video codec. His recent research concerns proposals of H.266 and sparse coding.

蒋敏强是美国圣塔克拉拉大学研究助理教授。 于中国西电大学取得电气工程学士学位、于中国清华大学取得电气工程硕士学位,于2006年毕业于美国圣克拉拉大学计算机工程并获得博士学位。他的研究方向主要在视频编码领域,尤其是速率控制,运动估计,帧内预测,视频编码标准,以及图像/视频稀疏编码。蒋博士共发表了12学术论文。他擁有1 項被採納的标准方案。2015年加入圣克拉拉大学之前,他是h.264/h.265视频编解码器软件开发工程师。他近期研究工作主要关注下一代视频编码标准H.266草案和视频稀疏编码。

 

Nam Ling received the B.Eng. degree from the National University of Singapore and the M.S. and Ph.D. degrees from the University of Louisiana, Lafayette, U.S.A. He is currently the Sanfilippo Family Chair Professor (University Endowed Chair) of Santa Clara University (U.S.A) and the Chair of its Department of Computer Engineering. From 2002 to 2010, he was an Associate Dean for its School of Engineering. Currently, he is also a Distinguished Professor for Xi’an University of Posts & Telecommunications (China), a Consulting Professor for the National University of Singapore, a Guest Professor for Tianjin University, a Guest Professor for Shanghai Jiao Tong University, and a Cuiying Chair Professor for Lanzhou University (China). He has more than 185 publications (including books) in video/image coding and systolic arrays. He also has seven adopted standards contributions and has filed/granted more than 20 U.S./European/PCT patents. He is an IEEE Fellow due to his contributions to video coding algorithms and architectures. He is also an IET Fellow. He was named IEEE Distinguished Lecturer twice and was also an APSIPA Distinguished Lecturer. He received the IEEE ICCE Best Paper Award (First Place) and the IEEE Umedia Best Paper Award. He received six awards from the University, four at the University level (Outstanding Achievement, Recent Achievement in Scholarship, President’s Recognition, and Sustained Excellence in Scholarship) and two at the School/College level (Researcher of the Year and Teaching Excellence). He has served as Keynote Speakers for IEEE APCCAS, VCVP (twice), JCPC, IEEE ICAST, IEEE ICIEA, IET FC & U-Media, IEEE U-Media, and Workshop at XUPT (twice), as well as a Distinguished Speaker for IEEE ICIEA. He is/was General Chairs/CoChairs for IEEE Hot Chips, VCVP (twice), IEEE ICME, IEEE U-Media (thrice), and IEEE SiPS. He has also served as Technical Program CoChairs for IEEE ISCAS, APSIPA ASC, IEEE APCCAS, IEEE SiPS (twice), DCV, and IEEE VCIP. He was Technical Committee Chairs for IEEE CASCOM TC and IEEE TCMM, and has served as Guest Editors/Associate Editors for IEEE TCASI, IEEE J-STSP, Springer JSPS, Springer MSSP, and other journals. He has delivered more than 120 invited colloquia worldwide and has served as Visiting Professors/Consultants/Scientists for many institutions/companies.

林楠,毕业于新加坡国立大学电气工程系并在美国考获碩士及博士学位。从2010年开始,他是美国圣塔克拉拉大学圣菲利波家族(Sanfilippo Family)讲席教授及该校计算机工程系系主任。在20022010年期间, 他是该校工程学院副院长(主管研究生课程, 研究, 及师资发展)。当前, 他也是中国西安邮电大学特聘教授, 新加坡国立大学咨询教授, 天津大学客座教授, 上海交通大学客座教授, 及兰州大学翠英讲席教授(中国)。基于林教授在视频编码算法和体系结构所作出的贡献, 被授予IEEE Fellow (IEEE院士)。他也同时是IET Fellow。林教授共发表了超过185篇学术论文及书。他擁有7 項被採納的标准方案。他申请/擁有超过20項美国/欧洲/PCT专利。他两次受任命为IEEE杰出讲员,也是APSIPA杰出讲员。他在IEEE ICCE 2003IEEE Umedia 2016所发表的论文获得最佳论文奖。他在全大学层级获得四大奖狀(持续卓越研究奖, 校长表彰奖, 近期研究成就奖, 及杰出成就奖)。另在学院层级获得两大奖狀(卓越教学奖及年度研究员奖)。他担任国际会议的主讲人(IEEE APCCAS 2008, VCVP 2008, JCPC 2009, IEEE ICAST 2011, IEEE ICIEA 2012, IET FC & U-Media 2012, VCVP 2014, IEEE U-Media 2014, 及 Workshop at XUPT 2014 & 2016), 荣誉讲员(IEEE ICIEA 2010), 大会主席/共同主席(IEEE Hot Chips 1995, VCVP 2008, IEEE ICME 2013, VCVP 2014, IEEE U-Media 2014, UMedia 2015, Umedia 2016,IEEE SiPS 2015), 大会技术节目共同主席(IEEE SiPS 2000, DCV 2002, IEEE ISCAS 2007, IEEE SiPS 2007, APSIPA ASC 2010, IEEE APCCAS 2010, 及 IEEE VCIP 2013), 学会技术委员会主席(IEEE CASCOM TC及TCMM)。他也担任过客座编辑/副编辑(IEEE TCAS-I, IEEE J-STSP, Springer JSPS, 及Springer MSSP), 在超过120个邀请讲座上发表演讲, 也担任过許多公司及研究机构的客座教授/顧问/科学家/学者。

 


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