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IEEE Fellow林楠教師科研團隊系列講座
發表日期:2017年4月21日 閱讀:2857

IEEE Fellow 林楠教授科研團隊系列講座

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報告日期 Date:????2017424

報告時間?Time: ???1430

報告地點?Location: ?物信學院祥聯廳

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報告人:

????????Dr. Minqiang Jiang 蔣敏強 博士

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

美國 加州 圣塔克拉拉大學?計算機工程系?研究助理教授?

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

??????美國 加州 圣塔克拉拉大學?圣菲利波家族講教授??計算機工程系系主任

??????中國?福州大學?講座教授

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

???????Santa Clara ??University, U.S.A

???????Chair Professor, Fuzhou University, China.

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Lecture 1?報告?

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

報告標題:???一種使用率失真優化進行正交匹配追蹤選擇的圖像壓縮方法

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.

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摘要?基于變換的編碼技術廣泛地應用于圖像和視頻壓縮領域。其通過解壓每一個編碼塊或者形成一個超完備字典的方式來實現壓縮。目前,使用基向量選擇進行信號近似和壓縮的算法日益成為熱點。用超完備字典生成的基本函數的線性組合來進行信號近似是一個NP-hard問題。為了解決這個問題,正交匹配追蹤選擇(OMP)算法被用做基向量及其相關系數選擇。由于其迭代性,本文假設在稀疏性有約束的情況下,討論了一種使用率失真優化(RDO)來進行選擇非零系數的方法。實驗結果表明相對常規的DCT變換,本方法有著明顯的性能提升。

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Lecture 2?報告

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Title: ???Enhanced Intra Prediction Mode Coding by using Reference Samples

報告標題:????利用參考像素來提高幀內預測模式編碼

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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.

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摘要?高效率視頻編碼(HEVC)使用35個幀內預測模式來預測每一個亮度塊。為了確保亮度塊所選擇的預測模式使用最少的信號編碼,HEVC定義了一組最有可能模式(MPM)。通過當前亮度塊的兩個鄰居塊可以生成一組三個MPM模式。如果預測模式不是這三個MPM模式其中任何一個,那么這個預測模式將用一個較長的碼字進行表示。為了進一步提高幀內預測模式編碼的效率,本文提出了一種利用相鄰的參考像素生成第二組MPM的方法。一行(或一列)參考像素被用做原始像素來預測另一行(或一列)參考像素。使用最小絕對和差(SAD) 來選擇并生成第二組MPM模式。實驗結果顯示本方法在Y, UV三個分量上BD-rate分別平均降低了0.54%, 0.34%, 0.34%。

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Biography?

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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.

蔣敏強是美國圣塔克拉拉大學研究助理教授。?于中國西電大學取得電氣工程學士學位、于中國清華大學取得電氣工程碩士學位,于2006年畢業于美國圣克拉拉大學計算機工程并獲得博士學位。他的研究方向主要在視頻編碼領域,尤其是速率控制,運動估計,幀內預測,視頻編碼標準,以及圖像/視頻稀疏編碼。蔣博士共發表了12學術論文。他擁有1 項被採納的標準方案。2015年加入圣克拉拉大學之前,他是h.264/h.265視頻編解碼器軟件開發工程師。他近期研究工作主要關注下一代視頻編碼標準H.266草案和視頻稀疏編碼。

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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,畢業于新加坡國立大學電氣工程系并在美國考獲碩士及博士學位。從2010年開始,他是美國圣塔克拉拉大學圣菲利波家族(Sanfilippo Family)講席教授及該校計算機工程系系主任。在20022010年期間, 他是該校工程學院副院長(主管研究生課程, 研究, 及師資發展)。當前, 他也是中國西安郵電大學特聘教授, 新加坡國立大學咨詢教授, 天津大學客座教授, 上海交通大學客座教授, 及蘭州大學翠英講席教授(中國)?;诹纸淌谠谝曨l編碼算法和體系結構所作出的貢獻, 被授予IEEE Fellow (IEEE院士)。他也同時是IET?Fellow。林教授共發表了超過185篇學術論文及書。他擁有7 項被採納的標準方案。他申請/擁有超過20項美國/歐洲/PCT專利。他兩次受任命為IEEE杰出講員,也是APSIPA杰出講員。他在IEEE ICCE 2003IEEE Umedia 2016所發表的論文獲得最佳論文獎。他在全大學層級獲得四大獎狀(持續卓越研究獎, 校長表彰獎, 近期研究成就獎, 及杰出成就獎)。另在學院層級獲得兩大獎狀(卓越教學獎及年度研究員獎)。他擔任國際會議的主講人(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), 榮譽講員(IEEE ICIEA 2010), 大會主席/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), 大會技術節目共同主席(IEEE SiPS 2000, DCV 2002, IEEE ISCAS 2007, IEEE SiPS 2007, APSIPA ASC 2010, IEEE APCCAS 2010, 及 IEEE VCIP 2013), 學會技術委員會主席(IEEE CASCOM TC及TCMM)。他也擔任過客座編輯/副編輯(IEEE TCAS-I, IEEE J-STSP, Springer JSPS, 及Springer MSSP), 在超過120個邀請講座上發表演講, 也擔任過許多公司及研究機構的客座教授/顧問/科學家/學者。

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