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Yu-Wen Huang
Researcher at National Taiwan University
Publications - 85
Citations - 3882
Yu-Wen Huang is an academic researcher from National Taiwan University. The author has contributed to research in topics: Motion estimation & Macroblock. The author has an hindex of 31, co-authored 85 publications receiving 3860 citations. Previous affiliations of Yu-Wen Huang include MediaTek.
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Analysis, fast algorithm, and VLSI architecture design for H.264/AVC intra frame coder
TL;DR: This paper proposed two solutions for platform-based design of H.264/AVC intra frame coder with comprehensive analysis of instructions and exploration of parallelism, and proposed a system architecture with four-parallel intra prediction and mode decision to enhance the processing capability.
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Analysis and architecture design of an HDTV720p 30 frames/s H.264/AVC encoder
Tung-Chien Chen,Shao-Yi Chien,Yu-Wen Huang,Chen-Han Tsai,Ching-Yeh Chen,To-Wei Chen,Liang-Gee Chen +6 more
TL;DR: The four-stage macroblock pipelined system architecture is proposed with an efficient scheduling and memory hierarchy, and the prototype chip of the efficient H.264/AVC video encoder for HDTV applications is implemented.
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Analysis and architecture design of variable block-size motion estimation for H.264/AVC
TL;DR: Two hardware architectures are proposed that can support traditional fixed block-size motion estimation as well as VBSME with less chip area overhead compared to previous approaches and an eight-parallel SAD tree with a shared reference buffer for H.264/AVC integer motion estimation is proposed.
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Analysis and complexity reduction of multiple reference frames motion estimation in H.264/AVC
TL;DR: This paper proposes a context-based adaptive method to speed up the multiple reference frames ME, and shows that the proposed algorithm can maintain competitively the same video quality as exhaustive search of several reference frames.
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Survey on Block Matching Motion Estimation Algorithms and Architectures with New Results
TL;DR: The main idea is quick checking of the entire search range with simplified matching criterion to globally eliminate impossible candidates, followed by finer selection among potential best matched candidates.