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Tse-Chung Su

dblp:13/5097 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2011
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 61% Knowledge graphs · 30% Web and social media mining · 9%
Computer graphics and multimedia
1 paper
Image and video coding · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
GPUs and heterogeneous computing · 100%

Topics — the 7 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video coding
distributed video coding
0.112011
Real-time decoding for LDPC based distributed video coding · ACM Multimedia 2011
Image and video coding › distributed video coding
wyner-ziv video coding
0.112011
Real-time decoding for LDPC based distributed video coding · ACM Multimedia 2011
GPUs and heterogeneous computing › GPU computing › GPU video coding
GPU-accelerated video decoding
0.112011
Real-time decoding for LDPC based distributed video coding · ACM Multimedia 2011
Information retrieval
query suggestion
0.112009
Building term suggestion relational graphs from collective intelligence · WWW 2009
Knowledge graphs
semantic graph construction
0.112009
Building term suggestion relational graphs from collective intelligence · WWW 2009
Information retrieval › query suggestion
term suggestion
0.112009
Building term suggestion relational graphs from collective intelligence · WWW 2009
Web and social media mining
social network analysis
0.012009
Building term suggestion relational graphs from collective intelligence · WWW 2009

Methods — techniques the papers use, named apart from their topics

sum-product algorithm · 0.2GPGPU · 0.2CUDA · 0.2social network analysis · 0.1graph visualization · 0.1
YearPublicationVenuePosition
2011 Real-time decoding for LDPC based distributed video coding
abstract
Wyner-Ziv (WZ) video coding -- a particular case of distributed video coding (DVC), is well known for its low-complexity encoding and high-complexity decoding characteristics. Although some works have been made in recent years, especially for improving the coding efficiency, most reported WZ codecs have high time delay in the decoder, which hinders its practical values for applications with critical timing constraint. In this paper, a fully parallelized sum-product algorithm (SPA) for low density parity check accumulate (LDPCA) codes is proposed and realized through Compute Unified Device Architecture (CUDA) based on General-Purpose Graphics Processing Unit (GPGPU). Simulation results show that, through our work, QCIF (surveillance) videos can be decoded in real-time with extremely high quality and without rate-distortion (RD) performance loss.
Tse-Chung Su, Yun-Chung Shen, Ja-Ling Wu
ACM Multimedia1
2009 Building term suggestion relational graphs from collective intelligence
abstract
This paper proposes an effective approach to provide relevant search terms for conceptual Web search. 'Semantic Term Suggestion' function has been included so that users can find the most appropriate query term to what they really need. Conventional approaches for term suggestion involve extracting frequently occurring key terms from retrieved documents. They must deal with term extraction difficulties and interference from irrelevant documents. In this paper, we propose a semantic term suggestion function called Collective Intelligence based Term Suggestion (CITS). CITS provides a novel social-network based framework for relevant terms suggestion with a semantic graph of the search term without limiting to the specific query term. A visualization of semantic graph is presented to the users to help browsing search results from related terms in the semantic graph. The search results are ranked each time according to their relevance to the related terms in the entire query session. Comparing to two popular commercial search engines, a user study of 18 users on 50 search terms showed better user satisfactions and indicated the potential usefulness of proposed method in real-world search applications.
Jyh-Ren Shieh, Yung-Huan Hsieh, Yang-Ting Yeh, Tse-Chung Su, Ching-Yung Lin, Ja-Ling Wu
WWW4