C
C.-C. Jay Kuo
Researcher at University of Southern California
Publications - 1070
Citations - 20283
C.-C. Jay Kuo is an academic researcher from University of Southern California. The author has contributed to research in topics: Computer science & Wavelet. The author has an hindex of 59, co-authored 955 publications receiving 16671 citations. Previous affiliations of C.-C. Jay Kuo include Ningbo University & Beihang University.
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Proceedings ArticleDOI
MTV-style home video generation via tempo analysis
TL;DR: In this article, an automatic approach to detect and remove bad shots often occurring in home video, such as video with poor lighting or motion blur, is presented, and the generation of MTV-style video clips by performing video and music tempo analysis and seeking an effective way in matching these two tempos.
Journal ArticleDOI
High Efficiency Intra Video Coding Based on Data-Driven Transform
Nan Lin,Yuan Zhang,C.-C. Jay Kuo +2 more
TL;DR: A high efficiency intra video coding based on data-driven transform, which is able to learn the source distributions of intra prediction residuals and improve the subsequent transform coding efficiency is proposed.
Proceedings ArticleDOI
Error-resilient coding of 3D graphics based on morphing and volume splitting
TL;DR: An error resilient coding method for 3D graphic models is proposed, which exploits the topology and geometry information of the original mesh to convert the mesh to a pre- defined 3D structure via morphing, and then partitions the morphed structure into a set of smaller pieces via volume splitting.
Journal ArticleDOI
Children Undergoing Laryngeal Surgery for Obstructive Sleep Apnea: NSQIP Analysis of Length of Stay, Readmissions, and Reoperations
TL;DR: In this data set, children with OSA undergoing laryngeal surgery experienced minimal postoperative complications, and recognition of the factors associated with increased LOS could lead to improvement in the quality of care for children withOSA.
Proceedings ArticleDOI
Low-variance TCP-friendly throughput estimation for congestion control of layered video multicast
Kitae Nahm,C.-C. Jay Kuo +1 more
TL;DR: The inclusion of min-max averaging operation in the basic average loss interval (ALI) algorithm helps reduce the variance of the ALI estimation by suppressing the correlation of neighboring loss intervals arising from the TCP additive-increase-multiplicative-decrease (AIMD) behavior.