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.
Papers
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Proceedings ArticleDOI
H.264/SVC temporal bit allocation with dependent distortion model
TL;DR: The bit allocation problem for hierarchical B-pictures in H.264/SVC is studied with a GOP-based dependent distortion model, which results in a highly efficient bit allocation scheme, which outperforms the rate control algorithm in the JSVM 9.12 reference codec by a significant margin.
Journal ArticleDOI
QoS-aware radio resource management scheme for CDMA cellular networks based on dynamic interference guard margin (IGM)
TL;DR: Simulations are conducted by OPNET to study performance of the proposed IGM scheme in terms of a defined cost function, new call blocking probability, handoff dropping probability and system utilization, under different traffic conditions.
Proceedings ArticleDOI
Automatic target-shape recognition via deformable wavelet templates
Jin Li,C.-C. Jay Kuo +1 more
TL;DR: A deformable wavelet template (DWT) is proposed for object shape description and a multiscale matching procedure is discussed, and the performance of the proposed algorithm is demonstrated with extensive experimental results.
Proceedings ArticleDOI
On interaction between MAC and transport layers for media streaming in 802.11 Ad-hoc networks
TL;DR: It is concluded that the assumption of the ideal steady-state TCP behavior is generally invalid in 802.11 multi-hop networking environment, and that congestion control based on TCP-friendly equation can hardly provide TCP-fair throughput and smoothness in802.11-based ad hoc networks.
Posted Content
A GMM-Based Stair Quality Model for Human Perceived JPEG Images
TL;DR: Based on the notion of just noticeable differences (JND), a stair quality function (SQF) was recently proposed to model human perception on JPEG images as discussed by the authors, which has a lower information criterion (BIC) value than the previous one, indicating that it offers a better model.