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Chang Wen Chen
Researcher at University at Buffalo
Publications - 562
Citations - 10526
Chang Wen Chen is an academic researcher from University at Buffalo. The author has contributed to research in topics: Communication channel & Data compression. The author has an hindex of 43, co-authored 511 publications receiving 8904 citations. Previous affiliations of Chang Wen Chen include China University of Science and Technology & University of Missouri.
Papers
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Proceedings ArticleDOI
Compressive data gathering for large-scale wireless sensor networks
TL;DR: This paper presents the first complete design to apply compressive sampling theory to sensor data gathering for large-scale wireless sensor networks and shows the efficiency and robustness of the proposed scheme.
Journal ArticleDOI
Joint source channel rate-distortion analysis for adaptive mode selection and rate control in wireless video coding
TL;DR: An analytic solution for adaptive intra mode selection and joint source-channel rate control under time-varying wireless channel conditions is derived and significantly improves the end-to-end video quality in wireless video coding and transmission.
Journal ArticleDOI
Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications
TL;DR: This work proposes a robust algorithm for the segmentation of three-dimensional (3-D) image data based on a novel combination of adaptive K-mean clustering and knowledge-based morphological operations that has been successfully applied to a sequence of cardiac CT volumetric images.
Journal ArticleDOI
No-Reference Quality Metric of Contrast-Distorted Images Based on Information Maximization
TL;DR: The proposed blind IQA method generates an overall quality estimation of a contrast-distorted image by properly combining local and global considerations and demonstrates the superiority of the training-free blind technique over state-of-the-art full- and no-reference IQA methods.
Journal ArticleDOI
Blind Quality Assessment Based on Pseudo-Reference Image
TL;DR: Comparative studies on five large IQA databases show that the proposed BPRI model is comparable to the state-of-the-art opinion-aware- and OU-BIQA models, and not only performs well on natural scene images, but also is applicable to screen content images.