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Hiuk Jae Shim
Researcher at Sungkyunkwan University
Publications - 39
Citations - 605
Hiuk Jae Shim is an academic researcher from Sungkyunkwan University. The author has contributed to research in topics: Coding tree unit & Encoder. The author has an hindex of 10, co-authored 39 publications receiving 442 citations. Previous affiliations of Hiuk Jae Shim include Nanjing University of Information Science and Technology & Nanjing University of Science and Technology.
Papers
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Journal ArticleDOI
Low Rank Component Induced Spatial-Spectral Kernel Method for Hyperspectral Image Classification
TL;DR: Experimental results validate that the proposed LRCISSK method can effectively explore the spatial-spectral information and deliver superior performance with at least 1.30% higher OA and 1.03% higher AA on average when compared to other state-of-the-art classifiers.
Journal ArticleDOI
Fractional Quaternion Zernike Moments for Robust Color Image Copy-Move Forgery Detection
TL;DR: Experimental results have demonstrated that the proposed FrQZM-based algorithm can achieve an overall better performance than the state-of-the-art algorithms, especially in some additional operation cases.
Book ChapterDOI
Digital video scrambling method using intra prediction mode
TL;DR: This paper proposes a simple and effective digital video scrambling method which utilizes the intra block properties of a recent video coding technique, H.264.
Journal ArticleDOI
A Comparative Analysis of Information Hiding Techniques for Copyright Protection of Text Documents
TL;DR: A comparative analysis of information hiding techniques, especially on those ones which are focused on modifying the structure and content of digital texts, and their characteristics are highlighted along with their applications.
Journal ArticleDOI
Adjacent Superpixel-Based Multiscale Spatial-Spectral Kernel for Hyperspectral Classification
TL;DR: The superpixel-based multiscale strategy is utilized in the framework of ASGSSK (termed ASMGSSK) to further explore the multiscales structures of HSI for improving the classification performance and sidestepping the selection of an optimal superpixel scale.