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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/51653


    Title: VQ Applications in Steganographic Data Hiding Upon Multimedia Images
    Authors: Wang,WJ;Huang,CT;Wang,SJ
    Contributors: 資訊工程學系
    Keywords: VECTOR QUANTIZATION;COMPRESSED IMAGES;SPREAD-SPECTRUM;SCHEME;INDEX;SECURITY;DESIGN;DISTORTION;ALGORITHM
    Date: 2011
    Issue Date: 2012-03-27 18:58:38 (UTC+8)
    Publisher: 國立中央大學
    Abstract: Data hiding is one of the most important techniques to achieve better data and communication protection by hiding information into a media carrier. It provides a secure method to distribute data through a public and open channel. Data hiding for vector quantization (VQ)-based images focuses on the problem of embedding secret data into a cover VQ-based image to achieve secret communication and data protection. This paper provides a state-of-the-art review and comparison of the different existing data-hiding methods for VQ-based images. In this paper, we classify VQ-based data-hiding methods into four nonoverlapping groups according to their reversibility and output formats, introduce the details of the representative methods, summarize the features of the representative methods, and compare the performance of the representative methods using peak signal-to-noise ratio, capacity of secret data, and bit rate. Our paper shows that an irreversible method is very likely a VQ-based data-hiding method that produces a stego-image as its output, and it can embed more secret data than a reversible method. Nonstandard encoding methods (e.g., joint neighboring coding) are becoming popular in reversible data hiding since they can increase the capacity for embedding the secret data. Some methods with high compression rate, such as the search-order coding-based methods, may reduce the compression rate in return for the capacity for the secret data.
    Relation: IEEE SYSTEMS JOURNAL
    Appears in Collections:[Department of Computer Science and information Engineering] journal & Dissertation

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