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            <name>Title</name>
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    <name>Article</name>
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          <name>Title</name>
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              <text>HHO-Based Vector Quantization Technique for Biomedical Image Compression in Cloud Computing</text>
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        <element elementId="49">
          <name>Subject</name>
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              <text>Biomedical image processing; cloud computing; data compression; HHO algorithm; LBG; vector quantization</text>
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              <text>In the present digital era, the exploitation of medical technologies and massive generation of medical data using different imaging modalities, adequate storage, management, and transmission of biomedical images necessitate image compression techniques. Vector quantization (VQ) is an effective image compression approach, and the widely employed VQ technique is Linde-Buzo-Gray (LBG), which generates local optimum codebooks for image compression. The codebook construction is treated as an optimization issue solved with utilization of metaheuristic optimization techniques. In this view, this paper designs an effective biomedical image compression technique in the cloud computing (CC) environment using Harris Hawks Optimization (HHO)-based LBG techniques. The HHO-LBG algorithm achieves a smooth transition among exploration as well as exploitation. To investigate the better performance of the HHO-LBG technique, an extensive set of simulations was carried out on benchmark biomedical images. The proposed HHO-LBG technique has accomplished promising results in terms of compression performance and reconstructed image quality.   2023 World Scientific Publishing Company.</text>
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          <name>Creator</name>
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              <text>Kumar T.S.; Jothilakshmi S.; James B.C.; Prakash M.; Arulkumar N.; Rekha C.</text>
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              <text>International Journal of Image and Graphics, Vol-23, No. 3</text>
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              <text>World Scientific</text>
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          <name>Date</name>
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              <text>2023-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1142/S0219467822400083" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1142/S0219467822400083&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122311596&amp;amp;doi=10.1142%2FS0219467822400083&amp;amp;partnerID=40&amp;amp;md5=0b465f1b811d8779c4526ae4995b62e9" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122311596&amp;amp;doi=10.1142%2fS0219467822400083&amp;amp;partnerID=40&amp;amp;md5=0b465f1b811d8779c4526ae4995b62e9&lt;/a&gt;</text>
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          <name>Rights</name>
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              <text>Restricted Access</text>
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              <text>ISSN: 2194678</text>
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              <text>Online</text>
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              <text>English</text>
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              <text>Kumar T.S., Department of CSE, BMS Institute of Technology and Management, Bengaluru, India; Jothilakshmi S., The American College, Madurai, India; James B.C., Angeles University Foundation, Angeles City, Philippines; Prakash M., Data Science and Analytics Center, Karpagam College of Engineering, Coimbatore, India; Arulkumar N., Department of CS, CHRIST (Deemed to Be University), Banglore, India; Rekha C., Department of IT, Sri Sai Ram Institute of Technology, Chennai, India</text>
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