{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T21:43:57Z","timestamp":1773870237028,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:00:00Z","timestamp":1731369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Culture, Sports and Tourism","award":["RS-2024-00396709"],"award-info":[{"award-number":["RS-2024-00396709"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This study investigates the integration of quantum computing, classical methods, and deep learning techniques for enhanced image processing in dynamic 6G networks, while also addressing essential aspects of copyright technology and detection. Our findings indicate that quantum methods excel in rapid edge detection and feature extraction but encounter difficulties in maintaining image quality compared to classical approaches. In contrast, classical methods preserve higher image fidelity but struggle to satisfy the real-time processing requirements of 6G applications. Deep learning techniques, particularly CNNs, demonstrate potential in complex image analysis tasks but demand substantial computational resources. To promote the ethical use of AI-generated images, we introduce copyright detection mechanisms that employ advanced algorithms to identify potential infringements in generated content. This integration improves adherence to intellectual property rights and legal standards, supporting the responsible implementation of image processing technologies. We suggest that the future of image processing in 6G networks resides in hybrid systems that effectively utilize the strengths of each approach while incorporating robust copyright detection capabilities. These insights contribute to the development of efficient, high-performance image processing systems in next-generation networks, highlighting the promise of integrated quantum-classical\u2013classical deep learning architectures within 6G environments.<\/jats:p>","DOI":"10.3390\/info15110727","type":"journal-article","created":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T06:28:32Z","timestamp":1731392912000},"page":"727","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4693-2507","authenticated-orcid":false,"given":"Jongho","family":"Seol","sequence":"first","affiliation":[{"name":"Department of Computer Science, Middle Georgia State University, Warner Robins, GA 31093, USA"}]},{"given":"Hye-Young","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Games\/Game Software, Hongik University, Seoul 121-791, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2151-5332","authenticated-orcid":false,"given":"Abhilash","family":"Kancharla","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Tampa, Tampa, FL 33606, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1068-9855","authenticated-orcid":false,"given":"Jongyeop","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Georgia Southern University, Statesboro, GA 30458, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1111\/j.1468-0394.2007.00433.x","article-title":"Artificial intelligence applications in the telecommunications industry","volume":"24","author":"Qi","year":"2007","journal-title":"Expert Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1587\/transcom.2022CEI0002","article-title":"Machine learning in 6G wireless communications","volume":"106","author":"Ohtsuki","year":"2023","journal-title":"IEICE Trans. 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