A Novel Encryption-Then-Lossy-Compression Scheme of Color Images Using Customized Residual Dense Spatial Network
Nowadays it has still remained as a big challenge to efficiently compress color images in the encrypted domain. In this paper we present a novel deep-learning-based approach to encryption-then-lossy-compression (ETC) of color images by incorporating the domain knowledge of the encrypted image recons...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on multimedia 2023, Vol.25, p.4026-4040 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 4040 |
|---|---|
| container_issue | |
| container_start_page | 4026 |
| container_title | IEEE transactions on multimedia |
| container_volume | 25 |
| creator | Wang, Chuntao Zhang, Tianjian Chen, Hao Huang, Qiong Ni, Jiangqun Zhang, Xinpeng |
| description | Nowadays it has still remained as a big challenge to efficiently compress color images in the encrypted domain. In this paper we present a novel deep-learning-based approach to encryption-then-lossy-compression (ETC) of color images by incorporating the domain knowledge of the encrypted image reconstruction process. In specific, a simple yet effective uniform down-sampling is utilized for lossy compression of images encrypted with a modulo-256 addition, and the task of image reconstruction from an encrypted down-sampled image is then formulated as a problem of constrained super-resolution (SR) reconstruction. A customized residual dense spatial network (RDSN) is proposed to solve the formulated constrained SR task by taking advantage of spatial attention mechanism (SAM), global skip connection (GSC), and uniform down-sampling constraint (UDC) that is specific to an ETC system. Extensive experimental results show that the proposed ETC scheme achieves significant performance improvement compared with other state-of-the-art ETC methods, indicating the feasibility and effectiveness of the proposed deep-learning based ETC scheme. |
| doi_str_mv | 10.1109/TMM.2022.3171099 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_crossref_primary_10_1109_TMM_2022_3171099</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9765370</ieee_id><sourcerecordid>2862646608</sourcerecordid><originalsourceid>FETCH-LOGICAL-c291t-5598bdb5dd43f2e7357f9b799f3db619e9bd92e3de52cf2efc6eeb9bb20e91813</originalsourceid><addsrcrecordid>eNo9kNFPwjAQxhujiYi-m_jSxOdh263r-kgmKglgIvC8rOsNhmyd7abBv94SiE933-X33eU-hO4pGVFK5NNqPh8xwtgopMJreYEGVEY0IESIS99zRgLJKLlGN87tCKERJ2KA2jFemG_Y40lT2EPbVaYJVltogplx7hCkpm4tOOfHeFlsoQZsSpyavbF4WucbcHjtqmaD0951pq5-QeMPcJXu8z1-hsYBXrZ5V3m1gO7H2M9bdFXmewd35zpE65fJKn0LZu-v03Q8CwomaRdwLhOlFdc6CksGIuSilEpIWYZaxVSCVFoyCDVwVnigLGIAJZViBCRNaDhEj6e9rTVfPbgu25neNv5kxpKYxVEck8RT5EQV1v9rocxaW9W5PWSUZMdcM59rdsw1O-fqLQ8nSwUA_7gUMQ8FCf8Awvx1cg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2862646608</pqid></control><display><type>article</type><title>A Novel Encryption-Then-Lossy-Compression Scheme of Color Images Using Customized Residual Dense Spatial Network</title><source>IEEE Electronic Library (IEL)</source><creator>Wang, Chuntao ; Zhang, Tianjian ; Chen, Hao ; Huang, Qiong ; Ni, Jiangqun ; Zhang, Xinpeng</creator><creatorcontrib>Wang, Chuntao ; Zhang, Tianjian ; Chen, Hao ; Huang, Qiong ; Ni, Jiangqun ; Zhang, Xinpeng</creatorcontrib><description>Nowadays it has still remained as a big challenge to efficiently compress color images in the encrypted domain. In this paper we present a novel deep-learning-based approach to encryption-then-lossy-compression (ETC) of color images by incorporating the domain knowledge of the encrypted image reconstruction process. In specific, a simple yet effective uniform down-sampling is utilized for lossy compression of images encrypted with a modulo-256 addition, and the task of image reconstruction from an encrypted down-sampled image is then formulated as a problem of constrained super-resolution (SR) reconstruction. A customized residual dense spatial network (RDSN) is proposed to solve the formulated constrained SR task by taking advantage of spatial attention mechanism (SAM), global skip connection (GSC), and uniform down-sampling constraint (UDC) that is specific to an ETC system. Extensive experimental results show that the proposed ETC scheme achieves significant performance improvement compared with other state-of-the-art ETC methods, indicating the feasibility and effectiveness of the proposed deep-learning based ETC scheme.</description><identifier>ISSN: 1520-9210</identifier><identifier>EISSN: 1941-0077</identifier><identifier>DOI: 10.1109/TMM.2022.3171099</identifier><identifier>CODEN: ITMUF8</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Cloud computing ; Color ; Color imagery ; Constraints ; Cryptography ; Customization ; Deep learning ; Encrypted image compression ; Encryption ; Feature extraction ; Image coding ; Image compression ; Image reconstruction ; residual dense network ; Sampling ; spatial attention mechanism ; super-resolution reconstruction</subject><ispartof>IEEE transactions on multimedia, 2023, Vol.25, p.4026-4040</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-5598bdb5dd43f2e7357f9b799f3db619e9bd92e3de52cf2efc6eeb9bb20e91813</citedby><cites>FETCH-LOGICAL-c291t-5598bdb5dd43f2e7357f9b799f3db619e9bd92e3de52cf2efc6eeb9bb20e91813</cites><orcidid>0000-0002-5482-1766 ; 0000-0002-7666-8985 ; 0000-0002-0212-3501 ; 0000-0002-7520-9031</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9765370$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,777,781,793,4010,27904,27905,27906,54739</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9765370$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wang, Chuntao</creatorcontrib><creatorcontrib>Zhang, Tianjian</creatorcontrib><creatorcontrib>Chen, Hao</creatorcontrib><creatorcontrib>Huang, Qiong</creatorcontrib><creatorcontrib>Ni, Jiangqun</creatorcontrib><creatorcontrib>Zhang, Xinpeng</creatorcontrib><title>A Novel Encryption-Then-Lossy-Compression Scheme of Color Images Using Customized Residual Dense Spatial Network</title><title>IEEE transactions on multimedia</title><addtitle>TMM</addtitle><description>Nowadays it has still remained as a big challenge to efficiently compress color images in the encrypted domain. In this paper we present a novel deep-learning-based approach to encryption-then-lossy-compression (ETC) of color images by incorporating the domain knowledge of the encrypted image reconstruction process. In specific, a simple yet effective uniform down-sampling is utilized for lossy compression of images encrypted with a modulo-256 addition, and the task of image reconstruction from an encrypted down-sampled image is then formulated as a problem of constrained super-resolution (SR) reconstruction. A customized residual dense spatial network (RDSN) is proposed to solve the formulated constrained SR task by taking advantage of spatial attention mechanism (SAM), global skip connection (GSC), and uniform down-sampling constraint (UDC) that is specific to an ETC system. Extensive experimental results show that the proposed ETC scheme achieves significant performance improvement compared with other state-of-the-art ETC methods, indicating the feasibility and effectiveness of the proposed deep-learning based ETC scheme.</description><subject>Cloud computing</subject><subject>Color</subject><subject>Color imagery</subject><subject>Constraints</subject><subject>Cryptography</subject><subject>Customization</subject><subject>Deep learning</subject><subject>Encrypted image compression</subject><subject>Encryption</subject><subject>Feature extraction</subject><subject>Image coding</subject><subject>Image compression</subject><subject>Image reconstruction</subject><subject>residual dense network</subject><subject>Sampling</subject><subject>spatial attention mechanism</subject><subject>super-resolution reconstruction</subject><issn>1520-9210</issn><issn>1941-0077</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kNFPwjAQxhujiYi-m_jSxOdh263r-kgmKglgIvC8rOsNhmyd7abBv94SiE933-X33eU-hO4pGVFK5NNqPh8xwtgopMJreYEGVEY0IESIS99zRgLJKLlGN87tCKERJ2KA2jFemG_Y40lT2EPbVaYJVltogplx7hCkpm4tOOfHeFlsoQZsSpyavbF4WucbcHjtqmaD0951pq5-QeMPcJXu8z1-hsYBXrZ5V3m1gO7H2M9bdFXmewd35zpE65fJKn0LZu-v03Q8CwomaRdwLhOlFdc6CksGIuSilEpIWYZaxVSCVFoyCDVwVnigLGIAJZViBCRNaDhEj6e9rTVfPbgu25neNv5kxpKYxVEck8RT5EQV1v9rocxaW9W5PWSUZMdcM59rdsw1O-fqLQ8nSwUA_7gUMQ8FCf8Awvx1cg</recordid><startdate>2023</startdate><enddate>2023</enddate><creator>Wang, Chuntao</creator><creator>Zhang, Tianjian</creator><creator>Chen, Hao</creator><creator>Huang, Qiong</creator><creator>Ni, Jiangqun</creator><creator>Zhang, Xinpeng</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-5482-1766</orcidid><orcidid>https://orcid.org/0000-0002-7666-8985</orcidid><orcidid>https://orcid.org/0000-0002-0212-3501</orcidid><orcidid>https://orcid.org/0000-0002-7520-9031</orcidid></search><sort><creationdate>2023</creationdate><title>A Novel Encryption-Then-Lossy-Compression Scheme of Color Images Using Customized Residual Dense Spatial Network</title><author>Wang, Chuntao ; Zhang, Tianjian ; Chen, Hao ; Huang, Qiong ; Ni, Jiangqun ; Zhang, Xinpeng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c291t-5598bdb5dd43f2e7357f9b799f3db619e9bd92e3de52cf2efc6eeb9bb20e91813</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Cloud computing</topic><topic>Color</topic><topic>Color imagery</topic><topic>Constraints</topic><topic>Cryptography</topic><topic>Customization</topic><topic>Deep learning</topic><topic>Encrypted image compression</topic><topic>Encryption</topic><topic>Feature extraction</topic><topic>Image coding</topic><topic>Image compression</topic><topic>Image reconstruction</topic><topic>residual dense network</topic><topic>Sampling</topic><topic>spatial attention mechanism</topic><topic>super-resolution reconstruction</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Chuntao</creatorcontrib><creatorcontrib>Zhang, Tianjian</creatorcontrib><creatorcontrib>Chen, Hao</creatorcontrib><creatorcontrib>Huang, Qiong</creatorcontrib><creatorcontrib>Ni, Jiangqun</creatorcontrib><creatorcontrib>Zhang, Xinpeng</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on multimedia</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang, Chuntao</au><au>Zhang, Tianjian</au><au>Chen, Hao</au><au>Huang, Qiong</au><au>Ni, Jiangqun</au><au>Zhang, Xinpeng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Novel Encryption-Then-Lossy-Compression Scheme of Color Images Using Customized Residual Dense Spatial Network</atitle><jtitle>IEEE transactions on multimedia</jtitle><stitle>TMM</stitle><date>2023</date><risdate>2023</risdate><volume>25</volume><spage>4026</spage><epage>4040</epage><pages>4026-4040</pages><issn>1520-9210</issn><eissn>1941-0077</eissn><coden>ITMUF8</coden><abstract>Nowadays it has still remained as a big challenge to efficiently compress color images in the encrypted domain. In this paper we present a novel deep-learning-based approach to encryption-then-lossy-compression (ETC) of color images by incorporating the domain knowledge of the encrypted image reconstruction process. In specific, a simple yet effective uniform down-sampling is utilized for lossy compression of images encrypted with a modulo-256 addition, and the task of image reconstruction from an encrypted down-sampled image is then formulated as a problem of constrained super-resolution (SR) reconstruction. A customized residual dense spatial network (RDSN) is proposed to solve the formulated constrained SR task by taking advantage of spatial attention mechanism (SAM), global skip connection (GSC), and uniform down-sampling constraint (UDC) that is specific to an ETC system. Extensive experimental results show that the proposed ETC scheme achieves significant performance improvement compared with other state-of-the-art ETC methods, indicating the feasibility and effectiveness of the proposed deep-learning based ETC scheme.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/TMM.2022.3171099</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-5482-1766</orcidid><orcidid>https://orcid.org/0000-0002-7666-8985</orcidid><orcidid>https://orcid.org/0000-0002-0212-3501</orcidid><orcidid>https://orcid.org/0000-0002-7520-9031</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1520-9210 |
| ispartof | IEEE transactions on multimedia, 2023, Vol.25, p.4026-4040 |
| issn | 1520-9210 1941-0077 |
| language | eng |
| recordid | cdi_crossref_primary_10_1109_TMM_2022_3171099 |
| source | IEEE Electronic Library (IEL) |
| subjects | Cloud computing Color Color imagery Constraints Cryptography Customization Deep learning Encrypted image compression Encryption Feature extraction Image coding Image compression Image reconstruction residual dense network Sampling spatial attention mechanism super-resolution reconstruction |
| title | A Novel Encryption-Then-Lossy-Compression Scheme of Color Images Using Customized Residual Dense Spatial Network |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-18T12%3A56%3A16IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Novel%20Encryption-Then-Lossy-Compression%20Scheme%20of%20Color%20Images%20Using%20Customized%20Residual%20Dense%20Spatial%20Network&rft.jtitle=IEEE%20transactions%20on%20multimedia&rft.au=Wang,%20Chuntao&rft.date=2023&rft.volume=25&rft.spage=4026&rft.epage=4040&rft.pages=4026-4040&rft.issn=1520-9210&rft.eissn=1941-0077&rft.coden=ITMUF8&rft_id=info:doi/10.1109/TMM.2022.3171099&rft_dat=%3Cproquest_RIE%3E2862646608%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2862646608&rft_id=info:pmid/&rft_ieee_id=9765370&rfr_iscdi=true |