Image Scrambling Degree Evaluation Algorithm Based on Grey Relation Analysis
In order to objectively and automatically evaluate the degree of digital image scrambling, introduce the grey relation analysis theory, the paper proposes a new evaluation method of image scrambling. In the method, the definition and the feature of the ideal scrambling image are analyzed first, whos...
Gespeichert in:
Hauptverfasser: | , |
---|---|
Format: | Tagungsbericht |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 514 |
---|---|
container_issue | |
container_start_page | 511 |
container_title | |
container_volume | |
creator | Tan Yongjie Zhou Wengang |
description | In order to objectively and automatically evaluate the degree of digital image scrambling, introduce the grey relation analysis theory, the paper proposes a new evaluation method of image scrambling. In the method, the definition and the feature of the ideal scrambling image are analyzed first, whose histogram is summarized at the same time. And then, the scrambling image is divided into some sub-images to construct some histogram sequences, and make these sequences be small samples sequences. Finally the gray relevancy of every two sequences using gray relation analysis is calculated to evaluate the image scrambling degree. Two kinds of experimental results indicate that compared with the method based SNR, the proposed method is not only efficient, flexible, running without the origin image involved, but also can provide with some conclusions which are consistent with the perception of human visual system. |
doi_str_mv | 10.1109/ICCIS.2010.131 |
format | Conference Proceeding |
fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5709136</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5709136</ieee_id><sourcerecordid>5709136</sourcerecordid><originalsourceid>FETCH-LOGICAL-i90t-6b089c10895284e89a8908e1e164ab8797a43c158b638c1bb444455b6658805d3</originalsourceid><addsrcrecordid>eNotjE9Lw0AUxFdEUGuuXrzsF0jdl-yft8ca2xoICLb3sps-40qSyiYK-fZG7O8wwwzDMHYPYgkg7GNZFOVumYm_nMMFS6xBYbRVMjNCX7JbkJmUiCDhmiXD8ClmVGak1jesKjvXEN_V0XW-DX3Dn6mJRHz949pvN4ZTz1dtc4ph_Oj4kxvoyOdqG2nib9SeB71rpyEMd-zq3bUDJWdfsP1mvS9e0up1WxarKg1WjKn2Am0Ns6gMJaF1aAUSEGjpPBprnMxrUOh1jjV4L2eU8lorRKGO-YI9_N8GIjp8xdC5OB2UERZynf8CSLZMCw</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Image Scrambling Degree Evaluation Algorithm Based on Grey Relation Analysis</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Tan Yongjie ; Zhou Wengang</creator><creatorcontrib>Tan Yongjie ; Zhou Wengang</creatorcontrib><description>In order to objectively and automatically evaluate the degree of digital image scrambling, introduce the grey relation analysis theory, the paper proposes a new evaluation method of image scrambling. In the method, the definition and the feature of the ideal scrambling image are analyzed first, whose histogram is summarized at the same time. And then, the scrambling image is divided into some sub-images to construct some histogram sequences, and make these sequences be small samples sequences. Finally the gray relevancy of every two sequences using gray relation analysis is calculated to evaluate the image scrambling degree. Two kinds of experimental results indicate that compared with the method based SNR, the proposed method is not only efficient, flexible, running without the origin image involved, but also can provide with some conclusions which are consistent with the perception of human visual system.</description><identifier>ISBN: 1424488141</identifier><identifier>ISBN: 9781424488148</identifier><identifier>EISBN: 9780769542706</identifier><identifier>EISBN: 0769542700</identifier><identifier>DOI: 10.1109/ICCIS.2010.131</identifier><language>eng</language><publisher>IEEE</publisher><subject>Algorithm design and analysis ; Correlation ; Grey Relation Analysis(GRA) ; histogram ; Histograms ; Humans ; image scrambling ; Pixel ; scrambling degree ; Signal to noise ratio ; Signal-to-Noise Ratio (SNR) ; Transforms</subject><ispartof>2010 International Conference on Computational and Information Sciences, 2010, p.511-514</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5709136$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5709136$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Tan Yongjie</creatorcontrib><creatorcontrib>Zhou Wengang</creatorcontrib><title>Image Scrambling Degree Evaluation Algorithm Based on Grey Relation Analysis</title><title>2010 International Conference on Computational and Information Sciences</title><addtitle>ICCIS</addtitle><description>In order to objectively and automatically evaluate the degree of digital image scrambling, introduce the grey relation analysis theory, the paper proposes a new evaluation method of image scrambling. In the method, the definition and the feature of the ideal scrambling image are analyzed first, whose histogram is summarized at the same time. And then, the scrambling image is divided into some sub-images to construct some histogram sequences, and make these sequences be small samples sequences. Finally the gray relevancy of every two sequences using gray relation analysis is calculated to evaluate the image scrambling degree. Two kinds of experimental results indicate that compared with the method based SNR, the proposed method is not only efficient, flexible, running without the origin image involved, but also can provide with some conclusions which are consistent with the perception of human visual system.</description><subject>Algorithm design and analysis</subject><subject>Correlation</subject><subject>Grey Relation Analysis(GRA)</subject><subject>histogram</subject><subject>Histograms</subject><subject>Humans</subject><subject>image scrambling</subject><subject>Pixel</subject><subject>scrambling degree</subject><subject>Signal to noise ratio</subject><subject>Signal-to-Noise Ratio (SNR)</subject><subject>Transforms</subject><isbn>1424488141</isbn><isbn>9781424488148</isbn><isbn>9780769542706</isbn><isbn>0769542700</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjE9Lw0AUxFdEUGuuXrzsF0jdl-yft8ca2xoICLb3sps-40qSyiYK-fZG7O8wwwzDMHYPYgkg7GNZFOVumYm_nMMFS6xBYbRVMjNCX7JbkJmUiCDhmiXD8ClmVGak1jesKjvXEN_V0XW-DX3Dn6mJRHz949pvN4ZTz1dtc4ph_Oj4kxvoyOdqG2nib9SeB71rpyEMd-zq3bUDJWdfsP1mvS9e0up1WxarKg1WjKn2Am0Ns6gMJaF1aAUSEGjpPBprnMxrUOh1jjV4L2eU8lorRKGO-YI9_N8GIjp8xdC5OB2UERZynf8CSLZMCw</recordid><startdate>201012</startdate><enddate>201012</enddate><creator>Tan Yongjie</creator><creator>Zhou Wengang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201012</creationdate><title>Image Scrambling Degree Evaluation Algorithm Based on Grey Relation Analysis</title><author>Tan Yongjie ; Zhou Wengang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-6b089c10895284e89a8908e1e164ab8797a43c158b638c1bb444455b6658805d3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Algorithm design and analysis</topic><topic>Correlation</topic><topic>Grey Relation Analysis(GRA)</topic><topic>histogram</topic><topic>Histograms</topic><topic>Humans</topic><topic>image scrambling</topic><topic>Pixel</topic><topic>scrambling degree</topic><topic>Signal to noise ratio</topic><topic>Signal-to-Noise Ratio (SNR)</topic><topic>Transforms</topic><toplevel>online_resources</toplevel><creatorcontrib>Tan Yongjie</creatorcontrib><creatorcontrib>Zhou Wengang</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Tan Yongjie</au><au>Zhou Wengang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Image Scrambling Degree Evaluation Algorithm Based on Grey Relation Analysis</atitle><btitle>2010 International Conference on Computational and Information Sciences</btitle><stitle>ICCIS</stitle><date>2010-12</date><risdate>2010</risdate><spage>511</spage><epage>514</epage><pages>511-514</pages><isbn>1424488141</isbn><isbn>9781424488148</isbn><eisbn>9780769542706</eisbn><eisbn>0769542700</eisbn><abstract>In order to objectively and automatically evaluate the degree of digital image scrambling, introduce the grey relation analysis theory, the paper proposes a new evaluation method of image scrambling. In the method, the definition and the feature of the ideal scrambling image are analyzed first, whose histogram is summarized at the same time. And then, the scrambling image is divided into some sub-images to construct some histogram sequences, and make these sequences be small samples sequences. Finally the gray relevancy of every two sequences using gray relation analysis is calculated to evaluate the image scrambling degree. Two kinds of experimental results indicate that compared with the method based SNR, the proposed method is not only efficient, flexible, running without the origin image involved, but also can provide with some conclusions which are consistent with the perception of human visual system.</abstract><pub>IEEE</pub><doi>10.1109/ICCIS.2010.131</doi><tpages>4</tpages></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | ISBN: 1424488141 |
ispartof | 2010 International Conference on Computational and Information Sciences, 2010, p.511-514 |
issn | |
language | eng |
recordid | cdi_ieee_primary_5709136 |
source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Algorithm design and analysis Correlation Grey Relation Analysis(GRA) histogram Histograms Humans image scrambling Pixel scrambling degree Signal to noise ratio Signal-to-Noise Ratio (SNR) Transforms |
title | Image Scrambling Degree Evaluation Algorithm Based on Grey Relation Analysis |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T09%3A28%3A33IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Image%20Scrambling%20Degree%20Evaluation%20Algorithm%20Based%20on%20Grey%20Relation%20Analysis&rft.btitle=2010%20International%20Conference%20on%20Computational%20and%20Information%20Sciences&rft.au=Tan%20Yongjie&rft.date=2010-12&rft.spage=511&rft.epage=514&rft.pages=511-514&rft.isbn=1424488141&rft.isbn_list=9781424488148&rft_id=info:doi/10.1109/ICCIS.2010.131&rft_dat=%3Cieee_6IE%3E5709136%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9780769542706&rft.eisbn_list=0769542700&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5709136&rfr_iscdi=true |