Deep Learning-Based Picture-Wise Just Noticeable Distortion Prediction Model for Image Compression
Picture Wise Just Noticeable Difference (PW-JND), which accounts for the minimum difference of a picture that human visual system can perceive, can be widely used in perception-oriented image and video processing. However, the conventional Just Noticeable Difference (JND) models calculate the JND th...
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| Veröffentlicht in: | IEEE transactions on image processing 2020-01, Vol.29, p.641-656 |
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| creator | Liu, Huanhua Zhang, Yun Zhang, Huan Fan, Chunling Kwong, Sam Kuo, C.-C. Jay Fan, Xiaoping |
| description | Picture Wise Just Noticeable Difference (PW-JND), which accounts for the minimum difference of a picture that human visual system can perceive, can be widely used in perception-oriented image and video processing. However, the conventional Just Noticeable Difference (JND) models calculate the JND threshold for each pixel or sub-band separately, which may not reflect the total masking effect of a picture accurately. In this paper, we propose a deep learning based PW-JND prediction model for image compression. Firstly, we formulate the task of predicting PW-JND as a multi-class classification problem, and propose a framework to transform the multi-class classification problem to a binary classification problem solved by just one binary classifier. Secondly, we construct a deep learning based binary classifier named perceptually lossy/lossless predictor which can predict whether an image is perceptually lossy to another or not. Finally, we propose a sliding window based search strategy to predict PW-JND based on the prediction results of the perceptually lossy/lossless predictor. Experimental results show that the mean accuracy of the perceptually lossy/lossless predictor reaches 92%, and the absolute prediction error of the proposed PW-JND model is 0.79 dB on average, which show the superiority of the proposed PW-JND model to the conventional JND models. |
| doi_str_mv | 10.1109/TIP.2019.2933743 |
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Jay ; Fan, Xiaoping</creator><creatorcontrib>Liu, Huanhua ; Zhang, Yun ; Zhang, Huan ; Fan, Chunling ; Kwong, Sam ; Kuo, C.-C. Jay ; Fan, Xiaoping</creatorcontrib><description>Picture Wise Just Noticeable Difference (PW-JND), which accounts for the minimum difference of a picture that human visual system can perceive, can be widely used in perception-oriented image and video processing. However, the conventional Just Noticeable Difference (JND) models calculate the JND threshold for each pixel or sub-band separately, which may not reflect the total masking effect of a picture accurately. In this paper, we propose a deep learning based PW-JND prediction model for image compression. Firstly, we formulate the task of predicting PW-JND as a multi-class classification problem, and propose a framework to transform the multi-class classification problem to a binary classification problem solved by just one binary classifier. Secondly, we construct a deep learning based binary classifier named perceptually lossy/lossless predictor which can predict whether an image is perceptually lossy to another or not. Finally, we propose a sliding window based search strategy to predict PW-JND based on the prediction results of the perceptually lossy/lossless predictor. Experimental results show that the mean accuracy of the perceptually lossy/lossless predictor reaches 92%, and the absolute prediction error of the proposed PW-JND model is 0.79 dB on average, which show the superiority of the proposed PW-JND model to the conventional JND models.</description><identifier>ISSN: 1057-7149</identifier><identifier>EISSN: 1941-0042</identifier><identifier>DOI: 10.1109/TIP.2019.2933743</identifier><identifier>PMID: 31425033</identifier><identifier>CODEN: IIPRE4</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Adaptation models ; Bit rate ; Classification ; Classifiers ; convolutional neural network ; Deep learning ; Distortion ; Image coding ; Image compression ; Image processing ; image quality assessment ; Just noticeable distortion ; Machine learning ; Masking ; Prediction models ; Predictive models ; Streaming media ; Video ; Visual perception ; Visualization</subject><ispartof>IEEE transactions on image processing, 2020-01, Vol.29, p.641-656</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c394t-57fe34bd4009244dd20cf2dbd7ab8b4a784a511b31be39044118a39710d542a53</citedby><cites>FETCH-LOGICAL-c394t-57fe34bd4009244dd20cf2dbd7ab8b4a784a511b31be39044118a39710d542a53</cites><orcidid>0000-0001-9474-5035 ; 0000-0002-5507-4985 ; 0000-0001-7484-7261 ; 0000-0003-0172-4070 ; 0000-0001-9457-7801</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8796396$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8796396$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31425033$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Huanhua</creatorcontrib><creatorcontrib>Zhang, Yun</creatorcontrib><creatorcontrib>Zhang, Huan</creatorcontrib><creatorcontrib>Fan, Chunling</creatorcontrib><creatorcontrib>Kwong, Sam</creatorcontrib><creatorcontrib>Kuo, C.-C. Jay</creatorcontrib><creatorcontrib>Fan, Xiaoping</creatorcontrib><title>Deep Learning-Based Picture-Wise Just Noticeable Distortion Prediction Model for Image Compression</title><title>IEEE transactions on image processing</title><addtitle>TIP</addtitle><addtitle>IEEE Trans Image Process</addtitle><description>Picture Wise Just Noticeable Difference (PW-JND), which accounts for the minimum difference of a picture that human visual system can perceive, can be widely used in perception-oriented image and video processing. However, the conventional Just Noticeable Difference (JND) models calculate the JND threshold for each pixel or sub-band separately, which may not reflect the total masking effect of a picture accurately. In this paper, we propose a deep learning based PW-JND prediction model for image compression. Firstly, we formulate the task of predicting PW-JND as a multi-class classification problem, and propose a framework to transform the multi-class classification problem to a binary classification problem solved by just one binary classifier. Secondly, we construct a deep learning based binary classifier named perceptually lossy/lossless predictor which can predict whether an image is perceptually lossy to another or not. Finally, we propose a sliding window based search strategy to predict PW-JND based on the prediction results of the perceptually lossy/lossless predictor. Experimental results show that the mean accuracy of the perceptually lossy/lossless predictor reaches 92%, and the absolute prediction error of the proposed PW-JND model is 0.79 dB on average, which show the superiority of the proposed PW-JND model to the conventional JND models.</description><subject>Adaptation models</subject><subject>Bit rate</subject><subject>Classification</subject><subject>Classifiers</subject><subject>convolutional neural network</subject><subject>Deep learning</subject><subject>Distortion</subject><subject>Image coding</subject><subject>Image compression</subject><subject>Image processing</subject><subject>image quality assessment</subject><subject>Just noticeable distortion</subject><subject>Machine learning</subject><subject>Masking</subject><subject>Prediction models</subject><subject>Predictive models</subject><subject>Streaming media</subject><subject>Video</subject><subject>Visual perception</subject><subject>Visualization</subject><issn>1057-7149</issn><issn>1941-0042</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE1r3DAQhkVoyVd7DxSKoJdcvNVI8so6tpuPbtm2e0jJUUjWOCjY1layD_331bKbHAoDM_A-MwwPIVfAFgBMf35YbxecgV5wLYSS4oScg5ZQMSb5mzKzWlUKpD4jFzk_MwayhuUpORMgec2EOCfuBnFHN2jTGMan6qvN6Ok2tNOcsHoMGen3OU_0Z5xCi9b1SG9CnmKaQhzpNqEv6H78ET32tIuJrgf7hHQVh13CnEv2jrztbJ_x_bFfkt93tw-rb9Xm1_169WVTtULLqapVh0I6LxnTXErvOWs77p1X1jVOWtVIWwM4AQ6FZlICNFZoBczXkttaXJLrw91din9mzJMZQm6x7-2Icc6GC7HUDTS8Kein_9DnOKexfGc4141iimsoFDtQbYo5J-zMLoXBpr8GmNn7N8W_2fs3R_9l5ePx8OwG9K8LL8IL8OEABER8jRull6LUP7tliAA</recordid><startdate>20200101</startdate><enddate>20200101</enddate><creator>Liu, Huanhua</creator><creator>Zhang, Yun</creator><creator>Zhang, Huan</creator><creator>Fan, Chunling</creator><creator>Kwong, Sam</creator><creator>Kuo, C.-C. Jay</creator><creator>Fan, Xiaoping</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>NPM</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><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-9474-5035</orcidid><orcidid>https://orcid.org/0000-0002-5507-4985</orcidid><orcidid>https://orcid.org/0000-0001-7484-7261</orcidid><orcidid>https://orcid.org/0000-0003-0172-4070</orcidid><orcidid>https://orcid.org/0000-0001-9457-7801</orcidid></search><sort><creationdate>20200101</creationdate><title>Deep Learning-Based Picture-Wise Just Noticeable Distortion Prediction Model for Image Compression</title><author>Liu, Huanhua ; Zhang, Yun ; Zhang, Huan ; Fan, Chunling ; Kwong, Sam ; Kuo, C.-C. Jay ; Fan, Xiaoping</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c394t-57fe34bd4009244dd20cf2dbd7ab8b4a784a511b31be39044118a39710d542a53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adaptation models</topic><topic>Bit rate</topic><topic>Classification</topic><topic>Classifiers</topic><topic>convolutional neural network</topic><topic>Deep learning</topic><topic>Distortion</topic><topic>Image coding</topic><topic>Image compression</topic><topic>Image processing</topic><topic>image quality assessment</topic><topic>Just noticeable distortion</topic><topic>Machine learning</topic><topic>Masking</topic><topic>Prediction models</topic><topic>Predictive models</topic><topic>Streaming media</topic><topic>Video</topic><topic>Visual perception</topic><topic>Visualization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Huanhua</creatorcontrib><creatorcontrib>Zhang, Yun</creatorcontrib><creatorcontrib>Zhang, Huan</creatorcontrib><creatorcontrib>Fan, Chunling</creatorcontrib><creatorcontrib>Kwong, Sam</creatorcontrib><creatorcontrib>Kuo, C.-C. 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Jay</au><au>Fan, Xiaoping</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep Learning-Based Picture-Wise Just Noticeable Distortion Prediction Model for Image Compression</atitle><jtitle>IEEE transactions on image processing</jtitle><stitle>TIP</stitle><addtitle>IEEE Trans Image Process</addtitle><date>2020-01-01</date><risdate>2020</risdate><volume>29</volume><spage>641</spage><epage>656</epage><pages>641-656</pages><issn>1057-7149</issn><eissn>1941-0042</eissn><coden>IIPRE4</coden><abstract>Picture Wise Just Noticeable Difference (PW-JND), which accounts for the minimum difference of a picture that human visual system can perceive, can be widely used in perception-oriented image and video processing. However, the conventional Just Noticeable Difference (JND) models calculate the JND threshold for each pixel or sub-band separately, which may not reflect the total masking effect of a picture accurately. In this paper, we propose a deep learning based PW-JND prediction model for image compression. Firstly, we formulate the task of predicting PW-JND as a multi-class classification problem, and propose a framework to transform the multi-class classification problem to a binary classification problem solved by just one binary classifier. Secondly, we construct a deep learning based binary classifier named perceptually lossy/lossless predictor which can predict whether an image is perceptually lossy to another or not. Finally, we propose a sliding window based search strategy to predict PW-JND based on the prediction results of the perceptually lossy/lossless predictor. Experimental results show that the mean accuracy of the perceptually lossy/lossless predictor reaches 92%, and the absolute prediction error of the proposed PW-JND model is 0.79 dB on average, which show the superiority of the proposed PW-JND model to the conventional JND models.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>31425033</pmid><doi>10.1109/TIP.2019.2933743</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0001-9474-5035</orcidid><orcidid>https://orcid.org/0000-0002-5507-4985</orcidid><orcidid>https://orcid.org/0000-0001-7484-7261</orcidid><orcidid>https://orcid.org/0000-0003-0172-4070</orcidid><orcidid>https://orcid.org/0000-0001-9457-7801</orcidid></addata></record> |
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| subjects | Adaptation models Bit rate Classification Classifiers convolutional neural network Deep learning Distortion Image coding Image compression Image processing image quality assessment Just noticeable distortion Machine learning Masking Prediction models Predictive models Streaming media Video Visual perception Visualization |
| title | Deep Learning-Based Picture-Wise Just Noticeable Distortion Prediction Model for Image Compression |
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