Image quality prediction using synthetic and natural codebooks: comparative results
We investigate a model for image/video quality assessment based on building a set of codevectors representing in a sense some basic properties of images, similar to well-known CORNIA model. We analyze the codebook building method and propose some modifications for it. Also the algorithm is investiga...
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| creator | Koroteev, Maxim Aistov, Kirill Berezovskiy, Valeriy Frolov, Pavel |
| description | We investigate a model for image/video quality assessment based on building a
set of codevectors representing in a sense some basic properties of images,
similar to well-known CORNIA model. We analyze the codebook building method and
propose some modifications for it. Also the algorithm is investigated from the
point of inference time reduction. Both natural and synthetic images are used
for building codebooks and some analysis of synthetic images used for codebooks
is provided. It is demonstrated the results on quality assessment may be
improves with the use if synthetic images for codebook construction. We also
demonstrate regimes of the algorithm in which real time execution on CPU is
possible for sufficiently high correlations with mean opinion score (MOS).
Various pooling strategies are considered as well as the problem of metric
sensitivity to bitrate. |
| doi_str_mv | 10.48550/arxiv.2212.10319 |
| format | Article |
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set of codevectors representing in a sense some basic properties of images,
similar to well-known CORNIA model. We analyze the codebook building method and
propose some modifications for it. Also the algorithm is investigated from the
point of inference time reduction. Both natural and synthetic images are used
for building codebooks and some analysis of synthetic images used for codebooks
is provided. It is demonstrated the results on quality assessment may be
improves with the use if synthetic images for codebook construction. We also
demonstrate regimes of the algorithm in which real time execution on CPU is
possible for sufficiently high correlations with mean opinion score (MOS).
Various pooling strategies are considered as well as the problem of metric
sensitivity to bitrate.</description><identifier>DOI: 10.48550/arxiv.2212.10319</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition</subject><creationdate>2022-12</creationdate><rights>http://creativecommons.org/licenses/by-sa/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2212.10319$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2212.10319$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Koroteev, Maxim</creatorcontrib><creatorcontrib>Aistov, Kirill</creatorcontrib><creatorcontrib>Berezovskiy, Valeriy</creatorcontrib><creatorcontrib>Frolov, Pavel</creatorcontrib><title>Image quality prediction using synthetic and natural codebooks: comparative results</title><description>We investigate a model for image/video quality assessment based on building a
set of codevectors representing in a sense some basic properties of images,
similar to well-known CORNIA model. We analyze the codebook building method and
propose some modifications for it. Also the algorithm is investigated from the
point of inference time reduction. Both natural and synthetic images are used
for building codebooks and some analysis of synthetic images used for codebooks
is provided. It is demonstrated the results on quality assessment may be
improves with the use if synthetic images for codebook construction. We also
demonstrate regimes of the algorithm in which real time execution on CPU is
possible for sufficiently high correlations with mean opinion score (MOS).
Various pooling strategies are considered as well as the problem of metric
sensitivity to bitrate.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz7tOwzAYhmEvDKhwAUz4BhJ8iOOYDVUcKlVioHv02_ldLHLCdipy90Bh-t7pkx5Cbjgrq0YpdgfxK5xKIbgoOZPcXJK33QBHpJ8L9CGvdI7YBZfDNNIlhfFI0zrmd8zBURg7OkJeIvTUTR3aafpI9z85zBAhhxPSiGnpc7oiFx76hNf_uyGHp8fD9qXYvz7vtg_7AmptCueFaYQUEqTlqI2peIO10M4z5MAV49Jg5dEiV7b2qEEr0JbZhnWm8rXckNu_27OqnWMYIK7tr6496-Q3C19L1w</recordid><startdate>20221220</startdate><enddate>20221220</enddate><creator>Koroteev, Maxim</creator><creator>Aistov, Kirill</creator><creator>Berezovskiy, Valeriy</creator><creator>Frolov, Pavel</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20221220</creationdate><title>Image quality prediction using synthetic and natural codebooks: comparative results</title><author>Koroteev, Maxim ; Aistov, Kirill ; Berezovskiy, Valeriy ; Frolov, Pavel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a679-cf2982323a3b1e799418e627cf0e1a150139e4febe15b6fe7a75a7b0b80d94f63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Koroteev, Maxim</creatorcontrib><creatorcontrib>Aistov, Kirill</creatorcontrib><creatorcontrib>Berezovskiy, Valeriy</creatorcontrib><creatorcontrib>Frolov, Pavel</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Koroteev, Maxim</au><au>Aistov, Kirill</au><au>Berezovskiy, Valeriy</au><au>Frolov, Pavel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Image quality prediction using synthetic and natural codebooks: comparative results</atitle><date>2022-12-20</date><risdate>2022</risdate><abstract>We investigate a model for image/video quality assessment based on building a
set of codevectors representing in a sense some basic properties of images,
similar to well-known CORNIA model. We analyze the codebook building method and
propose some modifications for it. Also the algorithm is investigated from the
point of inference time reduction. Both natural and synthetic images are used
for building codebooks and some analysis of synthetic images used for codebooks
is provided. It is demonstrated the results on quality assessment may be
improves with the use if synthetic images for codebook construction. We also
demonstrate regimes of the algorithm in which real time execution on CPU is
possible for sufficiently high correlations with mean opinion score (MOS).
Various pooling strategies are considered as well as the problem of metric
sensitivity to bitrate.</abstract><doi>10.48550/arxiv.2212.10319</doi><oa>free_for_read</oa></addata></record> |
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| recordid | cdi_arxiv_primary_2212_10319 |
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| subjects | Computer Science - Computer Vision and Pattern Recognition |
| title | Image quality prediction using synthetic and natural codebooks: comparative results |
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