RAID-Database: human Responses to Affine Image Distortions
Image quality databases are used to train models for predicting subjective human perception. However, most existing databases focus on distortions commonly found in digital media and not in natural conditions. Affine transformations are particularly relevant to study, as they are among the most comm...
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | Daudén-Oliver, Paula Agost-Beltran, David Sansano-Sansano, Emilio Laparra, Valero Malo, Jesús Martínez-Garcia, Marina |
| description | Image quality databases are used to train models for predicting subjective
human perception. However, most existing databases focus on distortions
commonly found in digital media and not in natural conditions. Affine
transformations are particularly relevant to study, as they are among the most
commonly encountered by human observers in everyday life. This Data Descriptor
presents a set of human responses to suprathreshold affine image transforms
(rotation, translation, scaling) and Gaussian noise as convenient reference to
compare with previously existing image quality databases. The responses were
measured using well established psychophysics: the Maximum Likelihood
Difference Scaling method. The set contains responses to 864 distorted images.
The experiments involved 105 observers and more than 20000 comparisons of
quadruples of images. The quality of the dataset is ensured because (a) it
reproduces the classical Pi\'eron's law, (b) it reproduces classical absolute
detection thresholds, and (c) it is consistent with conventional image quality
databases but improves them according to Group-MAD experiments. |
| doi_str_mv | 10.48550/arxiv.2412.10211 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2412_10211</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2412_10211</sourcerecordid><originalsourceid>FETCH-arxiv_primary_2412_102113</originalsourceid><addsrcrecordid>eNpjYJA0NNAzsTA1NdBPLKrILNMzMjE00jM0MDI05GSwCnL0dNF1SSxJTEosTrVSyCjNTcxTCEotLsjPK04tVijJV3BMS8vMS1XwzE1MT1VwySwuyS8qyQTK8jCwpiXmFKfyQmluBnk31xBnD12wLfEFRZm5iUWV8SDb4sG2GRNWAQANQjOA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>RAID-Database: human Responses to Affine Image Distortions</title><source>arXiv.org</source><creator>Daudén-Oliver, Paula ; Agost-Beltran, David ; Sansano-Sansano, Emilio ; Laparra, Valero ; Malo, Jesús ; Martínez-Garcia, Marina</creator><creatorcontrib>Daudén-Oliver, Paula ; Agost-Beltran, David ; Sansano-Sansano, Emilio ; Laparra, Valero ; Malo, Jesús ; Martínez-Garcia, Marina</creatorcontrib><description>Image quality databases are used to train models for predicting subjective
human perception. However, most existing databases focus on distortions
commonly found in digital media and not in natural conditions. Affine
transformations are particularly relevant to study, as they are among the most
commonly encountered by human observers in everyday life. This Data Descriptor
presents a set of human responses to suprathreshold affine image transforms
(rotation, translation, scaling) and Gaussian noise as convenient reference to
compare with previously existing image quality databases. The responses were
measured using well established psychophysics: the Maximum Likelihood
Difference Scaling method. The set contains responses to 864 distorted images.
The experiments involved 105 observers and more than 20000 comparisons of
quadruples of images. The quality of the dataset is ensured because (a) it
reproduces the classical Pi\'eron's law, (b) it reproduces classical absolute
detection thresholds, and (c) it is consistent with conventional image quality
databases but improves them according to Group-MAD experiments.</description><identifier>DOI: 10.48550/arxiv.2412.10211</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition ; Quantitative Biology - Neurons and Cognition ; Quantitative Biology - Quantitative Methods</subject><creationdate>2024-12</creationdate><rights>http://creativecommons.org/licenses/by/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/2412.10211$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2412.10211$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Daudén-Oliver, Paula</creatorcontrib><creatorcontrib>Agost-Beltran, David</creatorcontrib><creatorcontrib>Sansano-Sansano, Emilio</creatorcontrib><creatorcontrib>Laparra, Valero</creatorcontrib><creatorcontrib>Malo, Jesús</creatorcontrib><creatorcontrib>Martínez-Garcia, Marina</creatorcontrib><title>RAID-Database: human Responses to Affine Image Distortions</title><description>Image quality databases are used to train models for predicting subjective
human perception. However, most existing databases focus on distortions
commonly found in digital media and not in natural conditions. Affine
transformations are particularly relevant to study, as they are among the most
commonly encountered by human observers in everyday life. This Data Descriptor
presents a set of human responses to suprathreshold affine image transforms
(rotation, translation, scaling) and Gaussian noise as convenient reference to
compare with previously existing image quality databases. The responses were
measured using well established psychophysics: the Maximum Likelihood
Difference Scaling method. The set contains responses to 864 distorted images.
The experiments involved 105 observers and more than 20000 comparisons of
quadruples of images. The quality of the dataset is ensured because (a) it
reproduces the classical Pi\'eron's law, (b) it reproduces classical absolute
detection thresholds, and (c) it is consistent with conventional image quality
databases but improves them according to Group-MAD experiments.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><subject>Quantitative Biology - Neurons and Cognition</subject><subject>Quantitative Biology - Quantitative Methods</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNpjYJA0NNAzsTA1NdBPLKrILNMzMjE00jM0MDI05GSwCnL0dNF1SSxJTEosTrVSyCjNTcxTCEotLsjPK04tVijJV3BMS8vMS1XwzE1MT1VwySwuyS8qyQTK8jCwpiXmFKfyQmluBnk31xBnD12wLfEFRZm5iUWV8SDb4sG2GRNWAQANQjOA</recordid><startdate>20241213</startdate><enddate>20241213</enddate><creator>Daudén-Oliver, Paula</creator><creator>Agost-Beltran, David</creator><creator>Sansano-Sansano, Emilio</creator><creator>Laparra, Valero</creator><creator>Malo, Jesús</creator><creator>Martínez-Garcia, Marina</creator><scope>AKY</scope><scope>ALC</scope><scope>GOX</scope></search><sort><creationdate>20241213</creationdate><title>RAID-Database: human Responses to Affine Image Distortions</title><author>Daudén-Oliver, Paula ; Agost-Beltran, David ; Sansano-Sansano, Emilio ; Laparra, Valero ; Malo, Jesús ; Martínez-Garcia, Marina</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2412_102113</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><topic>Quantitative Biology - Neurons and Cognition</topic><topic>Quantitative Biology - Quantitative Methods</topic><toplevel>online_resources</toplevel><creatorcontrib>Daudén-Oliver, Paula</creatorcontrib><creatorcontrib>Agost-Beltran, David</creatorcontrib><creatorcontrib>Sansano-Sansano, Emilio</creatorcontrib><creatorcontrib>Laparra, Valero</creatorcontrib><creatorcontrib>Malo, Jesús</creatorcontrib><creatorcontrib>Martínez-Garcia, Marina</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv Quantitative Biology</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Daudén-Oliver, Paula</au><au>Agost-Beltran, David</au><au>Sansano-Sansano, Emilio</au><au>Laparra, Valero</au><au>Malo, Jesús</au><au>Martínez-Garcia, Marina</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>RAID-Database: human Responses to Affine Image Distortions</atitle><date>2024-12-13</date><risdate>2024</risdate><abstract>Image quality databases are used to train models for predicting subjective
human perception. However, most existing databases focus on distortions
commonly found in digital media and not in natural conditions. Affine
transformations are particularly relevant to study, as they are among the most
commonly encountered by human observers in everyday life. This Data Descriptor
presents a set of human responses to suprathreshold affine image transforms
(rotation, translation, scaling) and Gaussian noise as convenient reference to
compare with previously existing image quality databases. The responses were
measured using well established psychophysics: the Maximum Likelihood
Difference Scaling method. The set contains responses to 864 distorted images.
The experiments involved 105 observers and more than 20000 comparisons of
quadruples of images. The quality of the dataset is ensured because (a) it
reproduces the classical Pi\'eron's law, (b) it reproduces classical absolute
detection thresholds, and (c) it is consistent with conventional image quality
databases but improves them according to Group-MAD experiments.</abstract><doi>10.48550/arxiv.2412.10211</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.48550/arxiv.2412.10211 |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_arxiv_primary_2412_10211 |
| source | arXiv.org |
| subjects | Computer Science - Computer Vision and Pattern Recognition Quantitative Biology - Neurons and Cognition Quantitative Biology - Quantitative Methods |
| title | RAID-Database: human Responses to Affine Image Distortions |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-25T02%3A31%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=RAID-Database:%20human%20Responses%20to%20Affine%20Image%20Distortions&rft.au=Daud%C3%A9n-Oliver,%20Paula&rft.date=2024-12-13&rft_id=info:doi/10.48550/arxiv.2412.10211&rft_dat=%3Carxiv_GOX%3E2412_10211%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |