{"created":"2025-01-19T01:18:16.802265+00:00","updated":"2025-01-19T15:21:27.979061+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00217852","sets":["1164:4619:10826:10925"]},"path":["10925"],"owner":"44499","recid":"217852","title":["動画超解像における学習画像と復元画像の知覚的品質と画質との関係性"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-05-05"},"_buckets":{"deposit":"47bc3bc9-b65b-47cb-8cff-a71155431c43"},"_deposit":{"id":"217852","pid":{"type":"depid","value":"217852","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"動画超解像における学習画像と復元画像の知覚的品質と画質との関係性","author_link":["565200","565199","565202","565201"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"動画超解像における学習画像と復元画像の知覚的品質と画質との関係性"},{"subitem_title":"Relationship between Perceptual and Image Qualities of Training and Reconstruction Images in Video Super-Resolution","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"一般講演セッション1 ","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2022-05-05","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"豊田工業大学"},{"subitem_text_value":"豊田工業大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Toyota Technological Institute","subitem_text_language":"en"},{"subitem_text_value":"Toyota Technological Institute","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/217852/files/IPSJ-CVIM22230039.pdf","label":"IPSJ-CVIM22230039.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CVIM22230039.pdf","filesize":[{"value":"3.1 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"20"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"05ff1f9a-9e10-4972-aa4f-9f333b65d95f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"森, 啓"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"浮田, 宗伯"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Hroshi, Mori","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Norimichi, Ukita","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11131797","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8701","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"超解像は低解像度画像を高解像度画像に変換する技術である．近年の研究では，L1 損失や L2 損失といった再構成誤差に加え，敵対的損失や VGG 損失といった追加の損失関数を用いてモデルを学習することで，人間の知覚的により良好な超解像が可能であることが示されている．このような知覚的な超解像は，画像超解像では盛んに研究されているが，動画超解像においてはあまり研究されていない．本研究では，このような知覚的な動画超解像において，学習データが動画超解像の知覚的品質へ及ぼす影響を解明することを目的とする．具体的には，学習データを知覚的品質でクラスタリングして学習し，得られた超解像モデルの性能を解析することで，その関係性を明らかにする．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Super resolution is a technique for converting low-resolution images into high-resolution images. Recent research has shown that human perceptually better super resolution is possible by training models with additional loss functions such as adversarial loss and VGG loss, in addition to reconstruction errors such as L1 and L2 loss. Such perceptual super resolution has been extensively studied in image super resolution, but less so in video super resolution. The purpose of this study is to elucidate the effect of training data on the perceptual quality of video super resolution in such perceptual video super resolution. Specifically, we will learn by clustering training data by various indices, analyze the performance of the resulting super resolution models, and clarify the relationship between them.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告コンピュータビジョンとイメージメディア（CVIM）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-05-05","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"39","bibliographicVolumeNumber":"2022-CVIM-230"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":217852,"links":{}}