{"id":"https://openalex.org/W7155173274","doi":"https://doi.org/10.48550/arxiv.2604.19159","title":"MSDS: Deep Structural Similarity with Multiscale Representation","display_name":"MSDS: Deep Structural Similarity with Multiscale Representation","publication_year":2026,"publication_date":"2026-04-21","ids":{"openalex":"https://openalex.org/W7155173274","doi":"https://doi.org/10.48550/arxiv.2604.19159"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.19159","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19159","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.19159","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073979670","display_name":"Dengjie Kang","orcid":"https://orcid.org/0009-0001-4188-6114"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kang, Danling","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134304252","display_name":"Xue-Hua Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xue-Hua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134337045","display_name":"Bin Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Bin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100781981","display_name":"Keke Zhang","orcid":"https://orcid.org/0000-0002-9789-6151"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Keke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123855890","display_name":"Weiling Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Weiling","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134258319","display_name":"Tiesong Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Tiesong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.7513999938964844,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.7513999938964844,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.19949999451637268,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12650","display_name":"Aesthetic Perception and Analysis","score":0.0066999997943639755,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.7355999946594238},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6833000183105469},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6148999929428101},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6122999787330627},{"id":"https://openalex.org/keywords/structural-similarity","display_name":"Structural similarity","score":0.5710999965667725},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5450999736785889},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5306000113487244},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49219998717308044}],"concepts":[{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.7355999946594238},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7206000089645386},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6833000183105469},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6148999929428101},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6122999787330627},{"id":"https://openalex.org/C139489369","wikidata":"https://www.wikidata.org/wiki/Q770846","display_name":"Structural similarity","level":2,"score":0.5710999965667725},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5647000074386597},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5450999736785889},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5306000113487244},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49219998717308044},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4880000054836273},{"id":"https://openalex.org/C143271835","wikidata":"https://www.wikidata.org/wiki/Q254515","display_name":"Similitude","level":2,"score":0.3970000147819519},{"id":"https://openalex.org/C144386022","wikidata":"https://www.wikidata.org/wiki/Q1332997","display_name":"Scale factor (cosmology)","level":5,"score":0.3846000134944916},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.3569999933242798},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.35679998993873596},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3264000117778778},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.27570000290870667},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.27480000257492065},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2630000114440918},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26249998807907104}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.19159","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19159","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.19159","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.19159","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.7452359199523926,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Deep-feature-based":[0],"perceptual":[1,141],"similarity":[2,31,45],"models":[3],"have":[4],"demonstrated":[5],"strong":[6],"alignment":[7],"with":[8,75,101],"human":[9],"visual":[10],"perception":[11],"in":[12,43,139],"Image":[13],"Quality":[14],"Assessment":[15],"(IQA).":[16],"However,":[17],"most":[18],"existing":[19],"approaches":[20],"operate":[21],"at":[22,32],"a":[23,33,63,102,136,146],"single":[24],"spatial":[25,41,56,133],"scale,":[26],"implicitly":[27],"assuming":[28],"that":[29],"structural":[30],"fixed":[34],"resolution":[35],"is":[36],"sufficient.":[37],"The":[38,79,129],"role":[39],"of":[40,67,105],"scale":[42,57,134],"deep-feature":[44],"modeling":[46],"thus":[47],"remains":[48],"insufficiently":[49],"understood.":[50],"In":[51],"this":[52],"letter,":[53],"we":[54],"isolate":[55],"as":[58,71,135],"an":[59],"independent":[60],"factor":[61,138],"using":[62],"minimal":[64,147],"multiscale":[65],"extension":[66],"DeepSSIM,":[68],"referred":[69],"to":[70],"Deep":[72],"Structural":[73],"Similarity":[74],"Multiscale":[76],"Representation":[77],"(MSDS).":[78],"proposed":[80],"framework":[81],"decouples":[82],"deep":[83,140],"feature":[84],"representation":[85],"from":[86],"cross-scale":[87],"integration":[88],"by":[89],"computing":[90],"DeepSSIM":[91],"independently":[92],"across":[93],"pyramid":[94],"levels":[95],"and":[96,116],"fusing":[97],"the":[98,121],"resulting":[99],"scores":[100],"lightweight":[103],"set":[104],"learnable":[106],"global":[107],"weights.":[108],"Experiments":[109],"on":[110],"multiple":[111],"benchmark":[112],"datasets":[113],"demonstrate":[114],"consistent":[115],"statistically":[117],"significant":[118],"improvements":[119],"over":[120],"single-scale":[122],"baseline,":[123],"while":[124],"introducing":[125],"negligible":[126],"additional":[127],"complexity.":[128],"results":[130],"empirically":[131],"confirm":[132],"non-negligible":[137],"similarity,":[142],"isolated":[143],"here":[144],"via":[145],"testbed.":[148]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-23T00:00:00"}
