{"id":"https://openalex.org/W4409367298","doi":"https://doi.org/10.1609/aaai.v39i8.32897","title":"Achieving Lightweight Super-Resolution for Real-Time Computer Graphics","display_name":"Achieving Lightweight Super-Resolution for Real-Time Computer Graphics","publication_year":2025,"publication_date":"2025-04-11","ids":{"openalex":"https://openalex.org/W4409367298","doi":"https://doi.org/10.1609/aaai.v39i8.32897"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v39i8.32897","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v39i8.32897","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/32897/35052","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/32897/35052","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101696131","display_name":"Wen Yu","orcid":"https://orcid.org/0000-0002-6923-3158"},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yu Wen","raw_affiliation_strings":["University of Houston"],"affiliations":[{"raw_affiliation_string":"University of Houston","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100374071","display_name":"Chen Zhang","orcid":"https://orcid.org/0000-0001-8556-0186"},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen Zhang","raw_affiliation_strings":["University of Houston"],"affiliations":[{"raw_affiliation_string":"University of Houston","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024508481","display_name":"Chenhao Xie","orcid":"https://orcid.org/0000-0002-1399-0352"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenhao Xie","raw_affiliation_strings":["Beihang University"],"affiliations":[{"raw_affiliation_string":"Beihang University","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113854463","display_name":"Xin Fu","orcid":null},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xin Fu","raw_affiliation_strings":["University of Houston"],"affiliations":[{"raw_affiliation_string":"University of Houston","institution_ids":["https://openalex.org/I44461941"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101696131"],"corresponding_institution_ids":["https://openalex.org/I44461941"],"apc_list":null,"apc_paid":null,"fwci":0.4058,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.54712362,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"39","issue":"8","first_page":"8313","last_page":"8322"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9983999729156494,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9983999729156494,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9916999936103821,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.9897000193595886,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.735817015171051},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.692427933216095},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics","score":0.5683228969573975},{"id":"https://openalex.org/keywords/graphics","display_name":"Graphics","score":0.5620194673538208}],"concepts":[{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.735817015171051},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.692427933216095},{"id":"https://openalex.org/C77660652","wikidata":"https://www.wikidata.org/wiki/Q150971","display_name":"Computer graphics","level":2,"score":0.5683228969573975},{"id":"https://openalex.org/C21442007","wikidata":"https://www.wikidata.org/wiki/Q1027879","display_name":"Graphics","level":2,"score":0.5620194673538208}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v39i8.32897","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v39i8.32897","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/32897/35052","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v39i8.32897","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v39i8.32897","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/32897/35052","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G113617909","display_name":"SHF: Small: Enabling On-Device Bayesian Neural Network Training via An Integrated Architecture-System Approach","funder_award_id":"2130688","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G2471282032","display_name":null,"funder_award_id":"CCF-2130688","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6781623049","display_name":null,"funder_award_id":"CNS-2107057","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4409367298.pdf","grobid_xml":"https://content.openalex.org/works/W4409367298.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4234077872","https://openalex.org/W2066703180","https://openalex.org/W856338413","https://openalex.org/W2319989118","https://openalex.org/W82290398","https://openalex.org/W2381296300","https://openalex.org/W74297911","https://openalex.org/W4245723629","https://openalex.org/W1503820821","https://openalex.org/W86491366"],"abstract_inverted_index":{"Image":[0],"super-resolution":[1],"(SR)":[2],"is":[3,153],"essential":[4],"for":[5,98],"bridging":[6],"the":[7,53,65,120,126,180],"gap":[8],"between":[9],"modern":[10],"hardware":[11],"and":[12,51,61,68,111,131,156,198],"real-time":[13,100],"computer":[14],"graphics":[15],"(CG)":[16],"applications.":[17],"It":[18,113],"reduces":[19,193],"CG":[20],"workload":[21],"by":[22,82],"allowing":[23],"low-resolution":[24],"rendering,":[25],"with":[26],"original":[27],"quality":[28,205],"restored":[29],"later":[30],"via":[31],"mathematical":[32],"operations":[33],"or":[34],"machine":[35],"learning.":[36],"However,":[37],"recent":[38],"learning-based":[39],"SR":[40,66,143,204,209],"methods":[41],"often":[42],"rely":[43],"on":[44,89,183],"complex":[45],"models,":[46],"demanding":[47],"high":[48,203],"computational":[49],"resources":[50],"undermining":[52],"benefits":[54],"of":[55,64,133],"reduced":[56],"rendering":[57,69,74,104,117,185],"workload.":[58],"Our":[59],"qualitative":[60],"quantitative":[62],"analysis":[63],"process":[67],"reveals":[70],"that":[71,190],"readily":[72],"accessible":[73],"information":[75,105,118],"can":[76],"significantly":[77,192],"enhance":[78],"neural":[79,163],"network":[80,109,144,152,181],"design":[81],"serving":[83],"as":[84],"additional":[85],"features.":[86],"To":[87,167],"capitalize":[88],"this,":[90],"we":[91],"propose":[92],"CGSR,":[93],"an":[94],"optimization":[95],"framework":[96],"designed":[97],"lightweight":[99],"super-resolution.":[101],"CGSR":[102,171,191],"utilizes":[103,114],"to":[106,146],"boost":[107],"both":[108],"extensibility":[110],"efficiency.":[112],"progressively":[115],"available":[116],"from":[119],"pipeline,":[121],"which":[122,177],"arrives":[123],"earlier":[124],"than":[125],"rendered":[127],"frame,":[128],"enabling":[129],"pre-processing":[130],"masking":[132],"latency.":[134],"These":[135],"features":[136],"are":[137],"then":[138],"integrated":[139],"into":[140,158],"a":[141,148,159],"selected":[142],"backbone":[145,208],"form":[147],"CG-enhanced":[149],"network.":[150],"This":[151],"further":[154],"optimized":[155],"refined":[157],"CG-optimized":[160],"version":[161],"using":[162],"architecture":[164],"search":[165],"(NAS).":[166],"improve":[168],"runtime":[169],"performance,":[170],"also":[172],"employs":[173],"rendering-aware":[174],"hybrid":[175],"pruning,":[176],"dynamically":[178],"prunes":[179],"based":[182],"temporal":[184],"data.":[186],"Evaluation":[187],"results":[188],"show":[189],"parameter":[194],"size,":[195],"multi-add":[196],"operations,":[197],"inference":[199],"time":[200],"while":[201],"maintaining":[202],"across":[206],"various":[207],"networks.":[210]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
