{"id":"https://openalex.org/W4290098728","doi":"https://doi.org/10.1145/3524273.3533927","title":"Light field image quality assessment method based on deep graph convolutional neural network","display_name":"Light field image quality assessment method based on deep graph convolutional neural network","publication_year":2022,"publication_date":"2022-06-14","ids":{"openalex":"https://openalex.org/W4290098728","doi":"https://doi.org/10.1145/3524273.3533927"},"language":"en","primary_location":{"id":"doi:10.1145/3524273.3533927","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3524273.3533927","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM Multimedia Systems Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068748275","display_name":"Sana Alamgeer","orcid":"https://orcid.org/0000-0002-6472-7570"},"institutions":[{"id":"https://openalex.org/I150729083","display_name":"Universidade de Bras\u00edlia","ror":"https://ror.org/02xfp8v59","country_code":"BR","type":"education","lineage":["https://openalex.org/I150729083"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Sana Alamgeer","raw_affiliation_strings":["University of Bras\u00edlia, Brazil"],"affiliations":[{"raw_affiliation_string":"University of Bras\u00edlia, Brazil","institution_ids":["https://openalex.org/I150729083"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086454316","display_name":"Muhammad Irshad","orcid":"https://orcid.org/0000-0003-0223-898X"},"institutions":[{"id":"https://openalex.org/I150729083","display_name":"Universidade de Bras\u00edlia","ror":"https://ror.org/02xfp8v59","country_code":"BR","type":"education","lineage":["https://openalex.org/I150729083"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Muhammad Irshad","raw_affiliation_strings":["University of Bras\u00edlia, Brazil"],"affiliations":[{"raw_affiliation_string":"University of Bras\u00edlia, Brazil","institution_ids":["https://openalex.org/I150729083"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022483402","display_name":"Myl\u00e8ne C. Q. Farias","orcid":"https://orcid.org/0000-0002-1957-9943"},"institutions":[{"id":"https://openalex.org/I150729083","display_name":"Universidade de Bras\u00edlia","ror":"https://ror.org/02xfp8v59","country_code":"BR","type":"education","lineage":["https://openalex.org/I150729083"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Myl\u00e8ne C. Q. Farias","raw_affiliation_strings":["University of Bras\u00edlia, Brazil"],"affiliations":[{"raw_affiliation_string":"University of Bras\u00edlia, Brazil","institution_ids":["https://openalex.org/I150729083"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5068748275"],"corresponding_institution_ids":["https://openalex.org/I150729083"],"apc_list":null,"apc_paid":null,"fwci":0.1006,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.3724003,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"357","last_page":"361"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.9991000294685364,"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":0.9991000294685364,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.989300012588501,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7886399030685425},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.77482008934021},{"id":"https://openalex.org/keywords/light-field","display_name":"Light field","score":0.7431490421295166},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7007732391357422},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.647603452205658},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5912764072418213},{"id":"https://openalex.org/keywords/image-quality","display_name":"Image quality","score":0.5685699582099915},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5389507412910461},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5114651918411255},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5109788179397583},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5018119812011719},{"id":"https://openalex.org/keywords/receptive-field","display_name":"Receptive field","score":0.47170376777648926},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.46614161133766174},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.32221266627311707},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13583004474639893},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.12879294157028198}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7886399030685425},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.77482008934021},{"id":"https://openalex.org/C48983235","wikidata":"https://www.wikidata.org/wiki/Q593161","display_name":"Light field","level":2,"score":0.7431490421295166},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7007732391357422},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.647603452205658},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5912764072418213},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.5685699582099915},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5389507412910461},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5114651918411255},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5109788179397583},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5018119812011719},{"id":"https://openalex.org/C19071747","wikidata":"https://www.wikidata.org/wiki/Q1755207","display_name":"Receptive field","level":2,"score":0.47170376777648926},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46614161133766174},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.32221266627311707},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13583004474639893},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.12879294157028198},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3524273.3533927","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3524273.3533927","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 13th ACM Multimedia Systems Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2008857988","https://openalex.org/W2066636486","https://openalex.org/W2753630964","https://openalex.org/W2802884254","https://openalex.org/W2892336178","https://openalex.org/W2900283321","https://openalex.org/W3005391776","https://openalex.org/W3183391416","https://openalex.org/W3188281262"],"related_works":["https://openalex.org/W2089544495","https://openalex.org/W2079003682","https://openalex.org/W1555021777","https://openalex.org/W2913266608","https://openalex.org/W1964918325","https://openalex.org/W2799648451","https://openalex.org/W2189496153","https://openalex.org/W2186491718","https://openalex.org/W2034008118","https://openalex.org/W2055164815"],"abstract_inverted_index":{"This":[0,49],"paper":[1],"contains":[2],"the":[3,13,33,41,75,83,115,162,168,208],"research":[4],"proposal":[5],"of":[6,35,55,62,85,97,118,164,172,180],"Sana":[7],"Alamgeer":[8],"that":[9,21,64,136,176],"was":[10],"presented":[11],"at":[12],"MMSys":[14],"2022":[15],"doctoral":[16],"symposium.":[17],"Unlike":[18],"regular":[19],"images":[20],"represent":[22],"only":[23,149],"light":[24,36,43],"intensities,":[25],"Light":[26,86],"Field":[27,87],"(LF)":[28],"contents":[29],"carry":[30],"information":[31],"about":[32],"intensity":[34],"in":[37,47],"a":[38,52,119,131,178,197],"scene,":[39],"including":[40],"direction":[42],"rays":[44],"are":[45,111],"traveling":[46],"space.":[48],"allows":[50],"for":[51,200,215],"richer":[53],"representation":[54],"our":[56],"world,":[57],"but":[58,159],"requires":[59],"large":[60],"amounts":[61],"data":[63],"need":[65],"to":[66,74,113,121,206],"be":[67],"processed":[68],"and":[69,156,182,196],"compressed":[70],"before":[71],"being":[72],"transmitted":[73],"viewer.":[76],"Since":[77],"these":[78],"techniques":[79],"may":[80],"introduce":[81],"distortions,":[82],"design":[84],"Image":[88],"Quality":[89],"Assessment":[90],"(LF-IQA)":[91],"methods":[92,99],"is":[93,137,170,205],"important.":[94],"The":[95],"majority":[96],"LF-IQA":[98,134],"based":[100,138],"on":[101,139],"traditional":[102],"Convolutional":[103,142],"Neural":[104,143],"Network":[105,144],"(CNN)":[106],"have":[107],"limitations,":[108],"i.e.":[109],"they":[110],"unable":[112],"increase":[114],"receptive":[116],"field":[117],"neuron-pixel":[120],"model":[122],"non-local":[123],"image":[124],"features.":[125],"In":[126],"this":[127],"work,":[128],"we":[129],"propose":[130],"novel":[132],"no-reference":[133],"method":[135,147,169,211],"Deep":[140],"Graph":[141],"(GCNN).":[145],"Our":[146,203],"not":[148],"takes":[150,177],"into":[151],"account":[152],"both":[153],"LF":[154,217],"angular":[155],"spatial":[157],"information,":[158],"also":[160],"learns":[161],"order":[163],"pixel":[165],"information.":[166],"Specifically,":[167],"composed":[171],"one":[173],"input":[174],"layer":[175],"pair":[179],"graphs":[181],"their":[183],"corresponding":[184],"subjective":[185],"quality":[186,201,209],"scores":[187],"as":[188],"labels,":[189],"4":[190],"GCNN":[191],"layers,":[192,195],"fully":[193],"connected":[194],"regression":[198],"block":[199],"prediction.":[202],"aim":[204],"develop":[207],"prediction":[210],"with":[212],"maximum":[213],"accuracy":[214],"distorted":[216],"content.":[218]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
