{"id":"https://openalex.org/W2985588731","doi":"https://doi.org/10.4018/ijcvip.2019100103","title":"An Efficient Motion Vector Recovery and Reconstruction Method for Spatiotemporal Video Error Concealment","display_name":"An Efficient Motion Vector Recovery and Reconstruction Method for Spatiotemporal Video Error Concealment","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2985588731","doi":"https://doi.org/10.4018/ijcvip.2019100103","mag":"2985588731"},"language":"en","primary_location":{"id":"doi:10.4018/ijcvip.2019100103","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijcvip.2019100103","pdf_url":null,"source":{"id":"https://openalex.org/S4210177549","display_name":"International Journal of Computer Vision and Image Processing","issn_l":"2155-6989","issn":["2155-6989","2155-6997"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computer Vision and Image Processing","raw_type":"journal-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/A5029017522","display_name":"Ansari Vaqar Ahmed","orcid":"https://orcid.org/0000-0003-0841-9554"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ansari Vaqar Ahmed","raw_affiliation_strings":["Electronics and Telecommunication Engineering Department, St. Francis Institute of Technology, Mumbai, India"],"affiliations":[{"raw_affiliation_string":"Electronics and Telecommunication Engineering Department, St. Francis Institute of Technology, Mumbai, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050636879","display_name":"Uday Pandit Khot","orcid":"https://orcid.org/0000-0003-1315-3471"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Uday Pandit Khot","raw_affiliation_strings":["Electronics and Telecommunication Engineering Department, St. Francis Institute of Technology, Mumbai, India"],"affiliations":[{"raw_affiliation_string":"Electronics and Telecommunication Engineering Department, St. Francis Institute of Technology, Mumbai, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5029017522"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12906591,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"9","issue":"4","first_page":"28","last_page":"48"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10741","display_name":"Video Coding and Compression Technologies","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10741","display_name":"Video Coding and Compression Technologies","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.9991000294685364,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9990000128746033,"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/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.8934706449508667},{"id":"https://openalex.org/keywords/motion-vector","display_name":"Motion vector","score":0.7962449789047241},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6811118721961975},{"id":"https://openalex.org/keywords/euclidean-distance","display_name":"Euclidean distance","score":0.6154964566230774},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5748441815376282},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5207464694976807},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.48883146047592163},{"id":"https://openalex.org/keywords/standard-deviation","display_name":"Standard deviation","score":0.45750191807746887},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.431590735912323},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4171105623245239},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32367920875549316},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1507754623889923},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14219814538955688}],"concepts":[{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.8934706449508667},{"id":"https://openalex.org/C2779020251","wikidata":"https://www.wikidata.org/wiki/Q3555171","display_name":"Motion vector","level":3,"score":0.7962449789047241},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6811118721961975},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.6154964566230774},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5748441815376282},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5207464694976807},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.48883146047592163},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.45750191807746887},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.431590735912323},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4171105623245239},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32367920875549316},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1507754623889923},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14219814538955688}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.4018/ijcvip.2019100103","is_oa":false,"landing_page_url":"https://doi.org/10.4018/ijcvip.2019100103","pdf_url":null,"source":{"id":"https://openalex.org/S4210177549","display_name":"International Journal of Computer Vision and Image Processing","issn_l":"2155-6989","issn":["2155-6989","2155-6997"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320424","host_organization_name":"IGI Global","host_organization_lineage":["https://openalex.org/P4310320424"],"host_organization_lineage_names":["IGI Global"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computer Vision and Image Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.44999998807907104,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1544519864","https://openalex.org/W1983257115","https://openalex.org/W2022430420","https://openalex.org/W2024754883","https://openalex.org/W2041745141","https://openalex.org/W2052849618","https://openalex.org/W2099677950","https://openalex.org/W2141879129","https://openalex.org/W2142017078","https://openalex.org/W2152146613","https://openalex.org/W2155917804","https://openalex.org/W2312693555","https://openalex.org/W2492281453","https://openalex.org/W2791463507"],"related_works":["https://openalex.org/W2169159254","https://openalex.org/W3174657038","https://openalex.org/W1992732116","https://openalex.org/W2067578116","https://openalex.org/W2902494752","https://openalex.org/W2592471899","https://openalex.org/W2382061536","https://openalex.org/W4390673421","https://openalex.org/W4310043907","https://openalex.org/W149647761"],"abstract_inverted_index":{"In":[0],"this":[1],"article,":[2],"an":[3],"efficient":[4],"spatiotemporal":[5],"video":[6],"error":[7],"concealment":[8],"(EC)":[9],"based":[10,92,122,178],"on":[11,93,123],"motion":[12,26,71,160],"vector":[13,27,72],"(MV)":[14],"recovery":[15,126],"and":[16,51,104,130,182,199,218,232,236,244],"a":[17],"pixel":[18,114,118],"reconstruction":[19,119],"(PR)":[20],"method":[21,127,187],"is":[22,31,77,140,145,221],"proposed.":[23,141],"The":[24],"pixel-based":[25,70],"with":[28,73,168,180],"partition":[29,74],"(PMVP)":[30],"modified":[32,69,116],"by":[33,80,85,100,185,228,240],"using":[34,86,128,175],"Mahalanobis":[35,132,142],"distance":[36,41,133,143,154],"(MD)":[37],"rather":[38],"than":[39],"Euclidean":[40],"(ED)":[42],"for":[43,62,188],"recovering":[44],"MVs,":[45],"as":[46,194,203],"MD":[47,58],"uses":[48],"standard":[49],"deviation":[50],"covariance":[52],"of":[53,89,96,107,137,214],"available":[54,90,108,135],"pixels.":[55],"Further,":[56],"the":[57,94],"gives":[59],"more":[60],"accuracy":[61],"non-square":[63],"cluster":[64],"compared":[65,151,167,225,237],"to":[66,152,157,226,238],"ED.":[67],"This":[68],"(MPMVP)":[75],"algorithm":[76,121],"further":[78],"upgrade":[79],"two":[81],"different":[82],"strategies.":[83],"First,":[84],"voting":[87,111],"priority":[88],"MVs":[91,109],"probabilities":[95],"similar":[97],"directions.":[98],"Second,":[99],"considering":[101],"separate":[102],"horizontal":[103],"vertical":[105],"directions":[106],"in":[110,212],"priority.":[112],"For":[113,209],"reconstruction,":[115],"spiral":[117],"(MSPR)":[120],"directional":[124],"edge":[125],"minimum":[129],"maximum":[131],"from":[134],"pixels":[136],"surrounding":[138],"MBs":[139],"approach":[144,156],"most":[146],"optimized":[147],"similarity":[148],"measure":[149],"technique":[150],"other":[153],"measurement":[155],"obtained":[158],"lost":[159],"vectors.":[161],"These":[162],"proposed":[163],"EC":[164,170,174,179],"techniques":[165,171],"are":[166],"existing":[169],"like,":[172],"SPR":[173,239],"ED,":[176,181],"PMVP":[177,227],"MV":[183],"Interpolation":[184],"Zhou's":[186],"various":[189],"packet":[190],"loss":[191],"rates":[192],"(PLRs)":[193],"3%,":[195,215],"7%,":[196,216],"16%,":[197],"20%":[198],"quantization":[200],"parameters":[201],"(QPs)":[202],"20,":[204],"24,":[205],"28,":[206],"32,":[207],"36.":[208],"total":[210],"average":[211],"PLR":[213],"16%":[217],"20%,":[219],"MSPR":[220],"having":[222],"better":[223],"PSNR":[224],"2.516,":[229],"2.29,":[230],"2.06":[231],"2.02":[233],"dB,":[234,246],"respectively;":[235],"0.796,":[241],"0.718,":[242],"0.643":[243],"0.631":[245],"respectively.":[247]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}