{"id":"https://openalex.org/W4308234017","doi":"https://doi.org/10.1109/icip46576.2022.9897294","title":"P-Frame Coding with Generalized Difference: A Novel Conditional Coding Approach","display_name":"P-Frame Coding with Generalized Difference: A Novel Conditional Coding Approach","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4308234017","doi":"https://doi.org/10.1109/icip46576.2022.9897294"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9897294","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897294","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","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/A5053003385","display_name":"Fabian Brand","orcid":"https://orcid.org/0000-0002-2022-1033"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fabian Brand","raw_affiliation_strings":["Friedrich-Alexander-Universit&#x00E4;t Erlangen-N&#x00FC;rnberg,Multimedia Communications and Signal Processing,Erlangen,91058"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Friedrich-Alexander-Universit&#x00E4;t Erlangen-N&#x00FC;rnberg,Multimedia Communications and Signal Processing,Erlangen,91058","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101633542","display_name":"J\u00fcrgen Seiler","orcid":"https://orcid.org/0000-0002-3016-110X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jurgen Seiler","raw_affiliation_strings":["Friedrich-Alexander-Universit&#x00E4;t Erlangen-N&#x00FC;rnberg,Multimedia Communications and Signal Processing,Erlangen,91058"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Friedrich-Alexander-Universit&#x00E4;t Erlangen-N&#x00FC;rnberg,Multimedia Communications and Signal Processing,Erlangen,91058","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5062850220","display_name":"Andr\u00e9 Kaup","orcid":"https://orcid.org/0000-0002-0929-5074"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andre Kaup","raw_affiliation_strings":["Friedrich-Alexander-Universit&#x00E4;t Erlangen-N&#x00FC;rnberg,Multimedia Communications and Signal Processing,Erlangen,91058"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Friedrich-Alexander-Universit&#x00E4;t Erlangen-N&#x00FC;rnberg,Multimedia Communications and Signal Processing,Erlangen,91058","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2949,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.63576347,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1266","last_page":"1270"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9998999834060669,"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":0.9998999834060669,"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/T10741","display_name":"Video Coding and Compression Technologies","score":0.9998999834060669,"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/T10901","display_name":"Advanced Data Compression Techniques","score":0.9998000264167786,"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/coding-tree-unit","display_name":"Coding tree unit","score":0.6934019923210144},{"id":"https://openalex.org/keywords/context-adaptive-binary-arithmetic-coding","display_name":"Context-adaptive binary arithmetic coding","score":0.6910550594329834},{"id":"https://openalex.org/keywords/variable-length-code","display_name":"Variable-length code","score":0.6294630169868469},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.607345461845398},{"id":"https://openalex.org/keywords/shannon\u2013fano-coding","display_name":"Shannon\u2013Fano coding","score":0.5931557416915894},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.581501305103302},{"id":"https://openalex.org/keywords/context-adaptive-variable-length-coding","display_name":"Context-adaptive variable-length coding","score":0.5782718658447266},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5401633381843567},{"id":"https://openalex.org/keywords/coding-gain","display_name":"Coding gain","score":0.5106232762336731},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.47264546155929565},{"id":"https://openalex.org/keywords/sub-band-coding","display_name":"Sub-band coding","score":0.46510300040245056},{"id":"https://openalex.org/keywords/tunstall-coding","display_name":"Tunstall coding","score":0.4587973952293396},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.45640337467193604},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44055622816085815},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.4054602384567261},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.32808148860931396},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.32703161239624023},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2995712459087372},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.25285184383392334},{"id":"https://openalex.org/keywords/speech-coding","display_name":"Speech coding","score":0.23845472931861877},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09192213416099548}],"concepts":[{"id":"https://openalex.org/C190750250","wikidata":"https://www.wikidata.org/wiki/Q13533439","display_name":"Coding tree unit","level":3,"score":0.6934019923210144},{"id":"https://openalex.org/C175732694","wikidata":"https://www.wikidata.org/wiki/Q1128713","display_name":"Context-adaptive binary arithmetic coding","level":3,"score":0.6910550594329834},{"id":"https://openalex.org/C60603091","wikidata":"https://www.wikidata.org/wiki/Q2981616","display_name":"Variable-length code","level":3,"score":0.6294630169868469},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.607345461845398},{"id":"https://openalex.org/C130811719","wikidata":"https://www.wikidata.org/wiki/Q2645","display_name":"Shannon\u2013Fano coding","level":4,"score":0.5931557416915894},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.581501305103302},{"id":"https://openalex.org/C135534801","wikidata":"https://www.wikidata.org/wiki/Q1128721","display_name":"Context-adaptive variable-length coding","level":4,"score":0.5782718658447266},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5401633381843567},{"id":"https://openalex.org/C76862118","wikidata":"https://www.wikidata.org/wiki/Q1105671","display_name":"Coding gain","level":3,"score":0.5106232762336731},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.47264546155929565},{"id":"https://openalex.org/C98526533","wikidata":"https://www.wikidata.org/wiki/Q1691938","display_name":"Sub-band coding","level":3,"score":0.46510300040245056},{"id":"https://openalex.org/C73231260","wikidata":"https://www.wikidata.org/wiki/Q7853376","display_name":"Tunstall coding","level":4,"score":0.4587973952293396},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.45640337467193604},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44055622816085815},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.4054602384567261},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.32808148860931396},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.32703161239624023},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2995712459087372},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.25285184383392334},{"id":"https://openalex.org/C13895895","wikidata":"https://www.wikidata.org/wiki/Q3270773","display_name":"Speech coding","level":2,"score":0.23845472931861877},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09192213416099548}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9897294","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9897294","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2178928294","https://openalex.org/W2548527721","https://openalex.org/W2552465432","https://openalex.org/W2785562966","https://openalex.org/W2949361041","https://openalex.org/W2969260367","https://openalex.org/W2999868813","https://openalex.org/W3018065762","https://openalex.org/W3035195755","https://openalex.org/W3045658096","https://openalex.org/W3047936379","https://openalex.org/W3108139283","https://openalex.org/W3110286842","https://openalex.org/W3134368609","https://openalex.org/W6631190155","https://openalex.org/W6754634825","https://openalex.org/W6793902750"],"related_works":["https://openalex.org/W2148686647","https://openalex.org/W4312628177","https://openalex.org/W2123072960","https://openalex.org/W1579872668","https://openalex.org/W2213686625","https://openalex.org/W1587903609","https://openalex.org/W2668751350","https://openalex.org/W3083947873","https://openalex.org/W2083809258","https://openalex.org/W2116344231"],"abstract_inverted_index":{"Motion":[0],"compensated":[1],"inter":[2],"frame":[3],"prediction":[4],"is":[5,76],"a":[6,42,60,77,81,87],"common":[7],"component":[8],"of":[9,21,80],"all":[10],"video":[11,26],"coders":[12],"and":[13,25,49,70,85,104,118],"greatly":[14],"reduces":[15],"temporal":[16],"redundancy.":[17],"With":[18],"the":[19,67,110,128],"rise":[20],"deep":[22],"learning-based":[23],"image":[24],"compression,":[27],"this":[28,56],"concept":[29],"has":[30,86],"been":[31],"successfully":[32],"taken":[33],"over":[34],"from":[35],"traditional":[36,46],"coding":[37,48,63,114],"approaches.":[38],"These":[39],"approaches":[40],"offer":[41],"larger":[43],"flexibility":[44],"than":[45],"transform":[47],"therefore":[50],"enable":[51],"efficient":[52],"conditional":[53,62,83,103,119],"coding.":[54,106],"In":[55],"work,":[57],"we":[58],"develop":[59],"novel":[61],"approach":[64,75],"based":[65],"on":[66],"generalized":[68,71,112],"difference":[69,113],"sum":[72],"operators.":[73],"This":[74],"special":[78],"case":[79],"general":[82],"coder":[84],"very":[88],"small":[89],"complexity":[90],"overhead.":[91],"We":[92,107],"also":[93],"propose":[94],"an":[95],"extension":[96],"which":[97],"enables":[98],"dynamic":[99],"content-adaptive":[100],"switching":[101],"between":[102],"residual":[105,117],"show":[108],"that":[109],"extended":[111],"outperforms":[115],"both":[116],"coding,":[120],"saving":[121],"27.8%":[122],"Bj\u00f8ntegaard":[123],"delta":[124],"rate":[125],"compared":[126],"to":[127],"former.":[129]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
