{"id":"https://openalex.org/W2903706147","doi":"https://doi.org/10.1109/ickii.2018.8569207","title":"Saliency-Guided Deep Framework for Power Consumption Suppressing on Mobile Devices","display_name":"Saliency-Guided Deep Framework for Power Consumption Suppressing on Mobile Devices","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2903706147","doi":"https://doi.org/10.1109/ickii.2018.8569207","mag":"2903706147"},"language":"en","primary_location":{"id":"doi:10.1109/ickii.2018.8569207","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ickii.2018.8569207","pdf_url":null,"source":{"id":"https://openalex.org/S4306498242","display_name":"2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII)","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":"2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII)","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/A5101849856","display_name":"Jing Su","orcid":"https://orcid.org/0000-0003-0572-9194"},"institutions":[{"id":"https://openalex.org/I99908691","display_name":"Yuan Ze University","ror":"https://ror.org/01fv1ds98","country_code":"TW","type":"education","lineage":["https://openalex.org/I99908691"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Jing Su","raw_affiliation_strings":["Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan","institution_ids":["https://openalex.org/I99908691"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104229637","display_name":"Yi\u2010Chi Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I99908691","display_name":"Yuan Ze University","ror":"https://ror.org/01fv1ds98","country_code":"TW","type":"education","lineage":["https://openalex.org/I99908691"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Yi-Chi Huang","raw_affiliation_strings":["Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan","institution_ids":["https://openalex.org/I99908691"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079784316","display_name":"Jia-Li Yin","orcid":"https://orcid.org/0000-0002-8087-9769"},"institutions":[{"id":"https://openalex.org/I99908691","display_name":"Yuan Ze University","ror":"https://ror.org/01fv1ds98","country_code":"TW","type":"education","lineage":["https://openalex.org/I99908691"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jia-Li Yin","raw_affiliation_strings":["Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan","institution_ids":["https://openalex.org/I99908691"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003990275","display_name":"Bo\u2010Hao Chen","orcid":"https://orcid.org/0000-0002-7999-3396"},"institutions":[{"id":"https://openalex.org/I99908691","display_name":"Yuan Ze University","ror":"https://ror.org/01fv1ds98","country_code":"TW","type":"education","lineage":["https://openalex.org/I99908691"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Bo-Hao Chen","raw_affiliation_strings":["Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Yuan Ze University, Taoyuan, Taiwan","institution_ids":["https://openalex.org/I99908691"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033953625","display_name":"Shenming Qu","orcid":"https://orcid.org/0000-0001-7393-6203"},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shenming Qu","raw_affiliation_strings":["Institute of Intelligent Network System, School of Software, Henan University, Kaifeng, China"],"affiliations":[{"raw_affiliation_string":"Institute of Intelligent Network System, School of Software, Henan University, Kaifeng, China","institution_ids":["https://openalex.org/I173899330"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101849856"],"corresponding_institution_ids":["https://openalex.org/I99908691"],"apc_list":null,"apc_paid":null,"fwci":0.1206,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.48094512,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"191","last_page":"194"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11165","display_name":"Image and Video Quality Assessment","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9990000128746033,"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"}},{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9936000108718872,"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/computer-science","display_name":"Computer science","score":0.7651828527450562},{"id":"https://openalex.org/keywords/mobile-device","display_name":"Mobile device","score":0.6833136081695557},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6730153560638428},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.6163839101791382},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.5931543707847595},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.591191828250885},{"id":"https://openalex.org/keywords/power-consumption","display_name":"Power consumption","score":0.5737077593803406},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5653043389320374},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.5455788969993591},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5152654647827148},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.46474167704582214}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7651828527450562},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.6833136081695557},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6730153560638428},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6163839101791382},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.5931543707847595},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.591191828250885},{"id":"https://openalex.org/C2984118289","wikidata":"https://www.wikidata.org/wiki/Q29954","display_name":"Power consumption","level":3,"score":0.5737077593803406},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5653043389320374},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.5455788969993591},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5152654647827148},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.46474167704582214},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ickii.2018.8569207","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ickii.2018.8569207","pdf_url":null,"source":{"id":"https://openalex.org/S4306498242","display_name":"2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII)","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":"2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.8899999856948853,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W2039284295","https://openalex.org/W2063556243","https://openalex.org/W2293078015","https://openalex.org/W2339858850","https://openalex.org/W2342876882","https://openalex.org/W2343447635","https://openalex.org/W2406876270","https://openalex.org/W2604969152","https://openalex.org/W2724198442","https://openalex.org/W2769277030","https://openalex.org/W2964121744","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6739804131","https://openalex.org/W6746073370"],"related_works":["https://openalex.org/W2329500892","https://openalex.org/W28991112","https://openalex.org/W2370726991","https://openalex.org/W2369710579","https://openalex.org/W4327728159","https://openalex.org/W4394266730","https://openalex.org/W1990856605","https://openalex.org/W2053783616","https://openalex.org/W2545348020","https://openalex.org/W2912751582"],"abstract_inverted_index":{"With":[0],"the":[1,19,40,50,72,87,105,109,128,149],"growing":[2],"concern":[3],"for":[4,52,70],"power-hungry":[5],"on":[6,65,86],"mobile":[7,20,53],"devices,":[8],"many":[9],"power":[10,33,73,131,150],"constrained":[11,34],"contrast":[12,35,61,156],"enhancement":[13,36,62,157],"algorithms":[14,37],"have":[15],"been":[16],"developed":[17],"in":[18],"devices":[21],"embedded":[22],"with":[23,115],"emissive":[24],"displays,":[25],"such":[26],"as":[27,45],"organic":[28],"light-emitting":[29],"diodes.":[30],"However,":[31],"conventional":[32,154],"inevitably":[38],"degrade":[39,122],"visual":[41,100,143],"aesthetics":[42,144],"of":[43,75,92,130,145],"images":[44,146],"a":[46,58,66,93,116],"trade-off":[47],"to":[48,98,126,141],"gain":[49],"power-saving":[51],"devices.":[54],"This":[55],"paper":[56],"proposes":[57],"trainable":[59],"power-constrained":[60,155],"algorithm":[63,84,138],"based":[64],"saliency-guided":[67],"deep":[68,117],"framework":[69],"suppressing":[71],"consumption":[74,151],"an":[76],"image":[77,95,107],"while":[78,147],"preserving":[79],"its":[80],"perceptual":[81],"quality.":[82],"Our":[83],"relies":[85],"fact":[88],"that":[89,136],"imaging":[90,110],"features":[91,111,114,125],"displayed":[94],"is":[96,139],"salient":[97],"human":[99],"perception.":[101],"Hence,":[102],"we":[103],"decompose":[104],"input":[106],"into":[108],"and":[112,121],"textual":[113,124],"convolutional":[118],"neural":[119],"networks,":[120],"those":[123],"achieve":[127],"suppression":[129],"consumption.":[132],"Experimental":[133],"results":[134],"demonstrate":[135],"our":[137],"able":[140],"maintain":[142],"reducing":[148],"effectively,":[152],"outperforming":[153],"algorithms.":[158]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}