Edge Computing-Based SAT-Video Coding for Remote Sensing
This paper proposes an edge computing-based video coding implementation on an Earth observation satellite (SAT-video coding), which can encode video using limited resources and the power of mini/microsatellites. SAT-video coding proposes a hardware-related quantization (Q) function, hardware reducti...
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| Veröffentlicht in: | IEEE access 2022, Vol.10, p.52840-52852 |
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| creator | Bui, Trong-An Lee, Pei-Jun Chen, Kuan-Yu Chen, Chia-Ray Liu, Cynthia S. J. Lin, Hsin-Chia |
| description | This paper proposes an edge computing-based video coding implementation on an Earth observation satellite (SAT-video coding), which can encode video using limited resources and the power of mini/microsatellites. SAT-video coding proposes a hardware-related quantization (Q) function, hardware reduction of the motion estimation (ME) method, and simplified entropy coding (EC), which reduces the computation complexity. The hardware-related Q reduces hardware resource and power consumption by 72% and 55%, respectively, compared with traditional Q implementation. The hardware reduction of ME reduces resource use compared with regular ME implementation (59% of lookup tables [LUTs] and 79% of Registers). The total number of LUTs used for the simplified EC function is also much lower than other EC hardware implementations. The SAT-video encoder IP uses fewer hardware resources, and the power consumption is estimated at 0.0894 W at a high working frequency (125 MHz). The SAT-video encoding speed is 18.95 frames per second for 2560\times 2560 video. Therefore, the proposed SAT-video coding is an edge computation suitable for micro/minisatellites. The coding efficiency records the highest compression ratio at 33.8, with a peak signal-to-noise ratio of 34.46 dB. With the important task of designing edge computing based on satellite video encoding, these are adequate values for remote sensing video. |
| doi_str_mv | 10.1109/ACCESS.2022.3174553 |
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J. ; Lin, Hsin-Chia</creator><creatorcontrib>Bui, Trong-An ; Lee, Pei-Jun ; Chen, Kuan-Yu ; Chen, Chia-Ray ; Liu, Cynthia S. J. ; Lin, Hsin-Chia</creatorcontrib><description>This paper proposes an edge computing-based video coding implementation on an Earth observation satellite (SAT-video coding), which can encode video using limited resources and the power of mini/microsatellites. SAT-video coding proposes a hardware-related quantization (Q) function, hardware reduction of the motion estimation (ME) method, and simplified entropy coding (EC), which reduces the computation complexity. The hardware-related Q reduces hardware resource and power consumption by 72% and 55%, respectively, compared with traditional Q implementation. The hardware reduction of ME reduces resource use compared with regular ME implementation (59% of lookup tables [LUTs] and 79% of Registers). The total number of LUTs used for the simplified EC function is also much lower than other EC hardware implementations. The SAT-video encoder IP uses fewer hardware resources, and the power consumption is estimated at 0.0894 W at a high working frequency (125 MHz). The SAT-video encoding speed is 18.95 frames per second for <inline-formula> <tex-math notation="LaTeX">2560\times 2560 </tex-math></inline-formula> video. Therefore, the proposed SAT-video coding is an edge computation suitable for micro/minisatellites. The coding efficiency records the highest compression ratio at 33.8, with a peak signal-to-noise ratio of 34.46 dB. With the important task of designing edge computing based on satellite video encoding, these are adequate values for remote sensing video.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2022.3174553</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Coders ; Coding ; Compression ratio ; Earth observations (from space) ; Edge computing ; Encoding ; Field programmable gate arrays ; Frames per second ; Hardware ; hardware acceleration ; Lookup tables ; Mathematical models ; Microsatellites ; Motion simulation ; Power consumption ; Quantization (signal) ; Remote sensing ; remote sensing video ; satellite ; Satellite observation ; Satellites ; Signal to noise ratio ; Streaming media ; Video compression</subject><ispartof>IEEE access, 2022, Vol.10, p.52840-52852</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-d83cf1fd50ba13cc2d8fcd5cf1bd3bf720de5883eae81c39629d02c3705a90eb3</citedby><cites>FETCH-LOGICAL-c408t-d83cf1fd50ba13cc2d8fcd5cf1bd3bf720de5883eae81c39629d02c3705a90eb3</cites><orcidid>0000-0001-7660-4060</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9773309$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,2095,4009,27612,27902,27903,27904,54912</link.rule.ids></links><search><creatorcontrib>Bui, Trong-An</creatorcontrib><creatorcontrib>Lee, Pei-Jun</creatorcontrib><creatorcontrib>Chen, Kuan-Yu</creatorcontrib><creatorcontrib>Chen, Chia-Ray</creatorcontrib><creatorcontrib>Liu, Cynthia S. J.</creatorcontrib><creatorcontrib>Lin, Hsin-Chia</creatorcontrib><title>Edge Computing-Based SAT-Video Coding for Remote Sensing</title><title>IEEE access</title><addtitle>Access</addtitle><description>This paper proposes an edge computing-based video coding implementation on an Earth observation satellite (SAT-video coding), which can encode video using limited resources and the power of mini/microsatellites. SAT-video coding proposes a hardware-related quantization (Q) function, hardware reduction of the motion estimation (ME) method, and simplified entropy coding (EC), which reduces the computation complexity. The hardware-related Q reduces hardware resource and power consumption by 72% and 55%, respectively, compared with traditional Q implementation. The hardware reduction of ME reduces resource use compared with regular ME implementation (59% of lookup tables [LUTs] and 79% of Registers). The total number of LUTs used for the simplified EC function is also much lower than other EC hardware implementations. The SAT-video encoder IP uses fewer hardware resources, and the power consumption is estimated at 0.0894 W at a high working frequency (125 MHz). The SAT-video encoding speed is 18.95 frames per second for <inline-formula> <tex-math notation="LaTeX">2560\times 2560 </tex-math></inline-formula> video. Therefore, the proposed SAT-video coding is an edge computation suitable for micro/minisatellites. The coding efficiency records the highest compression ratio at 33.8, with a peak signal-to-noise ratio of 34.46 dB. With the important task of designing edge computing based on satellite video encoding, these are adequate values for remote sensing video.</description><subject>Coders</subject><subject>Coding</subject><subject>Compression ratio</subject><subject>Earth observations (from space)</subject><subject>Edge computing</subject><subject>Encoding</subject><subject>Field programmable gate arrays</subject><subject>Frames per second</subject><subject>Hardware</subject><subject>hardware acceleration</subject><subject>Lookup tables</subject><subject>Mathematical models</subject><subject>Microsatellites</subject><subject>Motion simulation</subject><subject>Power consumption</subject><subject>Quantization (signal)</subject><subject>Remote sensing</subject><subject>remote sensing video</subject><subject>satellite</subject><subject>Satellite observation</subject><subject>Satellites</subject><subject>Signal to noise ratio</subject><subject>Streaming media</subject><subject>Video compression</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkF9rwjAUxcPYYOL8BL4U9lyXP02TPLriNkEYrG6vIU1upKLGpfVh335xFVleEn4559zLQWhK8IwQrJ7mVbWo6xnFlM4YEQXn7AaNKClVzjgrb_-979Gk67Y4HZkQFyMkF24DWRX2x1PfHjb5s-nAZfV8nX-1DkL6cQlnPsTsA_ahh6yGQ5fQA7rzZtfB5HKP0efLYl295av312U1X-W2wLLPnWTWE-84bgxh1lInvXU8scaxxguKHXApGRiQxDJVUuUwtUxgbhSGho3Rcsh1wWz1MbZ7E390MK3-AyFutIl9a3egi4JK44UpVAOFU6bhRIErUrxQmBY4ZT0OWccYvk_Q9XobTvGQ1te0LBXhilGeVGxQ2Ri6LoK_TiVYnxvXQ-P63Li-NJ5c08HVAsDVoYRgDCv2C5Apeos</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Bui, Trong-An</creator><creator>Lee, Pei-Jun</creator><creator>Chen, Kuan-Yu</creator><creator>Chen, Chia-Ray</creator><creator>Liu, Cynthia S. J.</creator><creator>Lin, Hsin-Chia</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-7660-4060</orcidid></search><sort><creationdate>2022</creationdate><title>Edge Computing-Based SAT-Video Coding for Remote Sensing</title><author>Bui, Trong-An ; Lee, Pei-Jun ; Chen, Kuan-Yu ; Chen, Chia-Ray ; Liu, Cynthia S. J. ; Lin, Hsin-Chia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-d83cf1fd50ba13cc2d8fcd5cf1bd3bf720de5883eae81c39629d02c3705a90eb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Coders</topic><topic>Coding</topic><topic>Compression ratio</topic><topic>Earth observations (from space)</topic><topic>Edge computing</topic><topic>Encoding</topic><topic>Field programmable gate arrays</topic><topic>Frames per second</topic><topic>Hardware</topic><topic>hardware acceleration</topic><topic>Lookup tables</topic><topic>Mathematical models</topic><topic>Microsatellites</topic><topic>Motion simulation</topic><topic>Power consumption</topic><topic>Quantization (signal)</topic><topic>Remote sensing</topic><topic>remote sensing video</topic><topic>satellite</topic><topic>Satellite observation</topic><topic>Satellites</topic><topic>Signal to noise ratio</topic><topic>Streaming media</topic><topic>Video compression</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bui, Trong-An</creatorcontrib><creatorcontrib>Lee, Pei-Jun</creatorcontrib><creatorcontrib>Chen, Kuan-Yu</creatorcontrib><creatorcontrib>Chen, Chia-Ray</creatorcontrib><creatorcontrib>Liu, Cynthia S. J.</creatorcontrib><creatorcontrib>Lin, Hsin-Chia</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE access</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bui, Trong-An</au><au>Lee, Pei-Jun</au><au>Chen, Kuan-Yu</au><au>Chen, Chia-Ray</au><au>Liu, Cynthia S. J.</au><au>Lin, Hsin-Chia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Edge Computing-Based SAT-Video Coding for Remote Sensing</atitle><jtitle>IEEE access</jtitle><stitle>Access</stitle><date>2022</date><risdate>2022</risdate><volume>10</volume><spage>52840</spage><epage>52852</epage><pages>52840-52852</pages><issn>2169-3536</issn><eissn>2169-3536</eissn><coden>IAECCG</coden><abstract>This paper proposes an edge computing-based video coding implementation on an Earth observation satellite (SAT-video coding), which can encode video using limited resources and the power of mini/microsatellites. SAT-video coding proposes a hardware-related quantization (Q) function, hardware reduction of the motion estimation (ME) method, and simplified entropy coding (EC), which reduces the computation complexity. The hardware-related Q reduces hardware resource and power consumption by 72% and 55%, respectively, compared with traditional Q implementation. The hardware reduction of ME reduces resource use compared with regular ME implementation (59% of lookup tables [LUTs] and 79% of Registers). The total number of LUTs used for the simplified EC function is also much lower than other EC hardware implementations. The SAT-video encoder IP uses fewer hardware resources, and the power consumption is estimated at 0.0894 W at a high working frequency (125 MHz). The SAT-video encoding speed is 18.95 frames per second for <inline-formula> <tex-math notation="LaTeX">2560\times 2560 </tex-math></inline-formula> video. Therefore, the proposed SAT-video coding is an edge computation suitable for micro/minisatellites. The coding efficiency records the highest compression ratio at 33.8, with a peak signal-to-noise ratio of 34.46 dB. With the important task of designing edge computing based on satellite video encoding, these are adequate values for remote sensing video.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2022.3174553</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0001-7660-4060</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Coders Coding Compression ratio Earth observations (from space) Edge computing Encoding Field programmable gate arrays Frames per second Hardware hardware acceleration Lookup tables Mathematical models Microsatellites Motion simulation Power consumption Quantization (signal) Remote sensing remote sensing video satellite Satellite observation Satellites Signal to noise ratio Streaming media Video compression |
| title | Edge Computing-Based SAT-Video Coding for Remote Sensing |
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