Robust and efficient airplane cockpit video coding leveraging temporal redundancy.
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| Title: | Robust and efficient airplane cockpit video coding leveraging temporal redundancy. |
|---|---|
| Authors: | Mitrica, Iulia1 (AUTHOR) iulia.mitrica@gmail.com, Fiandrotti, Attilio1,2 (AUTHOR) attilio.fiandrotti@telecom-paris.fr, Ruellan, Christophe3 (AUTHOR) christophe.ruellan@safrangroup.com, Cagnazzo, Marco1,4 (AUTHOR) marco.cagnazzo@unipd.it |
| Source: | Multimedia Tools & Applications. Jan2025, Vol. 84 Issue 2, p805-830. 26p. |
| Subjects: | Convolutional neural networks, Video compression standards, Artificial intelligence, Airplane cockpits, Video codecs, Video coding |
| Abstract: | Airplane cockpit screens consist of virtual instruments where characters, numbers, and graphics are overlaid on a black or natural background. Recording the cockpit screen allows one to log vital plane data, as aircraft manufacturers do not offer direct access to raw data. However, traditional video codecs struggle at preserving character readability at the required low bit-rates. We showed in a previous work that large rate-distortion gains can be achieved if the characters are encoded as text rather than as pixels. We now leverage temporal redundancy to both achieve robust character recognition and improve encoding efficiency. A convolutional neural network is trained for character classification over synthetic samples augmented with occlusions to gain robustness against overlapping graphics. Further robustness to background occlusions is brought by a probabilistic framework that error-corrects the output of the convolutional neural network. Next, we propose a predictive text coding technique specifically tailored for text in cockpit videos that achieves competitive performance over commodity lossless methods. Experiments with real cockpit video footage show large rate-distortion gains for the proposed method with respect to three different video compression standards. Notably, the H.264/AVC codec retrofitted with our method outperforms H.265/HEVC-SCC and is competitive with the much more complex H.266/VVC while preserving text and graphics. The entire pipeline described in this work has been implemented at Safran Electronics as an embedded avionics system drawing just 2W of power thanks to a combination of software and FPGA implementation. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 182537504 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Robust and efficient airplane cockpit video coding leveraging temporal redundancy. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mitrica%2C+Iulia%22">Mitrica, Iulia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> iulia.mitrica@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Fiandrotti%2C+Attilio%22">Fiandrotti, Attilio</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> attilio.fiandrotti@telecom-paris.fr</i><br /><searchLink fieldCode="AR" term="%22Ruellan%2C+Christophe%22">Ruellan, Christophe</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> christophe.ruellan@safrangroup.com</i><br /><searchLink fieldCode="AR" term="%22Cagnazzo%2C+Marco%22">Cagnazzo, Marco</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<i> marco.cagnazzo@unipd.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Jan2025, Vol. 84 Issue 2, p805-830. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Video+compression+standards%22">Video compression standards</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Airplane+cockpits%22">Airplane cockpits</searchLink><br /><searchLink fieldCode="DE" term="%22Video+codecs%22">Video codecs</searchLink><br /><searchLink fieldCode="DE" term="%22Video+coding%22">Video coding</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Airplane cockpit screens consist of virtual instruments where characters, numbers, and graphics are overlaid on a black or natural background. Recording the cockpit screen allows one to log vital plane data, as aircraft manufacturers do not offer direct access to raw data. However, traditional video codecs struggle at preserving character readability at the required low bit-rates. We showed in a previous work that large rate-distortion gains can be achieved if the characters are encoded as text rather than as pixels. We now leverage temporal redundancy to both achieve robust character recognition and improve encoding efficiency. A convolutional neural network is trained for character classification over synthetic samples augmented with occlusions to gain robustness against overlapping graphics. Further robustness to background occlusions is brought by a probabilistic framework that error-corrects the output of the convolutional neural network. Next, we propose a predictive text coding technique specifically tailored for text in cockpit videos that achieves competitive performance over commodity lossless methods. Experiments with real cockpit video footage show large rate-distortion gains for the proposed method with respect to three different video compression standards. Notably, the H.264/AVC codec retrofitted with our method outperforms H.265/HEVC-SCC and is competitive with the much more complex H.266/VVC while preserving text and graphics. The entire pipeline described in this work has been implemented at Safran Electronics as an embedded avionics system drawing just 2W of power thanks to a combination of software and FPGA implementation. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-024-18755-2 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 805 Subjects: – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Video compression standards Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Airplane cockpits Type: general – SubjectFull: Video codecs Type: general – SubjectFull: Video coding Type: general Titles: – TitleFull: Robust and efficient airplane cockpit video coding leveraging temporal redundancy. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mitrica, Iulia – PersonEntity: Name: NameFull: Fiandrotti, Attilio – PersonEntity: Name: NameFull: Ruellan, Christophe – PersonEntity: Name: NameFull: Cagnazzo, Marco IsPartOfRelationships: – BibEntity: Dates: – D: 11 M: 01 Text: Jan2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 84 – Type: issue Value: 2 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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