Architecture of a Low Latency H.264/AVC Video Codec for Robust ML based Image Classification: How Region of Interests can Minimize the Impact of Coding Artifacts.
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| Titel: | Architecture of a Low Latency H.264/AVC Video Codec for Robust ML based Image Classification: How Region of Interests can Minimize the Impact of Coding Artifacts. |
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| Autoren: | Steinert, Fritjof, Stabernack, Benno |
| Quelle: | Journal of Signal Processing Systems for Signal, Image & Video Technology; Jul2022, Vol. 94 Issue 7, p693-708, 16p |
| Abstract: | The use of neural networks is considered as the state of the art in the field of image classification. A large number of different networks are available for this purpose, which, appropriately trained, permit a high level of classification accuracy. Typically, these networks are applied to uncompressed image data, since a corresponding training was also carried out using image data of similar high quality. However, if image data contains image errors, the classification accuracy deteriorates drastically. This applies in particular to coding artifacts which occur due to image and video compression. Typical application scenarios for video compression are narrowband transmission channels for which video coding is required but a subsequent classification is to be carried out on the receiver side. In this paper we present a special H.264/Advanced Video Codec (AVC) based video codec that allows certain regions of a picture to be coded with near constant picture quality in order to allow a reliable classification using neural networks, whereas the remaining image will be coded using constant bit rate. We have combined this feature with the ability to run with lowest latency properties, which is usually also required in remote control applications scenarios. The codec has been implemented as a fully hardwired High Definition video capable hardware architecture which is suitable for Field Programmable Gate Arrays. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Signal Processing Systems for Signal, Image & Video Technology 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.) | |
| Datenbank: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Architecture of a Low Latency H.264/AVC Video Codec for Robust ML based Image Classification: How Region of Interests can Minimize the Impact of Coding Artifacts. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Steinert%2C+Fritjof%22">Steinert, Fritjof</searchLink><br /><searchLink fieldCode="AR" term="%22Stabernack%2C+Benno%22">Stabernack, Benno</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Signal Processing Systems for Signal, Image & Video Technology; Jul2022, Vol. 94 Issue 7, p693-708, 16p – Name: Abstract Label: Abstract Group: Ab Data: The use of neural networks is considered as the state of the art in the field of image classification. A large number of different networks are available for this purpose, which, appropriately trained, permit a high level of classification accuracy. Typically, these networks are applied to uncompressed image data, since a corresponding training was also carried out using image data of similar high quality. However, if image data contains image errors, the classification accuracy deteriorates drastically. This applies in particular to coding artifacts which occur due to image and video compression. Typical application scenarios for video compression are narrowband transmission channels for which video coding is required but a subsequent classification is to be carried out on the receiver side. In this paper we present a special H.264/Advanced Video Codec (AVC) based video codec that allows certain regions of a picture to be coded with near constant picture quality in order to allow a reliable classification using neural networks, whereas the remaining image will be coded using constant bit rate. We have combined this feature with the ability to run with lowest latency properties, which is usually also required in remote control applications scenarios. The codec has been implemented as a fully hardwired High Definition video capable hardware architecture which is suitable for Field Programmable Gate Arrays. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Signal Processing Systems for Signal, Image & Video Technology 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/s11265-021-01727-2 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 693 Titles: – TitleFull: Architecture of a Low Latency H.264/AVC Video Codec for Robust ML based Image Classification: How Region of Interests can Minimize the Impact of Coding Artifacts. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Steinert, Fritjof – PersonEntity: Name: NameFull: Stabernack, Benno IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 19398018 Numbering: – Type: volume Value: 94 – Type: issue Value: 7 Titles: – TitleFull: Journal of Signal Processing Systems for Signal, Image & Video Technology Type: main |
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