• Treffer 63 von 673
Zurück zur Trefferliste

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

  • 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,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.zeige mehrzeige weniger

Metadaten exportieren

Weitere Dienste

Suche bei Google Scholar Statistik - Anzahl der Zugriffe auf das Dokument
Metadaten
Verfasserangaben:Fritjof SteinertORCiD, Benno StabernackORCiDGND
DOI:https://doi.org/10.1007/s11265-021-01727-2
ISSN:1939-8018
ISSN:1939-8115
Titel des übergeordneten Werks (Englisch):Journal of Signal Processing Systems for Signal, Image, and Video Technology
Verlag:Springer
Verlagsort:New York
Publikationstyp:Wissenschaftlicher Artikel
Sprache:Englisch
Datum der Erstveröffentlichung:01.07.2022
Erscheinungsjahr:2022
Datum der Freischaltung:05.01.2024
Freies Schlagwort / Tag:Advanced Video Codec (AVC); FPGA; H.264; Hardware accelerator; Inference; Low Latency; Machine Learning; Region of Interest
Band:94
Ausgabe:7
Seitenanzahl:16
Erste Seite:693
Letzte Seite:708
Fördernde Institution:German Federal Ministry for Economic Affairs and Energy (BMWi); [03SX481E]
Organisationseinheiten:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Informatik und Computational Science
DDC-Klassifikation:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Peer Review:Referiert
Publikationsweg:Open Access / Hybrid Open-Access
Lizenz (Deutsch):License LogoCC-BY - Namensnennung 4.0 International
Verstanden ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.