Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    Deutsche Normungsroadmap Künstliche Intelligenz
    (DIN, 2020)
    Adler, R.
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    Kolomiichuk, Sergii
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    Hecker, Dirk
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    Lämmel, Philipp
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    Ma, Jackie
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    Marko, Angelina
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    Mock, Michael
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    Nagel, Tobias
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    Poretschkin, Maximilian
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    Rennoch, Axel orcid-logo
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    Röhler, Marcus
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    Ruf, Miriam
    ;
    Schönhof, Raoul orcid-logo
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    Schneider, Martin A.
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    Tcholtchev, Nikolay
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    Ziehn, Jens
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    Böttinger, Konstantin
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    Jedlitschka, Andreas
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    Oala, Luis
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    Sperl, Philip
    ;
    Wenzel, Markus
    ;
    et al.
    Die deutsche Normungsroadmap Künstliche Intelligenz (KI) verfolgt das Ziel, für die Normung Handlungsempfehlungen rund um KI zu geben, denn sie gilt in Deutschland und Europa in fast allen Branchen als eine der Schlüsseltechnologien für künftige Wettbewerbsfähigkeit. Die EU geht davon aus, dass die Wirtschaft in den kommenden Jahren mit Hilfe von KI stark wachsen wird. Umso wichtiger sind die Empfehlungen der Normungsroadmap, die die deutsche Wirtschaft und Wissenschaft im internationalen KI-Wettbewerb stärken, innovationsfreundliche Bedingungen schaffen und Vertrauen in die Technologie aufbauen sollen.
  • Publication
    Pattern-based integer sample motion search strategies in the context of HEVC
    ( 2015)
    Maier, Georg
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    Bross, B.
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    Grois, D.
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    Marpe, D.
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    Schwarz, H.
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    Veltkamp, R.C.
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    Wiegand, T.
    The H.265/MPEG-H High Efficiency Video Coding (HEVC) standard provides a significant increase in coding efficiency compared to its predecessor, the H.264/MPEG-4 Advanced Video Coding (AVC) standard, which however comes at the cost of a high computational burden for a compliant encoder. Motion estimation (ME), which is a part of the inter-picture prediction process, typically consumes a high amount of computational resources, while significantly increasing the coding efficiency. In spite of the fact that both H.265/MPEG-H HEVC and H.264/MPEG-4 AVC standards allow processing motion information on a fractional sample level, the motion search algorithms based on the integer sample level remain to be an integral part of ME. In this paper, a flexible integer sample ME framework is proposed, thereby allowing to trade off significant reduction of ME computation time versus coding efficiency penalty in terms of bit rate overhead. As a result, through extensive experimentation, an integer sample ME algorithm that provides a good trade-off is derived, incorporating a combination and optimization of known predictive, pattern-based and early termination techniques. The proposed ME framework is implemented on a basis of the HEVC Test Model (HM) reference software, further being compared to the state-of-the-art fast search algorithm, which is a native part of HM. It is observed that for high resolution sequences, the integer sample ME process can be speed-up by factors varying from 3.2 to 7.6, resulting in the bit-rate overhead of 1.5% and 0.6% for Random Access (RA) and Low Delay P (LDP) configurations, respectively. In addition, the similar speed-up is observed for sequences with mainly Computer-Generated Imagery (CGI) content while trading off the bit rate overhead of up to 5.2%.