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Ye-Kui Wang
Researcher at Nokia
Publications - 107
Citations - 4820
Ye-Kui Wang is an academic researcher from Nokia. The author has contributed to research in topics: Scalable Video Coding & Motion compensation. The author has an hindex of 40, co-authored 107 publications receiving 4542 citations. Previous affiliations of Ye-Kui Wang include Huawei.
Papers
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Proceedings ArticleDOI
The error concealment feature in the H.26L test model
TL;DR: The specific concealment strategy and some special methods, including handling of B-pictures, multiple reference frames and entire frame losses, are described and both subjective and objective results are given based on simulations under Internet conditions.
Journal ArticleDOI
The emerging MVC standard for 3D video services
TL;DR: Multiview applications and solutions to support generic multiview as well as 3D services are introduced and cover a wide range of requirements for 3D video related to interface, transport of the MVC bitstreams, and MVC decoder resource management.
Journal ArticleDOI
Overview of the Versatile Video Coding (VVC) Standard and its Applications
TL;DR: Versatile Video Coding (VVC) was developed by the Joint Video Experts Team (JVET) and the ISO/IEC Moving Picture Experts Group (MPEG) to serve an evergrowing need for improved video compression as well as to support a wider variety of today's media content and emerging applications as mentioned in this paper.
Patent
Method and device for video coding and decoding
TL;DR: In this article, the authors present a method for encoding at least two views representative of a video scene, each of which is encoded in at least 2 scalable layers, where one of the layers represents one view of one view and the other represents another view of the same view.
Patent
Coding, storage and signalling of scalability information
Ye-Kui Wang,Miska Hannuksela +1 more
TL;DR: In this article, the authors propose a method for encoding, decoding, storage and transmission of a scalable data stream to include layers having different coding properties, such as fine granularity scalability, region-of-interest scalability information, sub-sample scalable layer information, decoding dependency information, and initial parameter sets.