F
Feng Dai
Researcher at Chinese Academy of Sciences
Publications - 66
Citations - 2583
Feng Dai is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Deblocking filter & Context-adaptive binary arithmetic coding. The author has an hindex of 18, co-authored 66 publications receiving 2112 citations. Previous affiliations of Feng Dai include Tsinghua University & Huawei.
Papers
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Journal ArticleDOI
Drug–target interaction prediction: databases, web servers and computational models
TL;DR: In this review, databases and web servers involved in drug-target identification and drug discovery are summarized, and some state-of-the-art computational models for drug- target interactions prediction, including network-based method, machine learning- based method and so on are introduced.
Journal ArticleDOI
Efficient Parallel Framework for HEVC Motion Estimation on Many-Core Processors
TL;DR: This paper analyzes the ME structure in HEVC and proposes a parallel framework to decouple ME for different partitions on many-core processors and achieves more than 30 and 40 times speedup for 1920 × 1080 and 2560 × 1600 video sequences, respectively.
Journal ArticleDOI
A Highly Parallel Framework for HEVC Coding Unit Partitioning Tree Decision on Many-core Processors
TL;DR: This paper proposes a parallel framework to decide coding unit trees through in-depth understanding of the dependency among different coding units, and achieves averagely more than 11 and 16 times speedup for 1920x1080 and 2560x1600 video sequences, respectively, without any coding efficiency degradation.
Journal ArticleDOI
DR2-Net: Deep Residual Reconstruction Network for image compressive sensing
TL;DR: A novel Deep Residual Reconstruction Network (DR2-Net) to reconstruct the image from its Compressively Sensed measurement by outperforms traditional iterative methods and recent deep learning-based methods by large margins at measurement rates 0.01, 0.1, and 0.25.
Proceedings ArticleDOI
Fast mode decision algorithm for intra prediction in HEVC
TL;DR: A fast intra mode decision algorithm is proposed to speed up the original intra coding in HEVC by analysing the gradient information of the current prediction unit (PU), and only a small part of modes are selected as the candidate modes for the rate-distortion optimization process.