What is he best known for?
The fields of study he is best known for:
- Computer network
- The Internet
- Artificial intelligence
His primary areas of investigation include Decoding methods, Artificial intelligence, Computer vision, Bitstream and Scalability.
Ye-Kui Wang combines subjects such as Data mining and Linear network coding with his study of Decoding methods.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Group of pictures, Theoretical computer science and Error concealment.
His Video sequence study in the realm of Computer vision connects with subjects such as Transition.
As part of the same scientific family, Ye-Kui Wang usually focuses on Bitstream, concentrating on Real-time computing and intersecting with Multiview Video Coding, Image processing and Computer architecture.
The Scalability study combines topics in areas such as File format, Computer network and Distributed computing.
His most cited work include:
- The error concealment feature in the H.26L test model (301 citations)
- The emerging MVC standard for 3D video services (227 citations)
- Method and device for video coding and decoding (200 citations)
What are the main themes of his work throughout his whole career to date?
Ye-Kui Wang mainly investigates Artificial intelligence, Computer vision, Decoding methods, Real-time computing and Scalable Video Coding.
In the field of Artificial intelligence, his study on Motion vector overlaps with subjects such as Residual frame.
His studies in Computer vision integrate themes in fields like Video encoding and Error concealment.
His study on Bitstream is often connected to Process as part of broader study in Decoding methods.
His Real-time computing study integrates concerns from other disciplines, such as Computer hardware, Algorithmic efficiency, Image processing, Random access and Rate–distortion theory.
His Scalable Video Coding research is multidisciplinary, incorporating elements of Computer architecture, Network Abstraction Layer and Smacker video.
He most often published in these fields:
- Artificial intelligence (36.21%)
- Computer vision (32.76%)
- Decoding methods (32.76%)
What were the highlights of his more recent work (between 2009-2021)?
- Bitstream (25.00%)
- Artificial intelligence (36.21%)
- Computer hardware (16.38%)
In recent papers he was focusing on the following fields of study:
Bitstream, Artificial intelligence, Computer hardware, Computer network and Decoding methods are his primary areas of study.
His work deals with themes such as Video encoding, Internet video, Videoconferencing, Smacker video and Scalable Video Coding, which intersect with Bitstream.
His Artificial intelligence research integrates issues from Computer vision and Pattern recognition.
His study in Computer vision is interdisciplinary in nature, drawing from both Position and Encoding.
His biological study spans a wide range of topics, including Connection and Media content.
The study incorporates disciplines such as Speech recognition, Distortion, Scalability and Error detection and correction in addition to Decoding methods.
Between 2009 and 2021, his most popular works were:
- RTP Payload Format for Scalable Video Coding (47 citations)
- System and method to support different ingest and delivery schemes for a content delivery network (45 citations)
- Multiple Description Video Coding With H.264/AVC Redundant Pictures (42 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- The Internet
- Algorithm
Ye-Kui Wang focuses on Multimedia, Computer network, Internet video, Videoconferencing and Smacker video.
His work on Media adaptation and Media content as part of general Multimedia study is frequently linked to Set, bridging the gap between disciplines.
Computer network is represented through his Real Time Streaming Protocol, Payload and Backward compatibility research.
His work carried out in the field of Internet video brings together such families of science as Network packet, Embedded system, Bitstream, Network Abstraction Layer and Scalable Video Coding.
His Videoconferencing study combines topics from a wide range of disciplines, such as Computer hardware and RTP Control Protocol.
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