What is he best known for?
The fields of study he is best known for:
- Algorithm
- Artificial intelligence
- Computer network
His scientific interests lie mostly in Algorithm, Context-adaptive binary arithmetic coding, Multiview Video Coding, Scalable Video Coding and Data compression.
The various areas that Detlev Marpe examines in his Algorithm study include Entropy and Theoretical computer science.
His work in Context-adaptive binary arithmetic coding tackles topics such as Coding tree unit which are related to areas like Artificial intelligence, Motion compensation and Computer vision.
His studies in Multiview Video Coding integrate themes in fields like MPEG-4, Real-time computing, Smacker video and Video compression picture types.
Detlev Marpe combines topics linked to Image compression with his work on Scalable Video Coding.
His work investigates the relationship between Data compression and topics such as Transform coding that intersect with problems in Wavelet transform and Discrete wavelet transform.
His most cited work include:
- Overview of the Scalable Video Coding Extension of the H.264/AVC Standard (3432 citations)
- Context-based adaptive binary arithmetic coding in the H.264/AVC video compression standard (1442 citations)
- Video coding with H.264/AVC: tools, performance, and complexity (888 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Algorithm, Decoding methods, Artificial intelligence, Data stream and Context-adaptive binary arithmetic coding.
The various areas that he examines in his Algorithm study include Entropy and Algorithmic efficiency.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Residual, Computer vision and Pattern recognition.
His research in Context-adaptive binary arithmetic coding focuses on subjects like Coding tree unit, which are connected to Multiview Video Coding, Sub-band coding and Macroblock.
In his study, Real-time computing is inextricably linked to Scalable Video Coding, which falls within the broad field of Multiview Video Coding.
His Data compression research incorporates elements of Image processing, Image compression and Quadtree.
He most often published in these fields:
- Algorithm (59.13%)
- Decoding methods (29.97%)
- Artificial intelligence (20.71%)
What were the highlights of his more recent work (between 2018-2021)?
- Algorithm (59.13%)
- Decoding methods (29.97%)
- Data stream (16.08%)
In recent papers he was focusing on the following fields of study:
Detlev Marpe focuses on Algorithm, Decoding methods, Data stream, Data compression and Random access.
His Algorithm study incorporates themes from Artificial neural network, Entropy and Algorithmic efficiency.
His research investigates the connection between Artificial neural network and topics such as Context-adaptive binary arithmetic coding that intersect with problems in Binary number.
Detlev Marpe has included themes like Encoding, Lossless compression, Motion compensation, Template matching and Search algorithm in his Decoding methods study.
Detlev Marpe combines subjects such as Base, Network packet and Backward compatibility with his study of Data stream.
His research in Data compression intersects with topics in Image processing, Quadtree, Adaptive filter and Diffusion filter.
Between 2018 and 2021, his most popular works were:
- An Intra Subpartition Coding Mode for VVC (16 citations)
- Intra Picture Prediction for Video Coding with Neural Networks (16 citations)
- Cross-Component Prediction in HEVC (16 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Artificial intelligence
- Computer network
His primary areas of investigation include Algorithm, Quantization, Decoding methods, Random access and Artificial neural network.
His work in Data compression and Coding gain are all subfields of Algorithm research.
His Quantization study also includes fields such as
- Entropy encoding together with Codec,
- Distortion most often made with reference to Transform coding.
His Context-adaptive binary arithmetic coding research extends to Artificial neural network, which is thematically connected.
Particularly relevant to Context-adaptive variable-length coding is his body of work in Context-adaptive binary arithmetic coding.
His Artificial intelligence research is multidisciplinary, incorporating elements of Multiview Video Coding, Coding tree unit and Chrominance.
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