What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Computer vision
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Motion estimation, Quarter-pixel motion and Motion compensation.
His Artificial intelligence research integrates issues from Multiview Video Coding, Video decoder, Inter frame and Video compression picture types.
His work carried out in the field of Video decoder brings together such families of science as Macroblock and Soft-decision decoder.
His Quarter-pixel motion study combines topics from a wide range of disciplines, such as Motion field and Block-matching algorithm.
His Motion compensation research incorporates themes from Reference frame and Control theory.
His Reference frame study integrates concerns from other disciplines, such as Compensation, Quantization, Header, Bitstream and Decodes.
His most cited work include:
- Multiple bit rate video encoding using variable bit rate and dynamic resolution for adaptive video streaming (152 citations)
- Coding of motion vector information (125 citations)
- Chroma motion vector derivation for interlaced forward-predicted fields (119 citations)
What are the main themes of his work throughout his whole career to date?
Thomas W. Holcomb focuses on Artificial intelligence, Computer vision, Decoding methods, Video decoder and Macroblock.
The concepts of his Artificial intelligence study are interwoven with issues in Frame, Residual frame and Decodes.
His Computer vision research is multidisciplinary, incorporating elements of Codec and Block-matching algorithm.
His Decoding methods study combines topics in areas such as Entropy and Speech recognition.
The Video decoder study combines topics in areas such as Scalable Video Coding and Soft-decision decoder.
His study in Macroblock is interdisciplinary in nature, drawing from both Pixel, Polarity and Coding tree unit.
He most often published in these fields:
- Artificial intelligence (62.64%)
- Computer vision (60.44%)
- Decoding methods (29.67%)
What were the highlights of his more recent work (between 2009-2018)?
- Computer vision (60.44%)
- Artificial intelligence (62.64%)
- Decoding methods (29.67%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Computer vision, Artificial intelligence, Decoding methods, Motion compensation and Video decoder.
His work in Computer vision is not limited to one particular discipline; it also encompasses Transcoding.
His research in the fields of Macroblock overlaps with other disciplines such as Integrally closed and Block.
His Motion compensation research includes themes of Multiview Video Coding, Video encoding, Motion estimation, Real-time computing and Block-matching algorithm.
The Multiview Video Coding study which covers Quarter-pixel motion that intersects with Inter frame, Residual frame and Video compression picture types.
His Video decoder research includes elements of Sampling, Codec and Sample.
Between 2009 and 2018, his most popular works were:
- Multiple bit rate video encoding using variable bit rate and dynamic resolution for adaptive video streaming (152 citations)
- Video encoding using previously calculated motion information (80 citations)
- Video transcoding with dynamically modifiable spatial resolution (40 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Artificial intelligence
- Data compression
Thomas W. Holcomb mainly focuses on Computer vision, Artificial intelligence, Real-time computing, Motion compensation and Temporal complexity.
His study of Motion estimation is a part of Computer vision.
Thomas W. Holcomb is studying Quarter-pixel motion, which is a component of Artificial intelligence.
His Real-time computing research incorporates elements of Macroblock, Decoding methods, Video decoder, Pixel and Sum of absolute transformed differences.
His Motion compensation study necessitates a more in-depth grasp of Algorithm.
Temporal complexity is integrated with Quantization, Transcoding and Image resolution in his study.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
