What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
His primary areas of investigation include Artificial intelligence, Computer vision, Image processing, Algorithm and Multimedia. His work carried out in the field of Artificial intelligence brings together such families of science as Decoding methods and Pattern recognition. His biological study spans a wide range of topics, including Transform coding, Computer graphics and Region of interest.
His specific area of interest is Algorithm, where Debargha Mukherjee studies Data compression. In his research on the topic of Multimedia, Codec is strongly related with Intra-frame. His study on Motion estimation also encompasses disciplines like
- Image resolution and related Motion compensation,
- Block-matching algorithm that connect with fields like Coding tree unit and Macroblock.
His most cited work include:
- Rate-distortion optimized mode selection for very low bit rate video coding and the emerging H.263 standard (381 citations)
- Method for embedding and extracting digital data in images and video (153 citations)
- The latest open-source video codec VP9 - An overview and preliminary results (126 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Algorithm, Decoding methods and Codec. His study looks at the relationship between Artificial intelligence and topics such as Inter frame, which overlap with Residual frame. His Computer vision research is multidisciplinary, relying on both Frame and Video compression picture types.
In general Algorithm study, his work on Data compression often relates to the realm of Encoder, thereby connecting several areas of interest. He has researched Decoding methods in several fields, including Block and Encoding. His Motion compensation research includes themes of Image resolution and Coding tree unit.
He most often published in these fields:
- Artificial intelligence (40.31%)
- Computer vision (33.16%)
- Algorithm (32.14%)
What were the highlights of his more recent work (between 2015-2021)?
- Algorithm (32.14%)
- Codec (18.37%)
- Artificial intelligence (40.31%)
In recent papers he was focusing on the following fields of study:
Debargha Mukherjee mostly deals with Algorithm, Codec, Artificial intelligence, Decoding methods and Data compression. His research investigates the connection between Algorithm and topics such as Filter that intersect with problems in Frequency domain, Convolutional neural network, Image texture and Least squares optimization. In his study, Entropy encoding and Quantization is strongly linked to Multimedia, which falls under the umbrella field of Codec.
His research integrates issues of Reference frame, Computer vision and Pattern recognition in his study of Artificial intelligence. His work on Motion compensation, Depth map and Color depth as part of general Computer vision research is frequently linked to Position and Panning, thereby connecting diverse disciplines of science. The concepts of his Decoding methods study are interwoven with issues in Block, Scalability and Video sequence.
Between 2015 and 2021, his most popular works were:
- Global and locally adaptive warped motion compensation in video compression (16 citations)
- An Overview of Coding Tools in AV1: the First Video Codec from the Alliance for Open Media (14 citations)
- A switchable loop-restoration with side-information framework for the emerging AV1 video codec (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Computer vision
His scientific interests lie mostly in Encoder, Algorithm, Codec, Data compression and Computer hardware. His research is interdisciplinary, bridging the disciplines of Encoding and Algorithm. As part of the same scientific family, Debargha Mukherjee usually focuses on Codec, concentrating on Decoding methods and intersecting with Reference software, Computer architecture and Scalability.
His study looks at the intersection of Data compression and topics like Reference frame with Average bitrate, Temporal filtering, Coding gain and Computer vision. His work in Computer hardware addresses subjects such as Transcoding, which are connected to disciplines such as Real-time computing. His Transform coding study is related to the wider topic of Artificial intelligence.
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