What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Algorithm
His primary areas of study are Computer vision, Artificial intelligence, Encoder, Decoding methods and Motion vector.
Computer vision connects with themes related to Computer graphics in his study.
In general Artificial intelligence, his work in Lossy compression, Lossless JPEG and Context-adaptive binary arithmetic coding is often linked to Scale and High color linking many areas of study.
Video decoder is the focus of his Encoder research.
His Decoding methods study combines topics in areas such as Tree traversal and Codec, Computer hardware.
His study in Motion vector is interdisciplinary in nature, drawing from both Luminance, Chrominance and Quarter-pixel motion.
His most cited work include:
- Skip macroblock coding (207 citations)
- Spatial extrapolation of pixel values in intraframe video coding and decoding (204 citations)
- Inferring road speeds for context-sensitive routing (165 citations)
What are the main themes of his work throughout his whole career to date?
Sridhar Srinivasan mostly deals with Encoder, Artificial intelligence, Computer vision, Decoding methods and Algorithm.
His Encoder research is mostly focused on the topic Video decoder.
The study of Artificial intelligence is intertwined with the study of Multiview Video Coding in a number of ways.
His Computer vision research incorporates themes from Bitstream and Block-matching algorithm.
His study explores the link between Decoding methods and topics such as Real-time computing that cross with problems in Routing and Simulation.
His Algorithm research is multidisciplinary, incorporating elements of Codec, Theoretical computer science and Lapped transform.
He most often published in these fields:
- Encoder (39.49%)
- Artificial intelligence (39.49%)
- Computer vision (38.85%)
What were the highlights of his more recent work (between 2011-2017)?
- Database (4.46%)
- Decoding methods (29.30%)
- Encoder (39.49%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Database, Decoding methods, Encoder, Digital media and Artificial intelligence.
His Decoding methods study which covers Encoding that intersects with Block and Motion vector.
His research in Encoder is mostly focused on Video decoder.
His research in Digital media intersects with topics in Topology, Image compression and Lapped transform.
Sridhar Srinivasan usually deals with Artificial intelligence and limits it to topics linked to Computer vision and Scaling.
His Algorithm research includes elements of Pixel, Sum of absolute transformed differences and Real-time computing.
Between 2011 and 2017, his most popular works were:
- Signaling and uses of windowing information for images (30 citations)
- Harmonic quantizer scale (26 citations)
- Video coding / decoding with re-oriented transforms and sub-block transform sizes (17 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Algorithm
- Artificial intelligence
Sridhar Srinivasan mainly investigates Encoder, Algorithm, Metadata repository, Database and Data element.
His Encoder research is multidisciplinary, incorporating perspectives in Transform coding and Motion compensation.
Research on Computer vision and Artificial intelligence is a part of his Motion compensation study.
Many of his research projects under Algorithm are closely connected to Denormal number with Denormal number, tying the diverse disciplines of science together.
The study incorporates disciplines such as Metadata modeling, Marker interface pattern, Process and Cloud computing in addition to Database.
His Video decoder research is included under the broader classification of Decoding methods.
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