What is he best known for?
The fields of study he is best known for:
- Computer network
- Artificial intelligence
- The Internet
Mohammed Ghanbari mainly investigates Artificial intelligence, Computer vision, Video quality, Data compression and Algorithm.
His research in Artificial intelligence tackles topics such as Coding which are related to areas like Multimedia and Set partitioning in hierarchical trees.
His study in Computer vision is interdisciplinary in nature, drawing from both Decimation and Signal.
His Video quality research incorporates elements of Image quality, Frame rate, Computer network and Packet switching.
His Data compression research includes themes of Transform coding, Real-time computing, Reduction and Transcoding.
In his research on the topic of Algorithm, Codec, Frequency domain, Variable-length code and Theoretical computer science is strongly related with Discrete cosine transform.
His most cited work include:
- Scope of validity of PSNR in image/video quality assessment (1102 citations)
- The cross-search algorithm for motion estimation (image coding) (512 citations)
- Heterogeneous video transcoding to lower spatio-temporal resolutions and different encoding formats (324 citations)
What are the main themes of his work throughout his whole career to date?
Mohammed Ghanbari focuses on Computer network, Artificial intelligence, Real-time computing, Computer vision and Video quality.
Mohammed Ghanbari interconnects Wireless and The Internet in the investigation of issues within Computer network.
His Artificial intelligence study often links to related topics such as Pattern recognition.
His research integrates issues of Codec, Communication channel, Asynchronous Transfer Mode, WiMAX and Scalable Video Coding in his study of Real-time computing.
His Codec study integrates concerns from other disciplines, such as Algorithm and Coding.
His Video quality research is multidisciplinary, incorporating perspectives in Bluetooth, Decoding methods, Broadband networks and Network congestion.
He most often published in these fields:
- Computer network (33.66%)
- Artificial intelligence (31.88%)
- Real-time computing (27.52%)
What were the highlights of his more recent work (between 2009-2020)?
- Computer network (33.66%)
- Real-time computing (27.52%)
- Network packet (17.03%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Computer network, Real-time computing, Network packet, Video quality and Codec.
His studies in Computer network integrate themes in fields like Wireless network, Wireless broadband and WiMAX.
His Real-time computing research focuses on Scalable Video Coding and how it relates to Multiview Video Coding, Video tracking and Uncompressed video.
His Network packet study incorporates themes from Quality of service, Flexible Macroblock Ordering, Wireless WAN and Propagation of uncertainty.
His Video quality research incorporates themes from Wireless, Forward error correction, Access network and Broadband networks.
His study in Codec is interdisciplinary in nature, drawing from both Image quality, Discrete cosine transform, Data compression, Computer vision and Coding.
Between 2009 and 2020, his most popular works were:
- The accuracy of PSNR in predicting video quality for different video scenes and frame rates (86 citations)
- Reversible and high-capacity data hiding in medical images (56 citations)
- Multi-source video streaming in a wireless vehicular ad hoc network (38 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Artificial intelligence
- The Internet
Mohammed Ghanbari mostly deals with Video quality, Computer network, Codec, Real-time computing and Network packet.
His Video quality study combines topics from a wide range of disciplines, such as Incentive, Multiple encryption and Artificial intelligence.
Mohammed Ghanbari specializes in Artificial intelligence, namely Pixel.
His Codec research incorporates elements of Image quality, Discrete cosine transform, Computer vision, Algorithm and Coding.
His Pattern recognition research extends to Computer vision, which is thematically connected.
The study incorporates disciplines such as Scalable Video Coding and Decoding methods in addition to Real-time computing.
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