Artificial Intelligence for Multimedia Signal Processing

Artificial intelligence technologies are also actively applied to broadcasting and multimedia processing technologies. A lot of research has been conducted in a wide variety of fields, such as content creation, transmission, and security, and these attempts have been made in the past two to three ye...

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Bibliographic Details
Other Authors: Kim, Byung-Gyu (Editor), Jun, Dongsan (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
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DOAB: description of the publication
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520 |a Artificial intelligence technologies are also actively applied to broadcasting and multimedia processing technologies. A lot of research has been conducted in a wide variety of fields, such as content creation, transmission, and security, and these attempts have been made in the past two to three years to improve image, video, speech, and other data compression efficiency in areas related to MPEG media processing technology. Additionally, technologies such as media creation, processing, editing, and creating scenarios are very important areas of research in multimedia processing and engineering. This book contains a collection of some topics broadly across advanced computational intelligence algorithms and technologies for emerging multimedia signal processing as: Computer vision field, speech/sound/text processing, and content analysis/information mining. 
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650 7 |a History of engineering & technology  |2 bicssc 
653 |a human-height estimation 
653 |a depth video 
653 |a depth 3D conversion 
653 |a artificial intelligence 
653 |a convolutional neural networks 
653 |a deep neural network 
653 |a convolutional neural network 
653 |a environmental sound recognition 
653 |a feature combination 
653 |a multimodal joint representation 
653 |a content curation social networks 
653 |a different recommend tasks 
653 |a content based recommend systems 
653 |a scene/place classification 
653 |a semantic segmentation 
653 |a deep learning 
653 |a weighting matrix 
653 |a speech enhancement 
653 |a generative adversarial network 
653 |a relativistic GAN 
653 |a lightweight neural network 
653 |a single image super-resolution 
653 |a image enhancement 
653 |a image restoration 
653 |a residual dense networks 
653 |a visual sentiment analysis 
653 |a sentiment classification 
653 |a graph convolutional networks 
653 |a generative adversarial networks 
653 |a traffic surveillance image processing 
653 |a image de-raining 
653 |a fluency evaluation 
653 |a speech recognition 
653 |a data augmentation 
653 |a variational autoencoder 
653 |a speech conversion 
653 |a heartbeat classification 
653 |a convolutional neural network (CNN) 
653 |a canonical correlation analysis (CCA) 
653 |a Indian Sign Language (ISL) 
653 |a natural language processing 
653 |a avatar 
653 |a sign movement 
653 |a context-free grammar 
653 |a object detection 
653 |a logical story unit detection (LSU) 
653 |a object re-ID 
653 |a computer vision 
653 |a image processing 
653 |a single image artifacts reduction 
653 |a dense networks 
653 |a residual networks 
653 |a channel attention networks 
653 |a n/a 
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