MDPI Books
Go to cart
Reprint
Download Flyer

Artificial Intelligence for Multimedia Signal Processing

Edited by
August 2022
212 pages
  • ISBN978-3-0365-4965-1 (Hardback)
  • ISBN978-3-0365-4966-8 (PDF)
https://doi.org/10.3390/books978-3-0365-4966-8
Free download (PDF)

This book is a reprint of the Special Issue Artificial Intelligence for Multimedia Signal Processing that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary

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.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
human-height estimation; depth video; depth 3D conversion; artificial intelligence; convolutional neural networks; deep neural network; convolutional neural network; environmental sound recognition; feature combination; multimodal joint representation; content curation social networks; different recommend tasks; content based recommend systems; scene/place classification; semantic segmentation; deep learning; weighting matrix; convolutional neural network; speech enhancement; generative adversarial network; relativistic GAN; convolutional neural network; deep learning; convolutional neural networks; lightweight neural network; single image super-resolution; image enhancement; image restoration; residual dense networks; visual sentiment analysis; sentiment classification; convolutional neural networks; graph convolutional networks; deep learning; generative adversarial networks; traffic surveillance image processing; image de-raining; fluency evaluation; speech recognition; data augmentation; variational autoencoder; speech conversion; heartbeat classification; convolutional neural network (CNN); canonical correlation analysis (CCA); Indian Sign Language (ISL); natural language processing; avatar; sign movement; context-free grammar; object detection; logical story unit detection (LSU); object re-ID; computer vision; deep learning; convolutional neural network; image processing; image restoration; single image artifacts reduction; dense networks; residual networks; channel attention networks; n/a