Semantic-oriented learning-based image compression by Only-Train-Once quantized autoencoders

Accessibility to big training datasets together with current advances in computing power has emerged interest in the leverage of deep learning to address image compression. This needs to train and deploy separate networks for rate adaptation, which is impractical and extensive in terms of memory cos...

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Vydané v:	Signal, image and video processing Ročník 17; číslo 1; s. 285 - 293
Hlavní autori:	Sebai, D., Shah, A. Ulah
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.02.2023 Springer Nature B.V
Predmet:	Adaptation Codec Computer Imaging Computer Science Datasets Deep learning Image coding Image compression Image Processing and Computer Vision Image quality Lagrange multiplier Machine learning Methods Multimedia Information Systems Neural networks Original Paper Pattern Recognition and Graphics Semantic analysis Semantics Signal,Image and Speech Processing Vision Learning-based image compression Loss-conditional autoencoder Variable-rate compression Multi-term loss function Quantized autoencoders Only-Train-Once
ISSN:	1863-1703, 1863-1711
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