Ensemble Learning-Based Rate-Distortion Optimization for End-to-End Image Compression

End-to-end image compression using trained deep networks as encoding/decoding models has been developed substantially in the recent years. Previous work is limited in using a single encoding/decoding model, whereas we explore the usage of multiple encoding/decoding models as an ensemble. We propose...

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Vydané v:	IEEE transactions on circuits and systems for video technology Ročník 31; číslo 3; s. 1193 - 1207
Hlavní autori:	Wang, Yefei, Liu, Dong, Ma, Siwei, Wu, Feng, Gao, Wen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.03.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptation models Coders Coding Decoding Distortion Encoding-Decoding Ensemble learning Entropy coding Floating point arithmetic Geometric transformation Image coding Image compression Modal choice Optimization Rate-distortion rate-distortion optimization Transforms
ISSN:	1051-8215, 1558-2205
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