Joint channel–spatial entropy modeling for efficient visual coding

Deep learning-based methods have recently achieved impressive performance in lossy image compression, surpassing traditional codecs in rate-distortion efficiency. However, current learned compressors still struggle to fully exploit crossed-channel redundancies and long-range spatial dependencies in...

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Veröffentlicht in:	Neural computing & applications Jg. 37; H. 21; S. 17111 - 17128
Hauptverfasser:	Li, Yuan, Jiang, Xiaotong, Sun, Zitang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.07.2025 Springer Nature B.V
Schlagworte:	Artificial Intelligence Codec Coding Compressors Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Context Data Mining and Knowledge Discovery Decoding Entropy Image compression Image Processing and Computer Vision Lagrange multiplier Modelling Normal distribution Original Article Probability and Statistics in Computer Science Representations Semantics Spatial dependencies Wavelet transforms Self-attention mechanism Lossy image compression Joint channel-spatial context Adaptive neighbored information
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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