Learning-driven lossy image compression: A comprehensive survey

Author:

Jamil SonainORCID,Piran Md. JalilORCID,Rahman MuhibUrORCID,Kwon Oh-JinORCID

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference135 articles.

1. Image compression using principal component analysis;Abbas;Mustansiriyah J. Sci.,2018

2. Soft-to-hard vector quantization for end-to-end learning compressible representations;Agustsson;Adv. Neural Inf. Process. Syst.,2017

3. Agustsson, E., Tschannen, M., Mentzer, F., Timofte, R., Gool, L.V., 2019. Generative adversarial networks for extreme learned image compression. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 221–231.

4. Akyazi, P., Ebrahimi, T., 2019. Learning-Based Image Compression using Convolutional Autoencoder and Wavelet Decomposition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.

5. Alexandre, D., Chang, C.P., Peng, W.H., Hang, H.M., 2018. An autoencoder-based learned image compressor: Description of challenge proposal by NCTU. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. pp. 2539–2542.

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