Trellis-Coded Quantization for End-to-End Learned Image Compression-Reference-Cited by-同舟云学术

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Trellis-Coded Quantization for End-to-End Learned Image Compression

Published:2022-10-16 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Image Processing (ICIP)
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Author:

Suhring Karsten¹,Schafer Michael¹,Pfaff Jonathan¹,Schwarz Heiko¹,Marpe Detlev¹,Wiegand Thomas¹

Affiliation:

1. Heinrich Hertz Institute,Fraunhofer Institute for Telecommunications,Berlin,Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9897158/9897159/09897685.pdf?arnumber=9897685

Reference30 articles.

1. Joint Autoregressive and Hierarchical Priors for Learned Image Compression;minnen;Advances in neural information processing systems,2018

2. Conditional Probability Models for Deep Image Compression

3. Variable rate image compression with re-current neural networks;toderici;4th International Conference on Learning Representations ICLR 2016 San Juan Puerto Rico May 2-4 2016 Conference Track Proceedings,2016

4. Channel-Wise Autoregressive Entropy Models for Learned Image Compression

5. Calculation of average PSNR differences between RD-Curves;bjontegaard;Proceedings of the ITU-T Video Coding Experts Group (VCEG) Thirteenth Meeting,2001

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1. A VVC Dependent Quantization Optimization Based on the Parallel Viterbi Algorithm and Its FPGA Implementation;IEICE Transactions on Information and Systems;2024-07-01

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