Block-optimized Variable Bit Rate Neural Image Compression

Caglar Aytekin, Xingyang Ni, Francesco Cricri, Jani Lainema, Emre Aksu, Miska Hannuksela; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 2551-2554

Abstract


In this work, we propose an end-to-end block-based auto-encoder system for image compression. We introduce novel contributions to neural-network based image compression, mainly in achieving binarization simulation, variable bit rates with multiple networks, entropyfriendly representations, inference-stage code optimization and performance-improving normalization layers in the auto-encoder. We evaluate and show the incremental performance increase of each of our contributions.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Aytekin_2018_CVPR_Workshops,
author = {Aytekin, Caglar and Ni, Xingyang and Cricri, Francesco and Lainema, Jani and Aksu, Emre and Hannuksela, Miska},
title = {Block-optimized Variable Bit Rate Neural Image Compression},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}