Lossless Image Compression Using a Multi-Scale Progressive Statistical Model

Honglei Zhang, Francesco Cricri, Hamed R. Tavakoli, Nannan Zou, Emre Aksu, Miska M. Hannuksela; Proceedings of the Asian Conference on Computer Vision (ACCV), 2020


Lossless image compression is an important technique for im-age storage and transmission when information loss is not allowed. Withthe fast development of deep learning techniques, deep neural networkshave been used in this field to achieve a higher compression rate. Meth-ods based on pixel-wise autoregressive statistical models have showngood performance. However, the sequential processing way prevents thesemethods to be used in practice. Recently, multi-scale autoregressive mod-els have been proposed to address this limitation. Multi-scale approachescan use parallel computing systems efficiently and build practical sys-tems. Nevertheless, these approaches sacrifice compression performancein exchange for speed. In this paper, we propose a multi-scale progressivestatistical model that takes advantage of the pixel-wise approach and themulti-scale approach. We developed a flexible mechanism where the pro-cessing order of the pixels can be adjusted easily. Our proposed methodoutperforms the state-of-the-art lossless image compression methods ontwo large benchmark datasets by a significant margin without degradingthe inference speed dramatically.

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@InProceedings{Zhang_2020_ACCV, author = {Zhang, Honglei and Cricri, Francesco and Tavakoli, Hamed R. and Zou, Nannan and Aksu, Emre and Hannuksela, Miska M.}, title = {Lossless Image Compression Using a Multi-Scale Progressive Statistical Model}, booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)}, month = {November}, year = {2020} }