L3C-Stereo: Lossless Compression for Stereo Images

• URL: http://arxiv.org/abs/2108.09422v1
• Date: Sat, 21 Aug 2021 02:36:15 GMT
• Title: L3C-Stereo: Lossless Compression for Stereo Images
• Authors: Zihao Huang, Zhe Sun, Feng Duan, Andrzej Cichocki, Peiying Ruan and Chao Li
• Abstract summary: A large number of autonomous driving tasks need high-definition stereo images, which requires a large amount of storage space. To tackle this, we propose L3C-Stereo, a multi-scale lossless compression model consisting of two main modules. In the experiments, our method outperforms the hand-crafted compression methods and the learning-based method on all three datasets used.
• Score: 24.6995551743392
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A large number of autonomous driving tasks need high-definition stereo images, which requires a large amount of storage space. Efficiently executing lossless compression has become a practical problem. Commonly, it is hard to make accurate probability estimates for each pixel. To tackle this, we propose L3C-Stereo, a multi-scale lossless compression model consisting of two main modules: the warping module and the probability estimation module. The warping module takes advantage of two view feature maps from the same domain to generate a disparity map, which is used to reconstruct the right view so as to improve the confidence of the probability estimate of the right view. The probability estimation module provides pixel-wise logistic mixture distributions for adaptive arithmetic coding. In the experiments, our method outperforms the hand-crafted compression methods and the learning-based method on all three datasets used. Then, we show that a better maximum disparity can lead to a better compression effect. Furthermore, thanks to a compression property of our model, it naturally generates a disparity map of an acceptable quality for the subsequent stereo tasks.

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