L3C-Stereo: Lossless Compression for Stereo Images
Zihao Huang, Zhe Sun, Feng Duan, Andrzej Cichocki, Peiying Ruan and, Chao Li

TL;DR
L3C-Stereo is a novel lossless stereo image compression model that leverages multi-scale features and disparity estimation to outperform existing methods and also provides useful disparity maps for stereo tasks.
Contribution
The paper introduces L3C-Stereo, a new multi-scale lossless compression model that combines warping and probability estimation modules for improved stereo image compression.
Findings
Outperforms existing compression methods on multiple datasets
Better maximum disparity improves compression quality
Generates disparity maps suitable for stereo tasks
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…
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Taxonomy
TopicsAdvanced Vision and Imaging · Advanced Image Processing Techniques · Video Coding and Compression Technologies
