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- W3194489076 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." @default.
- W3194489076 created "2021-08-30" @default.
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- W3194489076 date "2021-08-20" @default.
- W3194489076 modified "2023-10-16" @default.
- W3194489076 title "L3C-Stereo: Lossless Compression for Stereo Images" @default.
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- W3194489076 doi "https://doi.org/10.48550/arxiv.2108.09422" @default.
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