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Deep semantic segmentation-based multiple description coding

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Abstract

In this paper, we propose a deep semantic segmentation-based multiple description coding (DSSMDC). In the proposed scheme, the input image is divided into two different subsets and getting two descriptions, which is named multiple description pre-processing (MDP). Then, two descriptions are encoded and decoded respectively by utilizing the deep semantic segmentation codec, in which the semantic segmentation label is as side information for improving image reconstruction quality. We can get one side reconstruction, when only one description is received at the decoder. If both descriptions are received at the decoder, we can get the central reconstruction. Experimental results show that the proposed scheme achieves better performance than other existing compression methods.

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250014, China

    Xue Li, Lili Meng, Yanyan Tan, Jia Zhang, Wenbo Wan & Huaxiang Zhang

  2. Institute of Data Science and Technology, Shandong Normal University, Jinan, 250014, China

    Xue Li, Lili Meng, Yanyan Tan, Jia Zhang, Wenbo Wan & Huaxiang Zhang

Authors
  1. Xue Li
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  2. Lili Meng
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  3. Yanyan Tan
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  4. Jia Zhang
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  5. Wenbo Wan
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  6. Huaxiang Zhang
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Correspondence to Lili Meng.

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Li, X., Meng, L., Tan, Y. et al. Deep semantic segmentation-based multiple description coding. Multimed Tools Appl 80, 10323–10337 (2021). https://doi.org/10.1007/s11042-020-09283-w

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  • DOI: https://doi.org/10.1007/s11042-020-09283-w

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