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arXiv 2009.12480cs.ITcs.LGeess.IVeess.SPmath.IT
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基于深度联合信源-信道编码的带宽捷变图像传输

Bandwidth-Agile Image Transmission with Deep Joint Source-Channel Coding

Imperial College London(伦敦帝国学院)

David Burth Kurka, Deniz Gündüz

摘要:

我们提出了基于深度学习的通信方法,用于通过无线信道进行图像的自适应带宽传输。我们考虑的情况下,图像传输层随着时间或频率的推移,这些层可以聚合的接收器,以提高其重建的质量。我们调查了两种情况,一种情况下,层被顺序发送,并逐步有助于重建的细化,另一种情况下,层是独立的,可以以任何顺序检索。在联合信源-信道编码(JSCC)的上下文中,这些情形分别对应于\textit{successive refinement}和\textit{multiple descriptions}的公知问题。我们提出了DeepJSCC- $l$,这是一种使用卷积自动编码器的创新解决方案,并提出了三种具有不同复杂性权衡的架构。据我们所知,这是第一个针对实际信息源和渠道开发和测试的实用多描述JSCC方案。数值结果表明,与单次传输相比,DeepJSCC- $l$可以学习渐进地传输源,而端到端性能的损失可以忽略不计。此外,$l$在具有挑战性的低信噪比(SNR)和小带宽机制中具有与现有技术的数字渐进传输方案相当的性能,具有随信道SNR适度降级的额外优势。

英文摘要:

We propose deep learning based communication methods for adaptive-bandwidth transmission of images over wireless channels. We consider the scenario in which images are transmitted progressively in layers over time or frequency, and such layers can be aggregated by receivers in order to increase the quality of their reconstructions. We investigate two scenarios, one in which the layers are sent sequentially, and incrementally contribute to the refinement of a reconstruction, and another in which the layers are independent and can be retrieved in any order. Those scenarios correspond to the well known problems of \textit{successive refinement} and \textit{multiple descriptions}, respectively, in the context of joint source-channel coding (JSCC). We propose DeepJSCC-$l$, an innovative solution that uses convolutional autoencoders, and present three architectures with different complexity trade-offs. To the best of our knowledge, this is the first practical multiple-description JSCC scheme developed and tested for practical information sources and channels. Numerical results show that DeepJSCC-$l$ can learn to transmit the source progressively with negligible losses in the end-to-end performance compared with a single transmission. Moreover, DeepJSCC-$l$ has comparable performance with state of the art digital progressive transmission schemes in the challenging low signal-to-noise ratio (SNR) and small bandwidth regimes, with the additional advantage of graceful degradation with channel SNR.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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