论文标题
使用深关节通道编码的分布式图像传输
Distributed Image Transmission using Deep Joint Source-Channel Coding
论文作者
论文摘要
我们研究了相关图像源的深关节源通道编码（D-JSCC）的问题，其中每个源都通过嘈杂的独立通道传输到公共接收器。特别是，我们考虑了一对由两个摄像机捕获的图像，这些摄像机可能会通过无线通道传输并在中心节点中重建的视图重叠字段。具有挑战性的问题涉及设计实用的代码以同时利用源和通道相关性，以提高传输效率，而无需额外的传输开销。为了解决这个问题，我们需要考虑两个立体声图像上的常见信息以及两个传输通道之间的差异。在这种情况下，我们提出了一个深层神经网络解决方案，其中包括轻质边缘编码器和强大的中心解码器。此外，在解码器中，我们提出了一个新颖的频道状态信息，即“意识到交叉注意”模块，以突出重叠字段并利用两个嘈杂的特征映射之间的相关性。我们的结果表明，通过利用其他链接的嘈杂表示，在这两种链接中的重建质量都令人印象深刻。此外，与具有能力实现的通道代码的分离方案相比，提出的方案显示出竞争性结果。
We study the problem of deep joint source-channel coding (D-JSCC) for correlated image sources, where each source is transmitted through a noisy independent channel to the common receiver. In particular, we consider a pair of images captured by two cameras with probably overlapping fields of view transmitted over wireless channels and reconstructed in the center node. The challenging problem involves designing a practical code to utilize both source and channel correlations to improve transmission efficiency without additional transmission overhead. To tackle this, we need to consider the common information across two stereo images as well as the differences between two transmission channels. In this case, we propose a deep neural networks solution that includes lightweight edge encoders and a powerful center decoder. Besides, in the decoder, we propose a novel channel state information aware cross attention module to highlight the overlapping fields and leverage the relevance between two noisy feature maps.Our results show the impressive improvement of reconstruction quality in both links by exploiting the noisy representations of the other link. Moreover, the proposed scheme shows competitive results compared to the separated schemes with capacity-achieving channel codes.
