基于激活图的矢量量化用于360度图像语义通信
Activation Map-based Vector Quantization for 360-degree Image Semantic Communication
AI总结：
本文提出基于激活图的矢量量化（AM-VQ）框架，利用深度神经网络与矢量量化提取压缩360度图像语义特征，并结合GAN对抗训练，在相同传输符号下优于现有深度学习和传统编码方案。
摘要：
在虚拟现实（VR）应用中，360度图像在构建沉浸式体验和提供全景视野方面发挥着关键作用，从而提升用户体验质量（QoE）。然而，360度图像产生的海量数据给网络存储和带宽带来了挑战。为应对这些挑战，我们提出了一种新颖的基于激活图的矢量量化（AM-VQ）框架，旨在降低无线传输的通信开销。所提出的AM-VQ方案利用带有矢量量化（VQ）的深度神经网络（DNNs）来提取和压缩语义特征。特别地，AM-VQ框架利用激活图自适应地量化语义特征，从而减少由量化操作引起的数据失真。为进一步提升360度图像的重建质量，引入了带有生成对抗网络（GANs）判别器的对抗训练。数值结果表明，在相同传输符号下，我们提出的AM-VQ方案比现有的基于深度学习（DL）的编码方案和传统编码方案取得了更好的性能。
英文摘要：
In virtual reality (VR) applications, 360-degree images play a pivotal role in crafting immersive experiences and offering panoramic views, thus improving user Quality of Experience (QoE). However, the voluminous data generated by 360-degree images poses challenges in network storage and bandwidth. To address these challenges, we propose a novel Activation Map-based Vector Quantization (AM-VQ) framework, which is designed to reduce communication overhead for wireless transmission. The proposed AM-VQ scheme uses the Deep Neural Networks (DNNs) with vector quantization (VQ) to extract and compress semantic features. Particularly, the AM-VQ framework utilizes activation map to adaptively quantize semantic features, thus reducing data distortion caused by quantization operation. To further enhance the reconstruction quality of the 360-degree image, adversarial training with a Generative Adversarial Networks (GANs) discriminator is incorporated. Numerical results show that our proposed AM-VQ scheme achieves better performance than the existing Deep Learning (DL) based coding and the traditional coding schemes under the same transmission symbols.
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。
