  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.
