  Vector quantization-based image semantic communication systems have
successfully boosted transmission efficiency, but face a challenge with
conflicting requirements between codebook design and digital constellation
modulation. Traditional codebooks need a wide index range, while modulation
favors few discrete states. To address this, we propose a multilevel generative
semantic communication system with a two-stage training framework. In the first
stage, we train a high-quality codebook, using a multi-head octonary codebook
(MOC) to compress the index range. We also integrate a residual vector
quantization (RVQ) mechanism for effective multilevel communication. In the
second stage, a noise reduction block (NRB) based on Swin Transformer is
introduced, coupled with the multilevel codebook from the first stage, serving
as a high-quality semantic knowledge base (SKB) for generative feature
restoration. Experimental results highlight MOC-RVQ's superior performance over
methods like BPG or JPEG, even without channel error correction coding.
