Reinforcement Learning-Based Layered Lossy Image Semantic Coding

doi:10.3390/electronics14101986

Reinforcement Learning-Based Layered Lossy Image Semantic Coding

2025-05-13

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DOI：10.3390/electronics14101986

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Semantic communication has garnered increasing attention due to its ability to shift the focus from pixel-level transmission to the transfer of fundamental semantic information representing the core content of images. This paper proposes a novel reinforcement learning-based layered semantic coding (RL-SC) method aimed at optimizing image communication systems under bandwidth constraints. By leveraging deep reinforcement learning (DRL), the proposed method efficiently allocates semantic bits to maximize semantic fidelity while minimizing bitrates. By incorporating semantic segmentation and image reconstruction networks, the framework utilizes both semantic maps and residual information to enhance the image coding process. Experiments demonstrate that the proposed method outperforms traditional image compression techniques and layered image encoding methods without reinforcement learning in preserving semantic content and achieving high-quality image reconstruction. In particular, the proposed method excels in low-bitrate scenarios, effectively maintaining both semantic accuracy and perceptual quality.

Keywords:

semantic communication

reinforcement learning (RL)

image semantic coding

generative adversarial networks (GANs)

Reinforcement Learning-Based Layered Lossy Image Semantic Coding

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