Despite deep learning's progress in semantic communication, traditional fixed-length encoding does not adequately address the variable complexity of semantic content, often leading to loss of critical nuances and ...
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Despite deep learning's progress in semantic communication, traditional fixed-length encoding does not adequately address the variable complexity of semantic content, often leading to loss of critical nuances and reduced communication accuracy. Current methods also introduce unnecessary redundancy, compromising transmission efficiency. Addressing these challenges, our work introduces an innovative adaptive rate encoding mechanism that captures the intrinsic semantics of images and fine-tunes the coding rate based on semantic interconnection probability. We employ a cross-attention model to construct a layered semantic probability graph parsed into a hierarchical semantictree, which represents the probabilistic relationships of image semantics and unravels the latent semantic structure. This not only delineates the image's semantic architecture but also enables our adaptive encoding to dynamically allocate resources, minimizing redundancy and enhancing efficiency. Our experiments confirm that our approach provides a more judicious bit allocation to complex image features and allocates more bits to semantically rich features while achieving superior compression of simpler content. The proposed method not only improves upon existing semantic fidelity metrics but also reduces the bit demand for transmitting complex images. Our adaptive encoding strategy represents a significant stride in leveraging the endogenous semantic information of images for more accurate and efficient communication.
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