MAIP: A Multi-Attribute Informativeness Proxy for Image Semantic Broadcasting Communication

In the image semantic broadcasting communication system, the resources of the channel are limited, which restricts the transmission and broadcasting of large-scale image data. This paper proposed a deep learning assisted image semantic broadcasting scheme to improve source efficiency and alleviate communication resource pressure at the transmission terminal. We adopt an image informativeness evaluation method to screen high information image data and implement this data-driven source optimization scheme. Specifically, we propose a Multi Attribute Information Proxy (MAIP) method that integrates fine-grained information attributes such as uncertainty, novelty, and diversity to evaluate and screen image semantic broadcast data. Used to support the formation of optimal image data broadcast transmission strategies. To demonstrate the effectiveness of the proposed MAIP, we compared it with state-of-the-art over three benchmarks CIFAR-10, mini ImageNet and Fashion Minst based on active learning as a validation experiment.