Wireless Image Transmission for Computer Vision Task
With the integration and innovation of information technology and the rapid development of smart cities and other fields,the requirements of machine vision tasks and image data transmission is gradually increasing.Semantic communication technology improves data transmission efficiency by extracting and transmitting semantic information of the data instead of the original bit stream.The combination of CNN(Convolutional Neural Network)and Transformer structure brings new breakthroughs in the field of image semantic communication.CNN excels at extracting local features from images,while Transformer excels at capturing long-distance dependencies and global features.This paper focuses on the transmission and processing of images,based on the advantages of CNN and Transformer structures for feature extraction and incorporating the attention mechanism,it proposes an efficient end-to-end image semantic communication scheme to meet the requirements of intelligent tasks for image data transmission while optimizing the transmission performance.Experimental results indicate that the proposed scheme not only improves the computational efficiency but also has stronger robustness and adaptability compared with the existing methods.
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