Image Generative Semantic Communication with Multi-Modal Similarity Estimation for Resource-Limited Networks

To reduce network traffic and support environments with limited resources, a method for transmitting images with low amounts of transmission data is required. Machine learning-based image compression methods, which compress the data size of images while maintaining their features, have been proposed. However, in certain situations, reconstructing a part of semantic information of images at the receiver end may be sufficient. To realize this concept, semantic-information-based communication, called semantic communication, has been proposed, along with an image transmission method using semantic communication. This method transmits only the semantic information of an image, and the receiver reconstructs the image using an image-generation model. This method utilizes one type of semantic information, but reconstructing images similar to the original image using only it is challenging. This study proposes a multi-modal image transmission method that leverages diverse semantic information for efficient semantic communication. The proposed method extracts multi-modal semantic information from an image and transmits only it. Subsequently, the receiver generates multiple images using an image-generation model and selects an output based on semantic similarity. The receiver must select the output based only on the received features; however, evaluating semantic similarity using conventional metrics is challenging. Therefore, this study explored new metrics to evaluate the similarity between semantic features of images and proposes two scoring procedures. The results indicate that the proposed procedures can compare semantic similarities, such as position and composition, between semantic features of the original and generated images. Thus, the proposed method can facilitate the transmission and utilization of photographs through mobile networks for various service applications.