  To reduce network traffic and support environments with limited resources, a
method for transmitting images with minimal transmission data is required.
Several 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 only the 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 it using an image-generation model. This method
utilizes a single type of semantic information for image reconstruction, but
reconstructing images similar to the original image using only this information
is challenging. This study proposes a multi-modal image transmission method
that leverages various types of semantic information for efficient semantic
communication. The proposed method extracts multi-modal semantic information
from an original image and transmits only that to a receiver. Subsequently, the
receiver generates multiple images using an image-generation model and selects
an output image based on semantic similarity. The receiver must select the
result based only on the received features; however, evaluating the similarity
using conventional metrics is challenging. Therefore, this study explores new
metrics to evaluate the similarity between semantic features of images and
proposes two scoring procedures for evaluating semantic similarity between
images based on multiple semantic features. The results indicate that the
proposed procedures can compare semantic similarities, such as position and
composition, between the semantic features of the original and generated
images.
