  Semantic communication is focused on optimizing the exchange of information
by transmitting only the most relevant data required to convey the intended
message to the receiver and achieve the desired communication goal. For
example, if we consider images as the information and the goal of the
communication is object detection at the receiver side, the semantic of
information would be the objects in each image. Therefore, by only transferring
the semantics of images we can achieve the communication goal. In this paper,
we propose a design framework for implementing semantic-aware and goal-oriented
communication of images. To achieve this, we first define the baseline problem
as a set of mathematical problems that can be optimized to improve the
efficiency and effectiveness of the communication system. We consider two
scenarios in which either the data rate or the error at the receiver is the
limiting constraint. Our proposed system model and solution is inspired by the
concept of auto-encoders, where the encoder and the decoder are respectively
implemented at the transmitter and receiver to extract semantic information for
specific object detection goals. Our numerical results validate the proposed
design framework to achieve low error or near-optimal in a goal-oriented
communication system while reducing the amount of data transfers.
