  Semantic communications, a promising approach for agent-human and agent-agent
interactions, typically operate at a feature level, lacking true semantic
understanding. This paper explores understanding-level semantic communications
(ULSC), transforming visual data into human-intelligible semantic content. We
employ an image caption neural network (ICNN) to derive semantic
representations from visual data, expressed as natural language descriptions.
These are further refined using a pre-trained large language model (LLM) for
importance quantification and semantic error correction. The subsequent
semantic importance-aware communications (SIAC) aim to minimize semantic loss
while respecting transmission delay constraints, exemplified through adaptive
modulation and coding strategies. At the receiving end, LLM-based semantic
error correction is utilized. If visual data recreation is desired, a
pre-trained generative artificial intelligence (AI) model can regenerate it
using the corrected descriptions. We assess semantic similarities between
transmitted and recovered content, demonstrating ULSC's superior ability to
convey semantic understanding compared to feature-level semantic communications
(FLSC). ULSC's conversion of visual data to natural language facilitates
various cognitive tasks, leveraging human knowledge bases. Additionally, this
method enhances privacy, as neither original data nor features are directly
transmitted.
