  Recent semantic communication methods explore effective ways to expand the
communication paradigm and improve the system performance of the communication
systems. Nonetheless, the common problem of these methods is that the essence
of semantics is not explicitly pointed out and directly utilized. A new
epistemology suggests that synonymy, which is revealed as the fundamental
feature of semantics, guides the establishment of the semantic information
theory from a novel viewpoint. Building on this theoretical basis, this paper
proposes a semantic arithmetic coding (SAC) method for semantic lossless
compression using intuitive semantic synonymy. By constructing reasonable
synonymous mappings and performing arithmetic coding procedures over synonymous
sets, SAC can achieve higher compression efficiency for meaning-contained
source sequences at the semantic level and thereby approximate the semantic
entropy limits. Experimental results on edge texture map compression show an
evident improvement in coding efficiency using SAC without semantic losses,
compared to traditional arithmetic coding, which demonstrates its
effectiveness.
