{"ID":2871717,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.10061","arxiv_id":"2509.10061","title":"Semantic Rate-Distortion Theory with Applications","abstract":"Artificial intelligence (AI) is ushering in a new era for communication. As a result, the establishment of a semantic communication framework is putting on the agenda. Based on a realistic semantic communication model, this paper develops a rate-distortion framework for semantic compression. Different from the existing works primarily focusing on decoder-side estimation of intrinsic meaning and ignoring its inherent issues, such as ambiguity and polysemy, we exploit a constraint of conditional semantic probability distortion to effectively capture the essential features of practical semantic exchanges in an AI-assisted communication system. With the help of the methods in rate-distortion-perception theory, we establish a theorem specifying the minimum achievable rate under this semantic constraint and a traditional symbolic constraint and obtain its closed-form limit for a particular semantic scenario. From the experiments in this paper, bounding conditional semantic probability distortion can effectively improve both semantic transmission accuracy and bit-rate efficiency. Our framework bridges information theory and AI, enabling potential applications in bandwidth-efficient semantic-aware networks, enhanced transceiver understanding, and optimized semantic transmission for AI-driven systems.","short_abstract":"Artificial intelligence (AI) is ushering in a new era for communication. As a result, the establishment of a semantic communication framework is putting on the agenda. Based on a realistic semantic communication model, this paper develops a rate-distortion framework for semantic compression. Different from the existing...","url_abs":"https://arxiv.org/abs/2509.10061","url_pdf":"https://arxiv.org/pdf/2509.10061v1","authors":"[\"Yi-Qun Zhao\",\"Zhi-Ming Ma\",\"Geoffrey Ye Li\",\"Shuai Yuan\",\"Tong Ye\",\"Chuan Zhou\"]","published":"2025-09-12T08:48:47Z","proceeding":"cs.IT","tasks":"[\"cs.IT\",\"eess.SP\"]","methods":"[]","has_code":false}