Generative AI for Multimedia Communication: Recent Advances, An Information-Theoretic Framework, and Future Opportunities
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915460305387520 |
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| author | Jin, Yili Liu, Xue Liu, Jiangchuan |
| author_facet | Jin, Yili Liu, Xue Liu, Jiangchuan |
| contents | Recent breakthroughs in generative artificial intelligence (AI) are transforming multimedia communication. This paper systematically reviews key recent advancements across generative AI for multimedia communication, emphasizing transformative models like diffusion and transformers. However, conventional information-theoretic frameworks fail to address semantic fidelity, critical to human perception. We propose an innovative semantic information-theoretic framework, introducing semantic entropy, mutual information, channel capacity, and rate-distortion concepts specifically adapted to multimedia applications. This framework redefines multimedia communication from purely syntactic data transmission to semantic information conveyance. We further highlight future opportunities and critical research directions. We chart a path toward robust, efficient, and semantically meaningful multimedia communication systems by bridging generative AI innovations with information theory. This exploratory paper aims to inspire a semantic-first paradigm shift, offering a fresh perspective with significant implications for future multimedia research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17163 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative AI for Multimedia Communication: Recent Advances, An Information-Theoretic Framework, and Future Opportunities Jin, Yili Liu, Xue Liu, Jiangchuan Multimedia Image and Video Processing Recent breakthroughs in generative artificial intelligence (AI) are transforming multimedia communication. This paper systematically reviews key recent advancements across generative AI for multimedia communication, emphasizing transformative models like diffusion and transformers. However, conventional information-theoretic frameworks fail to address semantic fidelity, critical to human perception. We propose an innovative semantic information-theoretic framework, introducing semantic entropy, mutual information, channel capacity, and rate-distortion concepts specifically adapted to multimedia applications. This framework redefines multimedia communication from purely syntactic data transmission to semantic information conveyance. We further highlight future opportunities and critical research directions. We chart a path toward robust, efficient, and semantically meaningful multimedia communication systems by bridging generative AI innovations with information theory. This exploratory paper aims to inspire a semantic-first paradigm shift, offering a fresh perspective with significant implications for future multimedia research. |
| title | Generative AI for Multimedia Communication: Recent Advances, An Information-Theoretic Framework, and Future Opportunities |
| topic | Multimedia Image and Video Processing |
| url | https://arxiv.org/abs/2508.17163 |