Abstract With the rapid development of the Internet of Video Things and the explosive growth of video data, the resulting pressure on storage and transmission has made video compression increasingly important. Conventional codecs lack sufficient content adaptivity, while recent learned compression methods often incur high deployment cost. To address this challenge, this paper proposes a novel visual large model-enhanced soft actor-critic framework (VLM-SAC) for content-aware adaptive video compression. The proposed VLM-SAC formulates video compression as a quality-constrained bandwidth minimization problem and exploits the semantic capability derived from the VLM to guide lightweight state representation learning for efficient compression decision-making. Based on this content-aware state, a soft actor-critic policy is trained to jointly optimize bitrate reduction and quality preservation. Experiments show that VLM-SAC achieves competitive bandwidth efficiency and compression performance, while maintaining substantially lower deployment overhead and better practical deployability than heavy learned codecs.
Wei et al. (Tue,) studied this question.
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