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ABSTRACT The proliferation of unmanned aerial vehicles (UAVs) in the low‐altitude economy has driven a surge in demand for high‐fidelity video transmission; however, existing video codec methods struggle to balance the conflicting requirements of resource‐constrained onboard platforms and high‐quality downstream visual perception. While conventional macroscopic codecs offer computational efficiency, they suffer from artefacts and bandwidth congestion in complex UAV environments. Conversely, microscopic neural video compression (NVC) models achieve superior rate‐distortion performance but incur prohibitive computational overhead. To reconcile these limitations, we propose the Generative Content‐steering Video Codec (GCVC), a novel framework designed from a mesoscopic perspective. GCVC synergises lightweight architectural design with generative semantic recovery by integrating streamlined encoding with a Stable Diffusion‐based (SD‐based) frame predictor. By employing a strategic frame‐skipping protocol to alleviate transmission bottlenecks and utilising the generative prior to synthesise missing scenes, our framework achieves state‐of‐the‐art reconstruction fidelity at significantly lower bitrates. Extensive experiments on public and self‐collected UAV datasets demonstrate that GCVC outperforms both traditional standards and cutting‐edge NVC approaches, establishing a new benchmark for efficient, perception‐aware video compression in intelligent UAV systems. The implementation codes will be publicly available at https://github.com/ZSTU‐CV‐Lab/GCVC .
Wen et al. (Wed,) studied this question.