In Video on Demand (VoD) scenarios, traditional codecs are the industry standard due to their high decoding efficiency. However, they suffer from severe quality degradation under low bandwidth conditions. While emerging generative neural codecs offer significantly higher perceptual quality, their reliance on heavy frame-by-frame generation makes real-time playback on mobile devices impractical. We ask: is it possible to combine the blazing-fast speed of traditional standards with the superior visual fidelity of neural approaches? We present HybridPrompt, the first generative-based video system capable of achieving real-time 1080p decoding at over 150 FPS on a commercial smartphone. Specifically, we employ a hybrid architecture that encodes Keyframes using a generative model while relying on traditional codecs for the remaining frames. A major challenge is that the two paradigms have conflicting objectives: the "hallucinated" details from generative models often misalign with the rigid prediction mechanisms of traditional codecs, causing bitrate inefficiency. To address this, we demonstrate that the traditional decoding process is differentiable, enabling an end-to-end optimization loop. This allows us to use subsequent frames as additional supervision, forcing the generative model to synthesize keyframes that are not only perceptually high-fidelity but also mathematically optimal references for the traditional codec. By integrating a two-stage generation strategy, our system outperforms pure neural baselines by orders of magnitude in speed while achieving an average LPIPS gain of 8% over traditional codecs at 200kbps.

翻译：在视频点播（VoD）场景中，传统编解码器因其高效的解码性能已成为行业标准。然而，在低带宽条件下，其视频质量会出现严重劣化。虽然新兴的生成式神经编解码器能提供显著更高的感知质量，但其依赖逐帧进行繁重生成的计算模式，导致在移动设备上难以实现实时播放。我们提出核心问题：能否将传统标准的高速解码优势与神经方法的卓越视觉保真度相结合？本文提出HybridPrompt——首个基于生成模型的视频系统，可在商用智能手机上实现超过150 FPS的实时1080p视频解码。具体而言，我们采用混合架构：使用生成模型编码关键帧，其余帧则依赖传统编解码器处理。主要挑战在于两种范式存在目标冲突：生成模型"幻觉"产生的细节常与传统编解码器的刚性预测机制不匹配，导致码率效率下降。为解决该问题，我们证明了传统解码过程具有可微性，从而构建了端到端优化循环。这使得系统可利用后续帧作为额外监督信号，迫使生成模型合成的关键帧不仅具备高感知保真度，同时能成为传统编解码器在数学意义上的最优参考帧。通过集成两阶段生成策略，本系统在速度上较纯神经基线方法提升数个数量级，并在200kbps码率下相比传统编解码器平均获得8%的LPIPS指标增益。