PECV-RT: Real-Time Perceptual Enhancement of Compressed Video

Perceptual Enhancement of Compressed Video (PECV) methods have achieved substantial success. However, most methods suffer from high computational complexity, thereby limiting their deployment in real-time scenarios. To address this issue, we present PECV-RT, a lightweight framework that focuses on three bottlenecks in prior work: suboptimal computation allocation across resolutions, costly global spatial context modeling, and redundant computation across overlapping temporal windows. Resolution-aware computation allocation optimizes the distribution of computation by shifting intensive global context modeling to low resolutions, while making local processing lightweight at high resolutions. Linear spatial attention reduces computational complexity from quadratic to linear in the number of spatial tokens. Cross-frame computation sharing limits repeated feature extraction and reuses temporal-attention keys and values across output frames. Experiments show that PECV-RT achieves state-of-the-art LPIPS performance with negligible PSNR degradation. PECV-RT enhances 1080p compressed video at 102 FPS on a consumer-grade GPU, delivering 15.7× the throughput of the fastest evaluated baseline. To our knowledge, it is the first method to perform perceptual enhancement of 1080p compressed video at over 60 FPS on such hardware.