We are happy that our paper titled “x265-PVMAF: A Real-Time Perceptual Video Quality Metric for HEVC Video Encoding” was accepted at the 2025 IEEE International Conference on Image Processing (ICIP).
This work was done in collaboration with Synamedia, and was performed in the context of the PhD of Axel De Decker.
Real-time video encoding requires efficient and accurate quality metrics to optimize performance under strict computational and latency constraints. Traditional low-complexity metrics such as PSNR and SSIM often fall short in perceptual alignment, while accurate metrics such as VMAF are too computationally intensive for real-time deployment.We present x265-pVMAF, a low-complexity perceptual quality metric integrated into the x265 encoding loop. By leveraging machine learning and efficiently extracted encoder features, x265-pVMAF bridges the gap between computational efficiency and perceptual accuracy. It replicates VMAF predictions with a correlation of 0.99, while delivering a 37x speed-up. These results establish x265-pVMAF as a practical solution for real-time video quality assessment in next-generation encoding workflows.
The source code of x265-pVMAF is available on GitHub.com.
