Year: 2025
Type: conference-paper
Abstract: 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 37× speed-up. These results establish x265-pVMAF as a practical solution for real-time video quality assessment in next-generation encoding workflows.
Institutions: Ghent University, IMEC
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4413278401