Scalable AI-Powered Content-Adaptive Encoding for Next-Gen Video Delivery

Content-Adaptive Encoding (CAE) has emerged as a practical method for reducing bitrate while preserving or improving visual quality. This paper introduces a machine learning-based CAE approach developed by VisualOn, which employs single-pass prediction and real-time adaptation to dynamically optimize encoding parameters. Benchmark results across AVC, HEVC, AV1, and multiple hardware platforms demonstrate average bitrate reductions between 20% and 50%, with consistent quality improvements measured by VMAF. Case studies confirm bitrate savings in production environments, alongside reduced startup delay and buffering. Tests on Intel Xeon processors, Intel Data Center GPU Flex Series, NVIDIA NVENC, Qualcomm ARM devices, and ASIC encoders suggest that the approach is broadly applicable across diverse platforms.

Published

2025-10-13

Content type

Original Research

Keywords

content-adaptive encoding, machine learning, video compression, vmaf, ffmpeg, intel xeon, live streaming, vod

ISBN

978-1-61482-966-9