As video content evolves from HD to higher resolutions like 4K and 8K, storage and processing demands increase significantly. Qencode and Wasabi offer integrated solutions for efficient video workflows, combining automated encoding with transparent storage costs. This partnership addresses the complexities of modern video demands, ensuring scalability and cost-effectiveness.

This content outlines the significant cost savings achievable through machine learning (ML) powered encoding for video delivery. By applying efficient encoding techniques, platforms can reduce their delivery costs over 24 months by up to 7 million dollars compared to fixed methods. Ignoring encoding efficiency can lead to escalating expenses as content volume grows.

Qencode leverages NVIDIA architecture to enhance machine learning video compression, addressing inefficiencies in traditional encoding practices. By analyzing scenes across multiple dimensions, their approach reduces file sizes by an average of 60% without compromising quality. This strategic deployment of GPU resources supports scalable customer demands and optimizes cost-effectiveness in video delivery.

If you’ve been curious about AV1, but indecisive about committing to adding it to your workflow, you’re in the same spot as a lot of good teams. You’ve probably seen AV1 benchmarks for years. You know the theory: better compression than H.264 and H.265, cleaner artifacts at low bitrate, better long‑term fit for 4K/8K. But […]