Reduce H.265 High-Res Encoding Costs by over 80% with AWS Graviton2
The demand for high-resolution, high-definition video content is exploding. Growth in camera resolution, the size of devices (including smartphones, tablets and TVs), and in network bandwidth drives this demand. To save bandwidth and storage space, these video streams are often compressed using newer codecs like H.265. And while more efficient at compression, these codecs require significantly higher compute resources. This paper describes the work done by Videolan/FFlabs and AWS teams to optimize video encode processing for H.265 on Arm-based server platforms in the cloud.
Background
Over the last few years, there has been a steady growth in both generation and consumption of high-resolution content. Better device cameras and higher-resolution screens for viewing content has driven this growth. Newer codecs like H.265/HEVC, VP9 or AV1 are more than 50% efficient at compressing such higher-resolution content compared to legacy codecs like H.264, as table 1 shows.
To read the full article, click here
Related Semiconductor IP
- H.265 - Efficient video compression for high-quality, low-bandwidth streaming
- H.265 Codec - Efficiently compresses high-quality video for streaming and storage
- H265 ENCODER IIP
- H265 DECODER IIP
- H.265 HEVC Decoder
Related Blogs
- Designing Arm Cortex-M55 CPU on Arm Neoverse powered AWS Graviton2 Processors
- Ampere Altra Max Delivers Sustainable High-Resolution H.265 Encoding
- Arm-based Cloud Instances Outperform x86 Instances by up to 64% on VP9 Encoding
- EDA in the Cloud: Astera Labs, AWS, Arm, and Cadence Report
Latest Blogs
- Rivos and Canonical partner to deliver scalable RISC-V solutions in Data Centers and enable an enterprise-grade Ubuntu experience across Rivos platforms
- ReRAM-Powered Edge AI: A Game-Changer for Energy Efficiency, Cost, and Security
- Ceva-XC21 and Ceva-XC23 DSPs: Advancing Wireless and Edge AI Processing
- Cadence Silicon Success of UCIe IP on Samsung Foundry’s 5nm Automotive Process
- Empowering your Embedded AI with 22FDX+