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A brief review of the history of encoding ASICs reveals why they have become the technology of choice for high-volume video streaming services and cloud-gaming platforms.
Google, Meta, NETINT- ASICs technology for video transcoding
If you’ve made it past the title, you know that cloud gaming platforms operate in a highly competitive environment with narrow margins. This makes the purchase and operating costs per stream critical elements to system success.
Much of the pause for codecs like HEVC and VVC relates to the threat of patent royalties. In reality, content royalties generally apply in only a few well-defined cases. This reality hasn’t stopped video publishers from delaying the widespread use of HEVC or journalists from fawning and gushing over the supposed “open source and royalty-free” AV1 standard.
For years, H.264 has remained dominant because it plays everywhere; but as videos grow larger, faster, and deeper in color, cost of distributing H.264 has become too high. AV1 has leap-frogged VP9 in the so-called “open-source” horse race, while HEVC is the clear successor to H.264 in standards-based codecs, at least for the next 3-4 years as VVC slowly matures. AV1 and HEVC have had their well-known Achilles heels, AV1 in the living room and on Apple devices, and HEVC in browsers. The last few months have seen critical movement and new data in all these platforms that will fundamentally change how we use them.
As cloud gaming use cases expand, we are studying even more ways to deliver high-quality video with low latency and efficient bitrates. Region of Interest Encoding (ROI) is one way to enhance video quality while reducing bandwidth. This post will discuss three ROI-based techniques recently proposed in research papers that may soon be adopted in cloud gaming encoding workflows.
This article will introduce you to the NETINT product line and Codensity ASIC generations. We will focus primarily on the hardware differences since all products share a common software architecture and feature set, which are briefly described at the end of the article.
The thing about FFmpeg is that there are almost always multiple ways to accomplish the same basic function. In this post, we look at four approaches to scaling to reveal how the scaling method and techniques used impact quality and throughput.
The intersection of video processing and artificial intelligence (AI) delivers exciting new functionality, from real-time quality enhancement for video publishers to object detection and optical character recognition for security applications. One key feature in NETINT’s Quadra Video Processing Units are two onboard Neural Processing Units (NPUs). Combined with Quadra’s integrated decoding, scaling, and transcoding hardware, this creates an integrated AI and video processing architecture that requires minimal interaction from the host CPU. As you’ll learn in this post, this architecture makes Quadra the ideal platform for executing video-related AI applications. This post introduces the reader to what AI is, how it works, and how you deploy AI applications on NETINT Quadra. Along the way, we’ll explore one Quadra-supported AI application, Region of Interest (ROI) encoding.
ASICs provide tremendous energy efficiency and yet suffer from being fixed-function with limited programmability. This was a core engineering challenge that we addressed in the development of the Codensity ASIC family with upgradeable firmware that can be used for a variety of purposes, including adding new features and improving coding performance and functionality.
