Rapid development and deployment of GPU based computation has led to an improvement in diffusion generation of video and images. Further, a rapid reduction in the effective cost of compression using NNC techniques provides opportunities to compress images and videos in new ways. The overall structure of diffusion based generative video and images is leveraged to take advantage of the compressed latent to lower overall compression costs and latency. This paper presents an architecture to compress a latent for transmission and reduce overall latency and cost as compared to alternatives using traditional Codecs or NNC on the raw image. It explores a proof of concept based on image compression of a latent. It further presents computational cost, quantitative and perceptual quality, and latency for this architecture as compared to the alternatives.
KEYWORDS: Video, Video processing, Video acceleration, Video coding, Video compression, Artificial intelligence, Video surveillance, Super resolution, Image enhancement, RGB color model
Purpose-built silicon for hyper-scaled video platforms is becoming mainstream as developers move beyond common video IP cores and commodity chip designs. A new generation of video processing units (VPUs) powered by Application Specific Integrated Circuits (ASICs) combine the essential encoding, decoding, and transcoding functionality with an on-chip deep neural network engine for AI and ML framework integration. This paper explores the transformative impact of custom ASICs on data center video processing, exploring their pivotal role in meeting the ever-evolving demands of this dynamic landscape. We will discuss design trade-offs for data center workloads using VPUs that involve multiple priorities, such as improving video quality while maintaining low bitrates using AI and ML applications enabled by silicon-powered video encoding and processing stacks. The paper will also showcase practical applications of AI and ML that are currently infeasible using software alone.
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