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Advanced AI and new compression standards are needed to improve the viewing experience and reduce service costs, but the explosion in computational complexity is a significant barrier to adoption. This paper proposes AI algorithms and corresponding hardware accelerators for super-resolution and perceptual quality optimization. Super-resolution is for video upscaling and visual quality enhancement, while perceptual quality optimization is a pre-process to improve the coding efficiency of the encoders. Video ASICs for data centers include hardware decoders and encoders with high throughput to handle large amounts of data. The proposed algorithm is designed with dedicated hardware accelerators to maximize the efficiency of on-chip resources. These advances are essential to balancing high-quality streaming services with operational efficiency.
Conference Presentation
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Ungwon Lee,Jungtae Kim,Sijung Kim,Dong-gyu Kim, andMinyong Jeon
"Hardware accelerators for AI-based video enhancement and optimization in video ASICs for data center", Proc. SPIE 13137, Applications of Digital Image Processing XLVII, 1313710 (30 September 2024); https://doi.org/10.1117/12.3031655
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Ungwon Lee, Jungtae Kim, Sijung Kim, Dong-gyu Kim, Minyong Jeon, "Hardware accelerators for AI-based video enhancement and optimization in video ASICs for data center," Proc. SPIE 13137, Applications of Digital Image Processing XLVII, 1313710 (30 September 2024); https://doi.org/10.1117/12.3031655