{"ID":2860591,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.03812","arxiv_id":"2510.03812","title":"ReTiDe: Real-Time Denoising for Energy-Efficient Motion Picture Processing with FPGAs","abstract":"Denoising is a core operation in modern video pipelines. In codecs, in-loop filters suppress sensor noise and quantisation artefacts to improve rate-distortion performance; in cinema post-production, denoisers are used for restoration, grain management, and plate clean-up. However, state-of-the-art deep denoisers are computationally intensive and, at scale, are typically deployed on GPUs, incurring high power and cost for real-time, high-resolution streams. This paper presents Real-Time Denoise (ReTiDe), a hardware-accelerated denoising system that serves inference on data-centre Field Programmable Gate Arrays (FPGAs). A compact convolutional model is quantised (post-training quantisation plus quantisation-aware fine-tuning) to INT8 and compiled for AMD Deep Learning Processor Unit (DPU)-based FPGAs. A client-server integration offloads computation from the host CPU/GPU to a networked FPGA service, while remaining callable from existing workflows, e.g., NUKE, without disrupting artist tooling. On representative benchmarks, ReTiDe delivers 37.71$\\times$ Giga Operations Per Second (GOPS) throughput and 5.29$\\times$ higher energy efficiency than prior FPGA denoising accelerators, with negligible degradation in Peak Signal-to-Noise Ratio (PSNR)/Structural Similarity Index (SSIM). These results indicate that specialised accelerators can provide practical, scalable denoising for both encoding pipelines and post-production, reducing energy per frame without sacrificing quality or workflow compatibility. Code is available at https://github.com/RCSL-TCD/ReTiDe.","short_abstract":"Denoising is a core operation in modern video pipelines. In codecs, in-loop filters suppress sensor noise and quantisation artefacts to improve rate-distortion performance; in cinema post-production, denoisers are used for restoration, grain management, and plate clean-up. However, state-of-the-art deep denoisers are c...","url_abs":"https://arxiv.org/abs/2510.03812","url_pdf":"https://arxiv.org/pdf/2510.03812v1","authors":"[\"Changhong Li\",\"Clément Bled\",\"Rosa Fernandez\",\"Shreejith Shanker\"]","published":"2025-10-04T13:43:43Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":608743,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2860591,"paper_url":"https://arxiv.org/abs/2510.03812","paper_title":"ReTiDe: Real-Time Denoising for Energy-Efficient Motion Picture Processing with FPGAs","repo_url":"https://github.com/RCSL-TCD/ReTiDe","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}