LiteVPNet: A Lightweight Network for Video Encoding Control in Quality-Critical Applications
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| Format: | Preprint |
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2025
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| author | Vibhoothi, Vibhoothi Pitié, François Kokaram, Anil |
| author_facet | Vibhoothi, Vibhoothi Pitié, François Kokaram, Anil |
| contents | In the last decade, video workflows in the cinema production ecosystem have presented new use cases for video streaming technology. These new workflows, e.g. in On-set Virtual Production, present the challenge of requiring precise quality control and energy efficiency. Existing approaches to transcoding often fall short of these requirements, either due to a lack of quality control or computational overhead. To fill this gap, we present a lightweight neural network (LiteVPNet) for accurately predicting Quantisation Parameters for NVENC AV1 encoders that achieve a specified VMAF score. We use low-complexity features, including bitstream characteristics, video complexity measures, and CLIP-based semantic embeddings. Our results demonstrate that LiteVPNet achieves mean VMAF errors below 1.2 points across a wide range of quality targets. Notably, LiteVPNet achieves VMAF errors within 2 points for over 87% of our test corpus, c.f. approx 61% with state-of-the-art methods. LiteVPNet's performance across various quality regions highlights its applicability for enhancing high-value content transport and streaming for more energy-efficient, high-quality media experiences. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_12379 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LiteVPNet: A Lightweight Network for Video Encoding Control in Quality-Critical Applications Vibhoothi, Vibhoothi Pitié, François Kokaram, Anil Image and Video Processing Artificial Intelligence Machine Learning Multimedia In the last decade, video workflows in the cinema production ecosystem have presented new use cases for video streaming technology. These new workflows, e.g. in On-set Virtual Production, present the challenge of requiring precise quality control and energy efficiency. Existing approaches to transcoding often fall short of these requirements, either due to a lack of quality control or computational overhead. To fill this gap, we present a lightweight neural network (LiteVPNet) for accurately predicting Quantisation Parameters for NVENC AV1 encoders that achieve a specified VMAF score. We use low-complexity features, including bitstream characteristics, video complexity measures, and CLIP-based semantic embeddings. Our results demonstrate that LiteVPNet achieves mean VMAF errors below 1.2 points across a wide range of quality targets. Notably, LiteVPNet achieves VMAF errors within 2 points for over 87% of our test corpus, c.f. approx 61% with state-of-the-art methods. LiteVPNet's performance across various quality regions highlights its applicability for enhancing high-value content transport and streaming for more energy-efficient, high-quality media experiences. |
| title | LiteVPNet: A Lightweight Network for Video Encoding Control in Quality-Critical Applications |
| topic | Image and Video Processing Artificial Intelligence Machine Learning Multimedia |
| url | https://arxiv.org/abs/2510.12379 |