Summary of Hpc: Hierarchical Progressive Coding Framework For Volumetric Video, by Zihan Zheng et al.
HPC: Hierarchical Progressive Coding Framework for Volumetric Video
by Zihan Zheng, Houqiang Zhong, Qiang Hu, Xiaoyun Zhang, Li Song, Ya Zhang, Yanfeng Wang
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Multimedia (cs.MM); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel hierarchical progressive volumetric video coding framework, called HPC, tackles the challenges of compressing Neural Radiance Field (NeRF) videos for various 3D applications. The key innovation is a multi-resolution residual radiance field that reduces temporal redundancy in long-duration sequences while generating different levels of detail. To optimize this hierarchical representation and compression, an end-to-end progressive learning approach with a multi-rate-distortion loss function is proposed. This single model can produce multiple compression levels, whereas current methods require training separate models for different rate-distortion tradeoffs. The results show that HPC achieves flexible quality levels with variable bitrate and competitive rate-distortion performance across various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NeRF videos have big potential for many 3D things, but they take up a lot of space and are hard to send quickly. Right now, there’s no good way to adjust the video quality and speed within one model for different devices or networks. To fix this, researchers came up with a new way to compress NeRF videos called HPC (Hierarchical Progressive Volumetric Video Coding). It works by making a special kind of map that reduces the amount of data needed to show detailed images over time. They also created a way to train one model to work well for many different compression levels and speed settings. This means you only need to train one model instead of many, which makes it more efficient. |
Keywords
» Artificial intelligence » Loss function
