How2Compress: Scalable and Efficient Edge Video Analytics via Adaptive Granular Video Compression
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915566473707520 |
|---|---|
| author | Wu, Yuheng Nguyen, Thanh-Tung Liebe, Lucas Tau, Quang Campos, Pablo Espinosa Cheng, Jinghan Lee, Dongman |
| author_facet | Wu, Yuheng Nguyen, Thanh-Tung Liebe, Lucas Tau, Quang Campos, Pablo Espinosa Cheng, Jinghan Lee, Dongman |
| contents | With the rapid proliferation of the Internet of Things, video analytics has become a cornerstone application in wireless multimedia sensor networks. To support such applications under bandwidth constraints, learning-based adaptive quantization for video compression have demonstrated strong potential in reducing bitrate while maintaining analytical accuracy. However, existing frameworks often fail to fully exploit the fine-grained quality control enabled by modern blockbased video codecs, leaving significant compression efficiency untapped.
In this paper, we present How2Compress, a simple yet effective framework designed to enhance video compression efficiency through precise, fine-grained quality control at the macroblock level. How2Compress is a plug-and-play module and can be seamlessly integrated into any existing edge video analytics pipelines. We implement How2Compress on the H.264 codec and evaluate its performance across diverse real-world scenarios. Experimental results show that How2Compress achieves up to $50.4\%$ bitrate savings and outperforms baselines by up to $3.01\times$ without compromising accuracy, demonstrating its practical effectiveness and efficiency. Code is available at https://github.com/wyhallenwu/how2compress and a reproducible docker image at https://hub.docker.com/r/wuyuheng/how2compress. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_18409 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How2Compress: Scalable and Efficient Edge Video Analytics via Adaptive Granular Video Compression Wu, Yuheng Nguyen, Thanh-Tung Liebe, Lucas Tau, Quang Campos, Pablo Espinosa Cheng, Jinghan Lee, Dongman Multimedia Networking and Internet Architecture With the rapid proliferation of the Internet of Things, video analytics has become a cornerstone application in wireless multimedia sensor networks. To support such applications under bandwidth constraints, learning-based adaptive quantization for video compression have demonstrated strong potential in reducing bitrate while maintaining analytical accuracy. However, existing frameworks often fail to fully exploit the fine-grained quality control enabled by modern blockbased video codecs, leaving significant compression efficiency untapped. In this paper, we present How2Compress, a simple yet effective framework designed to enhance video compression efficiency through precise, fine-grained quality control at the macroblock level. How2Compress is a plug-and-play module and can be seamlessly integrated into any existing edge video analytics pipelines. We implement How2Compress on the H.264 codec and evaluate its performance across diverse real-world scenarios. Experimental results show that How2Compress achieves up to $50.4\%$ bitrate savings and outperforms baselines by up to $3.01\times$ without compromising accuracy, demonstrating its practical effectiveness and efficiency. Code is available at https://github.com/wyhallenwu/how2compress and a reproducible docker image at https://hub.docker.com/r/wuyuheng/how2compress. |
| title | How2Compress: Scalable and Efficient Edge Video Analytics via Adaptive Granular Video Compression |
| topic | Multimedia Networking and Internet Architecture |
| url | https://arxiv.org/abs/2510.18409 |