为物联网设备设计的轻量级自适应码率视频压缩模型
MCUCoder: Adaptive Bitrate Learned Video Compression for IoT Devices
- 采用极简编码器，仅10.5K参数，内存占用350KB
- 相比M-JPEG降低55%以上码率，保持相同能耗
- 支持按重要性传输，适配网络波动的低资源设备
摄像头驱动的物联网设备快速发展，对高效视频压缩提出需求，尤其在边缘计算场景中，设备常受限于1或2MB RAM及不稳定的网络连接。传统与深度视频压缩方法针对高端硬件设计，难以适配此类受限设备。因此，当前多依赖M-JPEG，因其硬件效率高、复杂度低。本文提出MCUCoder，一个开源的面向资源受限物联网环境的自适应码率视频压缩模型。该模型采用超轻量编码器，仅10.5K参数，内存占用仅350KB，适合边缘设备与MCU部署。在保持与M-JPEG相当能耗的前提下，其在MCL-JCV数据集上实现55.65%的码率降低，在UVG数据集上达55.59%（以MS-SSIM衡量）。此外，MCUCoder通过生成按重要性排序的潜在表示，支持自适应码率流传输，确保在低资源设备上面对波动网络仍能实现流畅实时视频传输。源代码见：https://github.com/ds-kiel/MCUCoder。
原文摘要 · Abstract (English)
The rapid growth of camera-based IoT devices demands the need for efficient video compression, particularly for edge applications where devices face hardware constraints, often with only 1 or 2 MB of RAM and unstable internet connections. Traditional and deep video compression methods are designed for high-end hardware, exceeding the capabilities of these constrained devices. Consequently, video compression in these scenarios is often limited to M-JPEG due to its high hardware efficiency and low complexity. This paper introduces , an open-source adaptive bitrate video compression model tailored for resource-limited IoT settings. MCUCoder features an ultra-lightweight encoder with only 10.5K parameters and a minimal 350KB memory footprint, making it well-suited for edge devices and MCUs. While MCUCoder uses a similar amount of energy as M-JPEG, it reduces bitrate by 55.65% on the MCL-JCV dataset and 55.59% on the UVG dataset, measured in MS-SSIM. Moreover, MCUCoder supports adaptive bitrate streaming by generating a latent representation that is sorted by importance, allowing transmission based on available bandwidth. This ensures smooth real-time video transmission even under fluctuating network conditions on low-resource devices. Source code available at https://github.com/ds-kiel/MCUCoder.
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