用特征图差分编码压缩监控视频，省带宽且保画质。
Inter-Feature-Map Differential Coding of Surveillance Video
- 利用特征图间差异进行编码，降低传输数据量。
- 压缩比优于或相当于HEVC，精度损失小。
- 适合对画质敏感的监控场景，边缘计算友好。
在协同智能中，深度神经网络（DNN）被分割并部署于边缘与云端以节省带宽并优化系统性能。当输入为图像时，已证实边缘输出的中间特征图可小于原始输入数据量。然而，当输入为视频时，该方法的有效性尚未报道。本研究提出一种针对监控视频特征图的跨特征图差分编码（IFMDC）方法。实验表明，在允许小幅精度下降的情况下，IFMDC 的压缩比可达到甚至优于视频编码标准 HEVC，且对图像质量敏感的视频更具优势。
原文摘要 · Abstract (English)
In Collaborative Intelligence, a deep neural network (DNN) is partitioned and deployed at the edge and the cloud for bandwidth saving and system optimization. When a model input is an image, it has been confirmed that the intermediate feature map, the output from the edge, can be smaller than the input data size. However, its effectiveness has not been reported when the input is a video. In this study, we propose a method to compress the feature map of surveillance videos by applying inter-feature-map differential coding (IFMDC). IFMDC shows a compression ratio comparable to, or better than, HEVC to the input video in the case of small accuracy reduction. Our method is especially effective for videos that are sensitive to image quality degradation when HEVC is applied
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