DynaFilter: Cloud-driven Dynamic Filtering for Satellite Edge Intelligence
2026-07-11 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionMachine Learning
AI summaryⓘ
The authors created DynaFilter, a method that helps satellites send only the important parts of images or videos to the cloud for analysis, saving bandwidth and energy. Instead of fully decompressing images or sending everything, their approach uses clues from compressed data like JPEG parts and video motion info to detect relevant regions. This means satellites do some checking right on board and send less data, making the whole process faster and more efficient. Their tests showed big reductions in data use, energy needs, and processing time compared to existing methods.
satellite edge systemsremote sensingcompressed-domain inferenceJPEG compressionmotion vectorsregion-of-interest (RoI)bandwidth savinginference latencyenergy consumptionedge-cloud systems
Authors
Ziyang Zhang, Jie Liu, Luca Mottola
Abstract
Modern satellite edge systems, including those performing remote sensing tasks such object detection and tracking, are characterized by severely limited bandwidth and intermittent connections, making continuous data transmission to the cloud impractical. Existing edge-cloud systems, however, either require heavy pre-processing before analysis, for instance, full decompression of imagery data, or transmit all compressed data regardless of relevance. To address these challenges, we design DynaFilter, a dynamic filtering technique that enables satellite edge devices to perform selective region-of-interest (RoI) inference directly in the compressed-domain, without full decompression. Our key insight is that low-level compression syntax, specifically DC coefficients/AC energy in JPEG images and motion vectors in video streams, exhibits strong correlations with high-level semantic queries. By establishing a precise mapping between cloud query semantics and multimodal compressed-domain features, DynaFilter enables the edge to identify and transmit only relevant data associated to RoIs. Extensive evaluations show that DynaFilter reduces the total volume of pixel data for decoding and subsequent inference by 1.6x-7.1x for images, and achieves 92.0% bandwidth savings for video streams compared to state-of-the-art baselines. Furthermore, it decreases energy consumption by 43.1-88.6% on target devices and achieves a 1.6x-3.0x speedup in inference latency.