arXiv:2604.25464cs.CV2026-04

通过识别气泡帧动态降帧率,显著降低胶囊内镜能耗。

Image Compression with Bubble-Aware Frame Rate Adaptation for Energy-Efficient Video Capsule Endoscopy

论文配图:Image Compression with Bubble-Aware Frame Rate Adaptation for Energy-Efficient Video Capsule Endoscopy
图 1 · 摘自论文原文
  • 根据压缩特征识别低诊断价值的气泡帧,无需额外分析。
  • 压缩比达5.748,峰值信噪比40.3 dB,视觉质量几乎无损。
  • 结合降帧策略,系统总能耗最多降低40%,适合医疗嵌入式设备。

视频胶囊内镜(VCE)是提升胃肠道小肠检查效率的有前景方法,但其体积受限导致电池寿命短,与图像采集和传输的高能耗存在矛盾。为此,本文提出一种图像压缩流水线,显著减少传输数据量,同时保持诊断级图像质量。进一步利用压缩过程特性,识别因气泡导致的低可见性帧,无需额外图像分析。针对此类帧,采用动态气泡感知帧率自适应策略,降低采集与传输频率,同时保持对潜在异常的敏感性。该压缩与帧率适配方案在RISC-V平台上基于Kvasir-Capsule和Galar数据集进行评估:压缩比达5.748(压缩率82.6%),峰值信噪比为40.3 dB,视觉质量损失可忽略;整体系统平均能耗降低20.58%;气泡感知帧率调整进一步实现最高40%的能耗节省。结果表明该方法显著提升了VCE的实用性。

原文摘要 · Abstract (English)

Video Capsule Endoscopy (VCE) is a promising method for improving the medical examination of the small intestine in the gastrointestinal tract. A key challenge is their limited size, resulting in a short battery lifetime which conflicts with high energy consumption for image capturing and transmission to an on-body device. Thus, we propose an image compression pipeline that substantially reduces the transmitted data while preserving diagnostic image quality. Furthermore, we exploit characteristics of the compression process to identify frames with low diagnostic value mainly caused by bubbles, without requiring additional image analysis. For low-visibility frames, a dynamic bubble-aware frame rate adaptation strategy reduces image acquisition and transmission during these phases while preserving sensitivity to potential anomalies. The proposed compression and frame rate adaptation are evaluated on a RISC-V platform using the Kvasir-Capsule and Galar datasets. The compression method achieves a compression ratio of 5.748 (82.6%) at a peak signal-to-noise ratio of 40.3 dB, indicating negligible loss of visual quality. The compression accomplished a mean energy reduction of the whole system by 20.58%. Additionally, the proposed bubble-aware frame rate adaptation reduced the energy consumption by up to 40%. These results demonstrate the potential of our method to increase the applicability of VCE.

胶囊内镜图像压缩能耗优化医疗嵌入式

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