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This paper introduces an image compression and frame rate adaptation pipeline for Video Capsule Endoscopy (VCE) to reduce energy consumption while maintaining diagnostic image quality. The method identifies frames with low diagnostic value due to bubbles, using compression characteristics to avoid additional image analysis. Evaluated on RISC-V with Kvasir-Capsule and Galar datasets, the pipeline achieves an 82.6% compression ratio with a PSNR of 40.3 dB and reduces overall energy consumption by up to 40% through bubble-aware frame rate adaptation.
Diagnose more, charge less: a new VCE pipeline slashes energy consumption by 40% by intelligently skipping bubble-filled frames without sacrificing diagnostic quality.
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.
