Evaluation of Energy-Efficient Image Compression Algorithms for Video Capsule Endoscopy
Bachelor’s Thesis
Abstract
Video Capsule Endoscopy transmits image data from a swallowable capsule under tight energy and bandwidth constraints, so compression must reduce data volume while preserving diagnostic image quality at low computational cost. This thesis implements and evaluates three low-complexity approaches — one-dimensional predictive coding (D1), two-dimensional top-left predictive coding (D2), and Run-Length Encoding — in lossless and near-lossless operation on a subset of the Rhode Island Capsule Endoscopy Dataset, together with a temporal preprocessing pipeline based on frame differencing and frame skipping, assessed in Python and on an embedded RISC-V platform. D1 achieved the highest compression ratios at acceptable reconstruction quality and remained computationally feasible on the embedded target, while D2 degraded substantially under quantization due to error propagation, making D1 the most promising approach for future VCE systems.