Lossless Image Compression Algorithm for Wireless Capsule Endoscopy by Content-Based Classification of Image Blocks

TL;DR

This paper proposes a lossless image compression algorithm for wireless capsule endoscopy that leverages content-based classification of image blocks to improve compression efficiency, thereby enhancing image quality and transmission performance.

Contribution

It introduces a novel lossless compression method using content-based classification of image blocks to achieve higher compression ratios in capsule endoscopy images.

Findings

High compression ratio achieved

Improved image transmission quality

Enhanced diagnostic image clarity

Abstract

Recent advances in capsule endoscopy systems have introduced new methods and capabilities. The capsule endoscopy system, by observing the entire digestive tract, has significantly improved diagnosing gastrointestinal disorders and diseases. The system has challenges such as the need to enhance the quality of the transmitted images, low frame rates of transmission, and battery lifetime that need to be addressed. One of the important parts of a capsule endoscopy system is the image compression unit. Better compression of images increases the frame rate and hence improves the diagnosis process. In this paper a high precision compression algorithm with high compression ratio is proposed. In this algorithm we use the similarity between frames to compress the data more efficiently.