Lossless Image Compression Algorithm for Wireless Capsule Endoscopy by Content-Based Classification of Image Blocks
This work addresses challenges in capsule endoscopy systems for medical diagnosis, but it appears incremental as it builds on existing compression methods with a content-based classification approach.
The paper tackles the problem of low frame rates and image quality in wireless capsule endoscopy by proposing a lossless image compression algorithm that uses similarity between frames to achieve high compression ratios, improving the diagnosis process.
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.