An intelligent compression system for wireless capsule endoscopy images. (September 2021) - Bouyaya, Dallel; Benierbah, Said; Khamadja, Mohammed
Highlights: An intelligent compression system is proposed to optimize the energy management of the wireless video capsule endoscopy. A classification based on deep learning is used to determine the important images for diagnostic. A simple scalable compression is adopted to save more bits by subsampling the chrominance of the non-important images and use it to enhance the quality and frame rate of important ones. The obtained results show that our proposed system is efficient and offers a good energy optimization for WCE. Abstract: In this paper, we propose an intelligent compression system that addresses the problems of energy limitations of wireless video capsule endoscopy. The principle is to include a classification feedback loop, based on deep learning, to determine the importance of the images being transmitted. This classification is used with a simple prediction-based compression algorithm to allow an intelligent management of the limited energy of the capsule. For this, the capsule starts by transmitting a subsampled version of each image with a small rate. The images will be decoded and classified, automatically, to detect any possible lesions. Following the classification result, the images considered as important, for diagnosis, will be enhanced with additional content, whereas the less important ones will be recorded with low quality. In this way, large amounts of bits will be saved, without affecting the diagnosis. The saved energy can be used to extend the life of the capsule or to increase the resolution and frame rate of some WCE images. The results of classification show an accuracy of more than 99%, which allowed us to code losslessly almost all the important images of our test sequences. Our results also show that many additional images can be transmitted and their number depend on the used subsampling and the number of important images.