Reducing the feedback requests in distributed coding of capsule endoscopy images with an LSTM model

Wyner-Ziv coding is a particular case of distributed video coding (DVC). It has a low complexity encoding that makes it a good candidate for many wireless applications. One of these applications is in coding the images of the capsule endoscopy, which is a medical technology used for imaging the gastrointestinal tract. One of the problems of DVC is the use of feedback channel (FC) to control the rate, but it can cause a large delay and can increase the decoder complexity. In this paper, we propose to use deep learning to predict the decoding rate and thus to reduce the use of the FC. The deep learning approaches are highly accurate in prediction and can surpass the traditional forecasting approaches. Therefore, a long short-term memory (LSTM) model is used, in our DVC system, to solve the feedback request problem in coding the capsule endoscopy images. The proposed model is implemented in the decoder side to keep the encoder simple. The obtained experimental results are very good and show that the number of requests are significantly reduced and the rate-distortion performance is very near-to-optimal rate and better than those of JPEG.