A Novel Pixel Value Predictor Using Long Short Term Memory (LSTM) Network

Abstract

Pixel value prediction refers to predicting a pixel value using its neighboring pixel values. It is an important part of several image processing tasks, such as image compression and reversible data hiding. Prediction error is defined as the difference between the original pixel value and the corresponding predicted pixel value. Low prediction error helps to attain good performances in both image compression and reversible data hiding. This has motivated researchers to explore various techniques for pixel value prediction. Most of these techniques map the neighborhood to the predicted value using a mathematical relationship. This kind of rigid approach does not fit to the varied pixel neighborhood in images. On the contrary, a neural network can be trained to predict the pixel value instead of using a conventional mathematical approach for this mapping. In this paper, pixel value prediction is conceptualized as predicting the next value in a spatial sequence of pixels in a particular direction. Therefore, a novel pixel value predictor is proposed using a long short term memory (LSTM) network. Two different LSTM networks are trained separately for horizontal and vertical directions. Finally, the proposed approach leverages the predictors for both directions. For each pixel, it considers the direction with lesser variation in the input pixel sequence. Experimental results indicate that the proposed LSTM network based predictor performs better than the comparing state-of-the-art methods.