A fuzzy neural network based adaptive predictor with P-controller compensation for lossless compression of images

Predictively encoded techniques are commonly used for lossless compression of images for its effectiveness of removing statistical redundancy between pixels. However, there can be large prediction errors for pixels around boundaries. In this paper, we introduce techniques commonly used in control systems to enhance the coding efficiency of predictive coding. Actually, the predictive coding system behaves just like a multi-input single-output system with the predictor itself can be taken as the system model. When compared with the purpose of a control system, which is to follow the system command as precisely as possible, we find the objective of both systems are the same. Moreover, an edge or a boundary among image pixels can be regarded as a step command in control systems. These observations lead to the idea of using control technologies to improve prediction result for pixels around boundaries. To realize this idea, we use an adaptive Takagi-Sugeno fuzzy neural network (TS-FNN) as the predictor. Furthermore, the widely used proportional controller in control system is implemented implicitly in the consequent part of the network so that the prediction error can be further compensated for pixels around boundaries. We find in experiments that the proposed approach can have a very good prediction result even without using any online training area for network adaptation process. This makes the proposed system more feasible under limited resources. Finally, comparisons to existing state-of-the-art lossless predictors and coders will be given to highlight the advantages of the proposed novel approach.