Adaptive predictor with dynamic fuzzy K-means clustering for lossless image coding

This paper proposed a nonlinear predictor ADFK (Adaptive predictor with Dynamic Fuzzy K-means clustering error feedback) for lossless image coding based on multi-layered perceptrons. Since real images are usually nonstationary, a fixed predictor is not adequate to handle the varying statistics of input images. Using back propagation learning with causal neighbors of the coding pixel as training patterns to update network weights continuously, ADFK is made adaptive on the fly. Furthermore, prediction error is further refined in ADFK by applying error compensation different to compound context error modeling used in CALIC based on dynamic codebook design with adaptive fuzzy k-means clustering algorithm. Compensated errors are then entropy encoded using conditional arithmetic coding based on error strength estimation. The proposed compensation mechanism is proved to be very useful through experiments by further improving the bit rates in an average amount of about 0.2bpp in test images. Success in the use of proposed predictor is demonstrated through the reduction in the entropy and actual bit rate of the differential error signal as compared to that of existing linear and nonlinear predictors.