Neuro-fuzzy image compression using differential pulse code modulation and probabilistic decision making

Abstract

Using an image compression hybrid model, the suggested research created a practical method for integrating learning system advantages with a decision logic framework. The emphasis here is that when integrated with the conventional image coding technology the potential usefulness of the decision logic is used as decision making. The execution is divided into three stages. In the first place, the image DCT representation of the image transformed to a different energy usage and is computed for different energy levels. A parallel processing of each power coefficient would then result in a substantially higher processing speed. In the second phase, differential pulse code modulation is used to compress the coefficients that correspond to the lowest energy level. Coefficients from the learning system are used as energy component, used to extract the coefficients. Finally, the algorithm is fed the results of the probabilistic decisions made in the second step of the program’s development. To validate the proposed approach, the suggested method is tested over different Magnetic resonance imaging (MRI) medical samples. The simulation findings reveal good results and suggest that the reconstructed images are better than the conventional system. The developed Neuro-Fuzzy image compression model, results in attaining high accuracy and precision with reduced processing overhead and computation complexity.