An Improved Image Compression Model Enabled by Adaptive Active Contour and Supervised Learning-Based ROI Classification

This paper plans to develop a novel image compression model with four major phases: (1) segmentation, (2) feature extraction, (3) ROI classification, and (4) compression. The image is segmented into two regions by Adaptive ACM. The result of ACM is the production of two regions. This model enables a separate ROI classification phase. For performing this, the features corresponding to GLCM are extracted from the segmented parts. Further, they are subjected to classification via NN, in which new training algorithm is adopted. As a main novelty, JA and WOA are merged together to form J-WOA with the aim of tuning the ACM (weighting factor and maximum iteration) and training algorithm of NN, where the weights are optimized. This model is referred as J-WOA-NN. This classification model exactly classifies the ROI regions. During the compression process, the ROI regions are handled by JPEG-LS algorithm, and the non-ROI regions are handled by wavelet-based lossy compression algorithm. Finally, the decompression model is carried out by adopting the same reverse process.