Container based parallelization for faster and reliable image segmentation

In this paper, we describe a scalable and economical architecture for performing container based parallelization to obtain the best possible quantized image using different quantization techniques on the cloud. This approach using containers can be scaled to be used with huge datasets. The quantization techniques used in this paper are fuzzy entropy and genetic algorithm based techniques. Different types of membership functions are used in each technique to calculate the fuzzy entropy. The best possible quantized image is determined using the Structural Similarity Index (SSIM). This is a futuristic approach for solving lengthy repetitive serial problems in a parallel and economical way. As expected the results significantly better than the serial approach.