Compression technique by Analyzing Peak Signal to Noise Ratio value using various Machine Learning Algorithms

Image compression method classifies the acquisition image and converts to grayscale image ($\text{P}=0.000$) using Support Vector Machine (SVM) compared with Decision Tree method. Peak Signal to Noise Ratio (PSNR) are performed with the acquisition image. 1000 sample images are taken as input. The image compression of the acquisition image is carried out using the Support Vector Machine algorithm where the number of samples $(\text{N}=40)$ and the sample size is divided into two ($\mathbf{N}=\mathbf{2 0}$) for SVM and $(\mathbf{N}=20)$ for Decision tree. The peak to signal noise ratio of SVM with 92.20 kbps is better than the Decision tree 84.80 kbps. There is a significant difference in PSNR is $\text{P}=0.293$ within the limit of study, the Support Vector Machine algorithm performs better at finding the Peak to signal noise ratio when compared with the Decision Tree.