Intelligent systems for redundancy removal with proficient run-length coding and statistical analysis using regression

Abstract

The surveillance video aspect has been one of the key technologies in various tactical monitoring. However, the quantum of analysis with proper implication of video quality subjected to enormous amount of time might degrade its error metrics. So in order to analyse this quantum has been made with the hierarchical order wherein four videos where taken and its peak errors where being analysed. The significance of the work is dealt with feature extraction and then comparison with input and extracted texture followed by feature analysis with cosine angle distance. Finally, a multiple regression analysis has been developed with peak signal to noise ratio (PSNR) as dependant variable where video size and execution time are taken as independent variable. The mathematical significance of regression has been based on prediction equation in order to near optimality of PSNR value for varying video size and execution time.