Comparison of Online Image Compression Tools in Grayscale and Colored Images

The growing content of multimedia on the world wide web thrive the need to study online image compression. There are many online image compression tools are available but the knowledge of the best tool still is an undiscovered area. This research is about analyzing as to which is the best online image compression tool available for coloured images and to develop a framework using neural network so that large number of images and large number of online image compression tools can be evaluated for their performance. To evaluate the performance of these tools Objective measurement technique is applied by calculating some image quality parameters namely Peak Signal Noise Ratio, Mean Square Error, Normalized Correlation, Maximum Difference. The results of these image quality parameters are rated on Likert scale from 1 to 5 and the average Likert scale points are processed to be fed to Back Propagation Neural Network Model to classify and evaluate the performance of these online image compression tools.