Publication

Abstract : In today’s world, there are many types of data. The text files, the images, the audio, and the videos are all data and have been integrated into everyone’s life. But the storage requirement of data is huge as all data is stored whether they are useful data or useless data. Data compression is the process of compressing the data such as an image, text, or audio, in such a way that the file is easier to store, takes less space, can be transferred over the network with less bandwidth, etc. Image Compression, especially, has evolved in many aspects. A new mathematical concept has started gaining traction. The paper shares an implementation of Ramanujan’s Sum-based transformation on bitmap images and tries to compress it with available algorithms. The image is transformed with Ramanujan’s Sum Matrix, then quantized, and then compressed. Then the results are compared with the existing algorithms available, in terms of storage space, compression percentage, and Peak Signal to Noise Ratio PSNR. The findings are pretty much fascinating, that how a simple change in the mathematical equation can make a difference in the results of compression.