Optimizing the complexity of matrix multiplication algorithm

With the bird's eye view of many analyst's attentions in the last few years to know how companies are collecting and transmitting enormous amounts of information. As there are problems in transmitting large amount of data. This is a need of an hour to overcome the problems. The data can be compress such as word file or image file etc. to send the data efficiently. Compression must be done in such a way that loss of data is minimum (i.e. 0%). Image compression helps to overcome the problem of sending large images. In this Discrete Cosine Transformation (DCT) plays a crucial role. In DCT, for compression of JPEG images we have techniques of Quantization and encoding. In this whole work, we have used Custom matrix multiplication algorithm (CMM) for reducing the complexity of matrix multiplication (MM) problem. The results from the experiment when comparing with Naive matrix multiplication and Strassen's matrix multiplication shows increase in the performance of DCT. As the performance have increased due to the less time and space complexity of Strassen's as compared to Naive. As CMM is having less time and space complexity as compared to Strassen's algorithm we are expecting to have more increase in performance. The time required for compression and the size of the files after compression with different algorithms for different images helps to differentiate between the performances.