Performance Study for Mixed Transforms Generated by Tensor Product in Image Compression and Processing

Abstract

In all applications and specially in real time applications, image processing and compression plays in modern life a very important part in both storage and transmission over internet for example, but finding orthogonal matrices as a filter or transform in different sizes is very complex and importance to using in different applications like image processing and communications systems, at present, new method to find orthogonal matrices as transform filter then used for Mixed Transforms Generated by using a technique so-called Tensor Product based for Data Processing, these techniques are developed and utilized. Our aims at this paper are to evaluate and analyze this new mixed technique in Image Compression using the Discrete Wavelet Transform and Slantlet Transform both as 2D matrix but mixed by Tensor Product. The performance parameters such as Compression Ratio, Peak Signal to Noise Ratio, and Root Mean Squared Error, are all evaluated for both standard colored and gray images. The simulation result shows that the techniques provide the quality of the images it was normal but acceptable and need more researchers works.