Singular Value Decomposition (SVD) Based Optimal Image Compression Technique

Images are prevalent in the Internet and throughout the digital space today. While they are an excellent way to communicate information (an image is worth a thousand words), it is also an expensive form of data to be transported across the network. To solve this problem, compression software is often used to encode an image to a smaller physical size so it can be transported more efficiently while minimizing the perceptible quality change from its original copy. In light of this, a question that comes up is: how much could an image is compressed before it is no longer valuable in providing information to the user. In this paper we will discuss how to optimize an image compression algorithm that is based on finding the singular value de- composition of a matrix. The effectiveness of the suggested strategy in terms of compression and quality retrieval has been demonstrated through a thorough investigation. It is clear from the testing results that a compression of up to 80% can be accomplished while maintaining acceptable visual quality according to the human vision system (HVS).