Abstract
Abstract In recent years, the important and fast growth in the development and demand of multimedia products is contributing to an insufficiency in the bandwidth of devices and network storage memory. Consequently, the theory of data compression becomes more significant for reducing data redundancy in order to allow more transfer and storage of data. In this context, this paper addresses the problem of lossy image compression. Indeed, this new proposed method is based on the block singular value decomposition (SVD) power method that overcomes the disadvantages of MATLAB’s SVD function in order to make a lossy image compression. The experimental results show that the proposed algorithm has better compression performance compared with the existing compression algorithms that use MATLAB’s SVD function. In addition, the proposed approach is simple in terms of implementation and can provide different degrees of error resilience, which gives, in a short execution time, a better image compression.
Highlights
Singular value decomposition (SVD) is a generalization of the eigen-decomposition used to analyze rectangular matrices
This new proposed method is based on the block singular value decomposition (SVD) power method that overcomes the disadvantages of MATLAB’s SVD function in order to make a lossy image compression
This study sets up a new algorithm for image compression that must be considered as an application of the block SVD power method to image compression
Summary
Singular value decomposition (SVD) is a generalization of the eigen-decomposition used to analyze rectangular matrices. SVD plays an important role in many applications, and it is the most useful tool of linear algebra with several applications including image compression [23]; mathematical models in economics, physical processes, and biological processes; data mining applications; search engines to rank documents in very large databases, including the Web; image processing applications; etc. The use of SVD in image compression has been widely studied [2, 13, 25, 27]. We generally take into account two aspects: image size in pixels and the degree of compression. There is another aspect: pixel depth or bit cost to represent each pixel. The main goal of such a system is to reduce the storage quantity as much as possible while ensuring that the decoded image displayed in the monitor can be visually similar to the original image as much as it can be
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