Abstract
The state of the art in fractal picture compression algorithms is investigated. The primary directions for improving compression algorithms are discussed. In terms of classification efficiency and picture processing speed, the methods are among the most effective. The concept of steganographic use of the fractal algorithm is discussed. The differences between the advanced compression algorithm and the classic compression algorithm are evaluated to accomplish so. The differences discovered are used to define ways to improve the efficiency of the stego algorithm. The development of digital image compression technology was spurred by the necessity for speedy communication and "live" digital image information over the internet. Time has passed, and many tactics exist now to reduce the compression ratio and increase the usability of speedy computation, but because we are limited by certain constraints, there are many inventive ways to overcome these limitations. Today's environment is heavily reliant on digital media storage, necessitating the creation of more effective image or data compression algorithms. Images must be compressed and soft-encoded before being used in the transmission phase due to limited bandwidth and power. This paper discusses the differences between Lossy and Lossless compression methods as they apply to image processing.
Highlights
The current state of development of fractal image compression methods is analysed
Considering the main trends in the development of modern fractal image compression algorithms, we can identify fragments, borrowing which will optimize time. Such fragments can be classified based on the steganographic significance of the operations performed, which will allow the development of an adapted stego method
Methods of accelerating the fractal compression algorithm should be considered in accordance with the stages of image processing
Summary
The current state of development of fractal image compression methods is analysed. The main directions of improvement of compression algorithms are determined. The problem with designing effective data embedding methods is uncertainty about both the transformations used for embedding and the transformations that should hide them In some cases, the latter somewhat limits the former so that the choice is obvious. Greater advantages are guaranteed by developing a method that uses the features of the generation of a particular algorithm of compressed image code and its main operations as a region of transformations. In this case, the choice of compression algorithm indicates the idea of embedding data (Cierniak & Rutkowski, 2000; Deshlahra, 2013; Niu et al, 2010; Saroya & Kaur, 2014)
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