Optimization of image compression using artificial neural networks

The object of research is artificial neural networks of adaptive resonance theory (ART). ART neural networks are classified by matching input data to one of the existing classes, provided that the data is sufficiently similar to the Class prototypes. Continuous and discrete adaptive resonance theory networks ART-1 and ART-2 work effectively in recognition systems, especially in conditions of high uncertainty, when it is necessary to identify a large number of different images. The main problem that was solved in this study was to optimize the process of image compression using artificial neural networks, because image compression is widely used in many scientific and technical fields and becomes especially relevant when transmitting over narrow-band communication channels. A way to overcome these difficulties may be to select basic data for reconstruction from an open data set (Modified National Institute of Standards and Technology) – Fashion-MNIST. There are still unresolved issues related to the fact that lossy compression algorithms with increasing compression ratio usually generate artifacts that are clearly visible to the human eye. A compression algorithm based on neural networks is described, which establishes a correspondence between the input and output spaces consisting of elements of the codebook and neurons. The proposed method uses a different approach (First Order), rather than a simple difference coding scheme (zero order), where the new code is calculated by subtracting the previous encoded block. The peak signal-to-noise ratio of PSNR and the root-mean-square error (MSE) of these algorithms is 24.7 DB with a compression ratio of 25.22. The main area of practical use of the results obtained is improved image compression for processing large – volume video and photo materials without significant loss of quality