Use of Neural Networks and Decision Trees in Compression of 2D and 3D Digital Signals
Mikhail V. Gashnikov · Optical Memory and Neural Networks · 2022
Abstract The article considers a compression framework for 2D/3D digital signals, including digital images and hyperspectral data. A compression framework is proposed that uses neural networks to exclude insignificant signal portions from the compression process with restoring these portions in decompression. Neural networks for key compression operations (sample prediction, determination of insignificant portions in decompression, etc.) are chosen. Additionally, the machine learning algorithms are modified to incorporate into the compression framework. The framework is extended to the case of 3D signals of hyperspectral data. The efficiency of the approach is tested by computer experiments using real 2D and 3D digital data. The experiment proves high efficiency of the compression algorithm and potentiality of using it in data storing and processing systems.