Increasing the speed of wavelet image processing based on the Winograd method taking into account decimation

Purpose of research. Wavelet transform is widely used to solve a wide range of digital image processing problems in various applied and scientific and technical fields. At the same time, modern visual information processing systems face the problem of insufficient performance against the background of a rapid increase in digital data volumes. This circumstance requires the development of computationally efficient wavelet processing algorithms suitable for implementation in modern computing devices. This study is aimed at reducing the computational complexity of wavelet image processing based on the use of a modification of the Winograd method. The article proposes the use of a new approach to organizing calculations for one-dimensional filtering with decimation. Methods. The study used a method for organizing calculations based on the Winograd transform and hardware simulation on a programmable valve matrix in Xilinx Vivado 2018.2 environment using Verilog language for Virtex 7 family model “xc7vx485tffg1157-1”, using standard synthesis and implementation parameters: “Vivado Synthesis Defaults” and “Vivado Implementation Defaults”, respectively. Results. Experimental modeling of the wavelet transform has demonstrated that the application of the Winograd method in wavelet image processing tasks allows for a reduction the computational delay by 34-63% compared to the direct method when using fourth-order wavelets and by 39-66% when using sixth-order wavelets. Conclusion. The application of the Winograd method provides a significant increase in the computation speed with some increase in hardware complexity and energy consumption. The results of the study can find wide application in modern signal, image and video processing systems, as well as in the development of machine learning systems.