High-Speed Wavelet Image Processing Using the Winograd Method

Wavelets are actively used for solving of image processing problems in various fields of science and technology. Modern imaging systems have not kept pace with the rapid growth in the amount of digital visual information that needs to be processed, stored, and transmitted. Many approaches are being developed and used to speed up computations in the implementation of various image processing methods. This paper proposes the Winograd method (WM) to speed up the wavelet image processing methods on modern microelectronic devices. The scheme for wavelet image filtering using WM has been developed. WM application reduced the computational complexity of wavelet filtering asymptotically to 72.9% compared to the direct implementation. An evaluation based on the unit-gate model showed that WM reduces the device delay to 66.9%, 73.6%, and 68.8% for 4-, 6-, and 8-tap wavelets, respectively. Revealed that the larger the processed image fragments size, the less time is spent on wavelet filtering, but the larger the transformation matrices size, the more difficult their compilation and WM design on modern microelectronic devices. The obtained results can be used to improve the performance of wavelet image processing devices for image compression and denoising. WM hardware implementation on a field-programmable gate arrays and an application-specific integrated circuits to accelerate wavelet image processing is a promising direction for further research.