In this paper, an inverse wavelet image transform method for JPEG XS format is proposed. The said format uses Le Gall wavelet filter and the lifting scheme is used as a wavelet transform. This method of wavelet processing of images and video signal has low computation speed. To improve the computation speed, it is proposed to use the Winograd method as this method allows parallel processing of groups of pixels. The paper analyses the impact of accuracy in obtaining high quality image for fixed point format computation. The simulation results show that processing of 2 pixels using Winograd's method is sufficient to use 3 decimal places to obtain high quality image. When processing 3 and 4 pixels of the image it is sufficient to use 7 decimal places each. When processing 5 pixels of the image it is enough to use 12 decimal places. A promising direction for further research is the development of hardware accelerators for performing the inverse discrete wavelet transform by the Winograd method.
Keywords: inverse discrete wavelet transform, Le Gall filter, Winograd method, image processing, digital filtering, JPEG XS
This paper is devoted to the application of the Winograd method to perform the wavelet transform in the problem of image compression. The application of this method reduces the computational complexity and also increases the speed of computation due to group processing of pixels. In this paper, the minimum number of bits at which high quality of processed images is achieved as a result of performing discrete wavelet transform in fixed-point computation format is determined. The experimental results showed that for processing fragments of 2 and 3 pixels without loss of accuracy using the Winograd method it is enough to use 2 binary decimal places for calculations. To obtain a high-quality image when processing groups of 4 and 5 pixels, it is sufficient to use 4 and 7 binary decimal places, respectively. Development of hardware accelerators of the proposed method of image compression is a promising direction for further research.
Keywords: wavelet transform, Winograd method, image processing, digital filtering, convolution with step