Evolutionary design of multiplierless lifting-based 2D DWT filters for low-resolution image processing

The most difficult challenge in developing a high-efficiency embedded image processing system relates to how to execute complicated Digital Signal Processing (DSP) algorithm with embedded processor, especially when processing high-resolution images. A whole system may start to run very slowly because of the large volumes of data being processed and the complexity of the algorithm, rendering it unable to achieve real-time processing. Executing motion detection in low-resolution image was already proved to be able to reduce the loading of data manipulation and to be able to help filter out noise and fake motion. However, smoothing filter pointed out in the work will affect the quality of image. Although the LL-band signal of a 2D Discrete Wavelet Transform (DWT) domain can retain the main part of the energy and information of the original image, a complex algorithm is more suitable for the application of a low-pass filter. Symmetric Mask-based DWT (SMDWT) has the advantage of reduced complexity, regular signal coding, and independent subband coding processing. In this paper, we use a Particle Swarm Optimization (PSO) approach to automatically evolve the multiplierless 2D SMDWT filters hardware architecture. The architecture employs only shift-and-addition operations to replace the complex floating-point multiplication and division operations. It can use shift-and-add based SMDWT filter to decompose a low-resolution image and do real-time image processing system design with low-resolution image processing technique.