Vectorized computing for edge-avoiding wavelet

Abstract

The discrete wavelet transform (DWT) is an essential tool for image and signal processing. The edge-avoiding wavelet (EAW) is an extension for DWT to have edge-preserving property. EAW constructs a basis based on the edge content of input images; thus, the wavelet contains nonlinear filtering. DWT is computationally efficient processing in the scale-space analysis; however, EAW has a complex loop structure. Therefore, parallel computing for EAW is not an easy task. In this paper, we vectorize EAW computing by using single instruction, multiple data (SIMD) parallelization. Especially, the lifting-based wavelet allows the in-place operation, i.e., the source and destination array for DWT can be shared, and the in-place operation improves cache efficiency. However, the EAW prevents the operation in the update processing. Moreover, data interleaving for wavelet computing is the bottleneck for SIMD computing. Therefore, we show the suitable data structure for effective SIMD vectorization for EAW. Experimental results show that our effective implementation accelerates EAW. For the WCDF method, we accelerate more than two times faster, and for the WRB method, we accelerate about three times faster than the simple implementation.