A novel fast algorithm based on SMDWT for visual processing applications

This work presents a fast algorithm, namely 2-D symmetric mask-based discrete wavelet transform (SMDWT), to address some critical issues of the 2-D discrete wavelet transform (DWT). Unlike the traditional DWT involving dependent decompositions, the SMDWT itself is subband processing independent, which can significantly reduce complexity. Moreover, DWT cannot directly obtain target subbands, which leads to an extra wasting in transpose memory, critical path, and operation time. These problems can be fully improved with the proposed SMDWT. Nowadays, many applications employ DWT as the core transformation approach, the problems indicated above have motivated researchers to develop fast algorithms for DWT. The proposed SMDWT has been proved as a highly efficient independent processing to yield target subbands which can be applied to real-time visual applications, such as moving object detection and tracking, texture segmentation, image/video compression, and any DWT-based applications.