A fast Discrete Wavelet Transform algorithm for visual processing applications

• Published in: Signal processing 2012, Vol.92 (1), p.89-106
• Main Authors: Hsia, Chih-Hsien, Guo, Jing-Ming, Chiang, Jen-Shiun
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Coding, codes; Complexity; Critical path; Decomposition; Detection, estimation, filtering, equalization, prediction; Discrete Wavelet Transform; Exact sciences and technology; Image processing; Information, signal and communications theory; Pattern recognition; Signal and communications theory; Signal processing; Signal, noise; Surface layer; Symmetric Mask-based Discrete Wavelet Transform; Telecommunications and information theory; Texture; Transpose memory; Visual
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2011.06.009

For visual processing applications, the two-dimensional (2-D) Discrete Wavelet Transform (DWT) can be used to decompose an image into four-subband images. However, when a single band is required for a specific application, the four-band decomposition demands a huge complexity and transpose time. 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 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 as mentioned, 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 lower complexity schemes for DWT. The proposed SMDWT has been proved as a highly efficient and 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 possible DWT-based applications. ► This work presents a fast algorithm, SMDWT. ► To solve the high complexity issue of the traditional 2-D DWT. ► The SMDWT itself is independent subband processing, which can significantly reduce complexity.