Discrete Wavelet Transform on Consumer-Level Graphics Hardware

Tien-Tsin Wong , Chi-Sing Leung, Pheng-Ann Heng, and Jianqing Wang
in IEEE Transactions on Multimedia, Vol. 9, No. 3, April 2007, pp. 668-673.

Abstract

Discrete wavelet transform (DWT) has been heavily studied and developed in various scientific and engineering fields. Its multiresolution and locality nature facilitates applications requiring progressiveness and capturing high-frequency details. However, when dealing with enormous data volume, its performance may drastically reduce. On the other hand, with the recent advances in consumer-level graphics hardware, personal computers nowadays usually equip with a graphics processing unit (GPU) based graphics accelerator which offers SIMD-based parallel processing power. This paper presents a SIMD algorithm that performs the convolution-based DWT completely on a GPU, which brings us significant performance gain on a normal PC without extra cost. Although the forward and inverse wavelet transforms are mathematically different, the proposed algorithm unifies them to an almost identical process that can be efficiently implemented on GPU. Different wavelet kernels and boundary extension schemes can be easily incorporated by simply modifying input parameters. To demonstrate its applicability and performance, we apply it to wavelet-based geometric design, stylized image processing, texture-illuminance decoupling, and JPEG2000 image encoding.

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The full source code of DWT-GPU has been launched since 2004. It can be downloaded and evaluated from DWT-GPU homepage.


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