2008
DOI: 10.1109/tmm.2007.911195
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Implementing the 2-D Wavelet Transform on SIMD-Enhanced General-Purpose Processors

Abstract: Abstract-The 2-D Discrete Wavelet Transform (DWT) consumes up to 68% of the JPEG2000 encoding time. In this paper, we develop efficient implementations of this important kernel on general-purpose processors (GPPs), in particular the Pentium 4 (P4). Efficient implementations of the 2-D DWT on the P4 must address three issues. First, the P4 suffers from a problem known as 64K aliasing, which can degrade performance by an order of magnitude. We propose two techniques to avoid 64K aliasing which improve performanc… Show more

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Cited by 21 publications
(7 citation statements)
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“…Further, SIMD optimizations are often used in signal (1D) and image (2D) processing. In [24], SIMD was used to efficient implement the 2D wavelet transform. SIMD vectorization of histogram functions is described in [25].…”
Section: Related Workmentioning
confidence: 99%
“…Further, SIMD optimizations are often used in signal (1D) and image (2D) processing. In [24], SIMD was used to efficient implement the 2D wavelet transform. SIMD vectorization of histogram functions is described in [25].…”
Section: Related Workmentioning
confidence: 99%
“…Giving a multiresolution analysis in both time and frequency domain and having different alternatives for the basis function makes wavelet transform a better candidate than Fourier transform [4,5] for many applications including JPEG2000 [6]. A growing number of publications that deal with hardware implementation of wavelet transform [7][8][9][10] are another proof of its applicability.…”
Section: Wavelet Based Feature Extractionmentioning
confidence: 99%
“…Wavelet transform [13] has been widely used in many fields such as JPEG2000 [14].A growing number of publications deal with hardware implementation of this transform [15][16][17][18] which demonstrates its utility in the area of Pattern Recognition. A multi-resolution analysis of a signal with localization in both time and frequency is the advantage of wavelet transform over Fourier and cosine transforms [19][20][21]. In order to perform feature extraction, the two-dimensional (2-D) wavelet transform of the faces is calculated using different basis functions only up to 1 scale .…”
Section: A Wavelet Transformmentioning
confidence: 99%