A DISCRETE WAVELET TRANSFORM CODEC DESIGN

2004 ◽  
Vol 13 (06) ◽  
pp. 1347-1378 ◽  
Author(s):  
YI-QIANG HU ◽  
BING-FEI WU ◽  
CHORNG-YANN SU

This manuscript presents a VLSI architecture and its design rule, called embedded instruction code (EIC), to realize discrete wavelet transform (DWT) codec in a single chip. Since the essential computation of DWT is convolution, we build a set of multiplication instruction, MUL, and the addition instruction, ADD, to complete the work. We segment the computation paths of DWT according to the multiplication and addition, and apply the instruction codes to execute the operators. Besides, we offer a parallel arithmetic logic unit (PALU) organization that is composed of two multipliers and four adders (2M4A) in our design. Thus, the instruction codes programmed by EIC control the PALU to compute efficiently. Additionally, we establish a few necessary registers in PALU, and the number of registers depends on the wavelet filters' length and the decomposition level. Yet, the numbers of multipliers and adders do not increase as we execute the DWT or the inverse DWT (IDWT) in multilevel decomposition. Furthermore, we deduce the similarity between DWT and IDWT, so the functions can be integrated in the same architecture. Besides, we schedule the instructions; thus, the execution of the multilevel processes can be achieved without superfluous PALU in a single chip. Moreover, we solve the boundary problem of DWT by using the symmetric extension. Therefore, the perfect reconstruction (PR) condition for DWT requirement can be accomplished. Through EIC, we can systematically generate a flexible instruction codes while we adopt different filters. Our chip supports up to six levels of decomposition, and versatile image specifications, e.g., VGA, MPEG-1, MPEG-2, and 1024×1024 image sizes. The processing speed is 7.78 Mpixel/s when the operation frequency, for normal case, is 100 MHz.

Author(s):  
BRANDON WHITCHER ◽  
PETER F. CRAIGMILE

We investigate the use of Hilbert wavelet pairs (HWPs) in the non-decimated discrete wavelet transform for the time-varying spectral analysis of multivariate time series. HWPs consist of two high-pass and two low-pass compactly supported filters, such that one high-pass filter is the Hilbert transform (approximately) of the other. Thus, common quantities in the spectral analysis of time series (e.g., power spectrum, coherence, phase) may be estimated in both time and frequency. Compact support of the wavelet filters ensures that the frequency axis will be partitioned dyadically as with the usual discrete wavelet transform. The proposed methodology is used to analyze a bivariate time series of zonal (u) and meridional (v) winds over Truk Island.


Author(s):  
M. Kalaiarasi ◽  
T. Vigneswaran

<p>Image compression is a key technology in the development of various multimedia and communication applications. Perfect reconstruction of the image without any loss in picture quality and data is very important. This can be achieved with the Discrete Wavelet Transform (DWT), which is an efficient tool for image compression and video compression. The lifting based DWT architecture has the advantage of lower computational complexities and also requires less memory compared to the conventional convolution method. The existing DWT architectures are represented in terms of folded, flipping and recursive structures. The various architectures are discussed in terms of memory, power consumption and operating frequency involved with the given size of image and required levels of decomposition. This paper presents a survey of these architectures for 2-dimensional and 3-dimensional Discrete Wavelet Transform. This study is useful for deriving an efficient method for improving the speed and hardware complexities of existing architectures.</p>


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