- Non Separable Two Dimensional Discrete Wavelet Transform for Image Signals
- Chapter 4: Applications of the 2-D CWT. I: Image Processing | Engineering
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Non Separable Two Dimensional Discrete Wavelet Transform for Image Signals
Views Total views. Actions Shares. Embeds 0 No embeds. No notes for slide. Wavelet transform in two dimensions 1. Wavelet Transform The wavelet transform is a tool that cuts up data, functions or operators into different frequency components, and then studies each component with a resolution matched to its scale Uses a variable length window, e. Resultant Decomposition Example: As in the Fourier domain, the basic approach is to — Step 1.
This problem is avoided by compensating word length at the minimum cost. Table 4 summarizes lossless coding performance of the DWTs in table 3 at different number of stages in octave decomposition. The difference between them is only 0. The difference is 0. As a result of this experiment, it was found that there is no significant difference in lossless coding performance.
Five-stage octave decomposition of DWT is applied. Transformed coefficients are quantized with the optimum bit allocation and EBCOT is applied as an entropy coder. In the figure, PSNR is calculated as. As indicated in Fig. All of them have the same rate-distortion curve. Note that this is true under long enough word length. In this experiment, word length of signals F s of both of the forward and the backward transform is set to 64 [bit]. Signal values are multiplied by 2 -Fs , floored to integers and then multiplied by 2 Fs.
- Implementation and Interpretation of the 2-D CWT | Engineering.
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As a result, all the signals have the word length F s [bit] in fraction. It was found that the NS DWTs have quality deterioration problem at high bit rates in lossy coding, even though they have less lifting steps.
Chapter 4: Applications of the 2-D CWT. I: Image Processing | Engineering
In case of finite word length implementation, the distortion D n 1 ,n 2 in 42 contains two kinds of errors; a quantization noise q for rate control in lossy coding and b truncation noise c due to finite word length expression of signals inside the forward transform. Assuming that q and c are uncorrelated and both of them has zero mean, variance of the distortion is approximated as. It means that finite word length noise c is negligible at lower bit rates comparing to the quantization noise q in respect of L 2 norm.
However, variance of c dominates over that of q at high bit rates. Therefore the quality deterioration problem can be avoided by increasing the word length F s. We utilize the fact that C compatibility is a monotonically increasing function of F s. Their relation is approximately described as. As a result, the minimum word length for compensation is clarified as.
Table 5 summarizes the parameters p 0 and p 1 calculated from this figure. Table 6 summarizes two parameters a and b in 47 which were calculated from p 0 and p 1.
As a result, the minimum word length for compensation is found to be 1 bit at maximum as summarized in table 7. It is confirmed that the deterioration problem observed in Fig. It means that the finite word length problem peculiar to the non-separable 2D DWTs can be perfectly compensated by adding only 1 bit word length, in case of implementation with very short word length, i. It was confirmed that the total number of lifting steps is reduced by the 'non-separable' DWT maintaining good compatibility with the 'separable' DWT.
Performance of these DWTs were evaluated in lossless coding mode, and no significant difference was observed.
A problem in finite word length implementation in lossy coding mode was discussed. It was found that only one bit compensation guarantees good compatibility with the 'separable' DWTs. In the future, execution time of the DWTs on hardware or software platform should be investigated.
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Downloaded: Introduction Over the past few decades, a considerable number of studies have been conducted on two dimensional 2D discrete wavelet transforms DWT for image or video signals. Table 1. Total number of lifting steps. Performance Evaluation In this section, all the DWTs summarized in table 3 are compared in respect of lossless coding performance first.
Table 2. Total number of rounding operations. Table 3. DWTs discussed in this chapter. Table 4. Bit rate for each image in lossless coding [bpp]. Lossy Coding Performance Fig.
Finite Word Length Implementation Fig. Table 5. Parameters in the rate distortion curves. Table 6. Parameters for word length compensation. Table 7. The minimum word length for compensation. More Print chapter. How to cite and reference Link to this chapter Copy to clipboard. Available from:. Over 21, IntechOpen readers like this topic Help us write another book on this subject and reach those readers Suggest a book topic Books open for submissions.
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