<< Chapter < Page
  Image coding   Page 1 / 1
Chapter >> Page >
This module introduces 2-D DWT.

We have already seen in our discussion of The Haar Transform how the 1-D Haar transform (or wavelet) could be extended to 2-D by filtering therows and columns of an image separably.

All 1-D 2-band wavelet filter banks can be extended in a similar way. shows two levels of a 2-D filter tree. The input image at each level is split into 4bands (Lo-Lo = y 0 0 , Lo-Hi = y 0 1 , Hi-Lo = y 1 0 , and Hi-Hi = y 1 1 ) using the lowpass and highpass wavelet filters on the rows and columns in turn. The Lo-Lo band subimage y 0 0 is then used as the input image to the next level. Typically 4 levels are used, as for the Haar transform.

Two levels of a 2-D filter tree, formed from 1-D lowpass ( H 0 ) and highpass ( H 1 ) filters.

Filtering of the rows of an image by H a z 1 and of the columns by H b z 2 , where a , b = 0 or 1, is equivalent to filtering by the 2-D filter:

H a b z 1 z 2 H a z 1 H b z 2
In the spatial domain, this is equivalent to convolving theimage matrix with the 2-D impulse response matrix
h a b h a h b
where h a and h b are column vectors of the 1-D filter impulse responses. However note that performing the filtering separably(i.e. as separate 1-D filterings of the rows and columns) is much more computationally efficient.

To obtain the impulse responses of the four 2-D filters at each level of the 2-D DWT we form h a b from h 0 and h 1 using with a b = 00, 01, 10 and 11.

2-D impulse responses of the level-4 wavelets and scaling functions derived from the LeGall 3,5-tap filters (a), and thenear-balanced 5,7-tap (b) and 13,19-tap (c) filters.

shows the impulse responses at level 4 as images for three 2-D wavelet filtersets, formed from the following 1-D wavelet filter sets:

  • The LeGall 3,5-tap filters: H 0 and H 1 from these equations , and these equations in our discussion of Good Filters / Wavelets.
  • The near-balanced 5,7-tap filters: substituting Z 1 2 z z into this previous equation .
  • The near-balanced 13,19-tap filters: substituting this equation into this equation .
Note the sharp points in (b), produced by the sharp peaks in the 1-D wavelets of this previous figure (Impulse and frequency responses of the 4-level tree of near-balanced 5,7-tapfilters). These result in noticeable artefacts in reconstructed images when these wavelets are used. The smoother wavelets of (c) are much better in this respect.

The 2-D frequency responses of the level 1 filters, derived from the LeGall 3,5-tap filters, are shown in figs (in mesh form) and (in contour form). These are obtained by substituting z 1 ω 1 and z 2 ω 2 into . demonstrates that the 2-D frequency response is just the product of the responses of the relevant1-D filters.

Mesh frequency response plots of the 2-D level 1 filters, derived from the LeGall 3,5-tap filters.
Contour plots of the frequency responses of .

and are the equivalent plots for the 2-D filters derived from the near-balanced 13,19-tap filters. Wesee the much sharper cut-offs and better defined pass and stop bands of these filters. The high-band filters no longer exhibitgain peaks, which are rather undesirable features of the LeGall 5-tap filters.

Mesh frequency response plots of the 2-D level 1 filters, derived from the near-balanced 13,19-tap filters.
Contour plots of the frequency responses of .

Get Jobilize Job Search Mobile App in your pocket Now!

Get it on Google Play Download on the App Store Now




Source:  OpenStax, Image coding. OpenStax CNX. Jan 22, 2004 Download for free at http://cnx.org/content/col10206/1.3
Google Play and the Google Play logo are trademarks of Google Inc.

Notification Switch

Would you like to follow the 'Image coding' conversation and receive update notifications?

Ask