The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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2. Design the directional filters, for this paper we select the 
directional db5/3 filters. 
3. Performs the directional decomposition using the images: LH, 
HL and HH. The process is made using the usual 5/3 tap filter 
that decomposes an image with a maximum of 3 directions or 8 
subbands at the finer wavelet subbands. 
4. Repeat the step three with the next wavelet level of LH, HL 
and HH images, in this paper, we only do the directional 
decomposition to the two high-frequency levels. 
In Figure 4. We show the process to compute the WBCT (LiYu, 
Lin, 2007).. 
Figure 4. The process of WBCT 
2.4 The Bitplane Coding Based On Significant Bits 
Choose an adaptive encode method is very important for image 
compression, in this paper, we use the bitplane coding based on 
significant bits. For this method is very simple compared with 
SPIHT or EZW and time-saving. The matter is how to encode 
the bits so we can get a higher compression rate. 
Before coding, two conceptions should be referred to (FeiPeng 
Li, 2003): the significant bit and the refinement bit. The 
significant bit is the first non-zero bit of a coefficient; the bits 
after the significant bit are called refinement bits. And the bits 
before the significant bit are called zero bits. For zero bits and 
significant bits, we encode them with a Multiple Quantization 
(MQ) coder, the sign bits are encoded by another MQ coder, 
but for refinement bits, we don’t encode them but preserve 
directly for each one’s probability of 0 or 1 is equal. The 
definition of the three kinds of bits and their encode method are 
shown in Figure 5. 
Figure 5. Coefficient bits encoding method (shadows are the 
significant bits) 
2.5 Structure Similarity 
As we all know, the classical objective measure PSNR is not 
the only evaluation method of image quality, and it is not 
appropriated to measure the goodness of our coder. In this paper, 
we try to use a structure concerned method to evaluate the 
reconstructed images: the Structure SIMilarity (SSIM), 
represented by Zhou Wang (Zhou Wang et al. 2005). This 
method considered not just the error of each pixel between two 
images, but three different factors that decide the image quality: 
luminance, contrast and structure. So we choose SSIM as the 
image quality assessment method in this paper. The SSIM value 
has a constraint from 0 to 1, where high quality image has a 
larger SSIM value than a lower quality image. 
3. TESTS, RESULTS AND DISCUSSION 
In this paper, the experiments are based on the ImagZip2.0 
system constructed by doctor Li FeiPeng et al. 2001. In order to 
best illustrate the detail preserving effect of our method, we 
choose the 512*512 size image”Barbara” which is abundant in 
directional details. We use this image to perform two different 
tests, the first one with a bit rate of 0.5 in order to obtain a 
compression factor of 16, and the second one with a bit rate of 
0.2 in order to obtain a compression factor of 40. 
3.1 The comparing of the reconstructed images 
The decompressed images obtained for test (bit rate of 0.5 and 
0.2) using the DWT and the WBCT are shown in Figure 6. 
(e) (f)