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is B object
, and the number of empty blocks is B , the
empty
compression rate can be computed by (3).
N×
M × K×
B
Ratecomp
=
(3)
Cflags
+ 3×
Bobject
+ Ccodebook
Here, C
flags
is the capacity of the flags. The flags of
each block which are used for identifying the different
class should be stored in capacity of
N × M × K /( n×
n×
n×8)
.And C presents the
codebook
capacity of the codebook.
C. Decoding
The main idea of decoding algorithm of our algorithm
is to reconstruct the whole data in each block according
to the saved information like FCHVQ. Different from that
of FCHVQ, for empty blocks , we just skip that
blocks ,while in FCHVQ for those blocks whose average
gradient values are zero, we need replace their whole
block data with their mean values. Evidently, our method
is faster than FCHVQ. What’s more, when decompress in
GPU, for empty blocks, we just discard that blocks for
that these blocks make no contributes to the final
reconstructed image. So, acceleration techniques for
GPU-based volume rendering [7],for example, empty
space leaping can be well used.
D. Results and Comparison
In order to provide a context for the evaluation of our
work, we compare our approach with analogous
implementations of FCHVQ.
The performance of VQ is measured by the
compression rate(Original Data Size/Compressed Data
Size) and the reconstructed image quality. The
reconstructed image quality is evaluated by the peak
signal to noise ratio (PSNR) [8]. Here, the number of
codeword in the codebook is 256. The size of volume
data bonsai, aneurism and foot is 256×256×256×8 bits.
The comparison of the compression rate among
different volume data illustrates in Fig.V. ICVQ
presents our proposed algorithm.
From Fig.V , we can see that our proposed algorithm
can get much higher compression rate than FCHVQ.
Especially for aneurism volume data , the compression
Compression Rate
250
200
150
100
50
0
Figure V.
FCHVQ
ICVQ
bonsai foot aneurism
Volume Data
Compression rate comparison among volume data
rate of our proposed algorithm is almost three times more
than that of FCHVQ .For the same volume data, we use
different codebook size (64,128,256), our proposed
algorithm can also get higher compression rate. The
PSNR obtained from our proposed algorithm is about
0.1~0.2 higher than that of FCHVQ. Take volume data
bonsai for example, the PSNR of the our proposed
algorithm and FCHVQ is respectively 36.51 db and 36.36
db. But, our proposed algorithm is a little more
time-consuming than that of FCHVQ.
IV CONCLUSION AND FUTURE WORK
Classified Vector quantization has been proved to
be an efficient solution for CVR. Noticing that
classification scheme should be coupled to the
acceleration techniques of rendering because of its SIMD
architecture. This paper presents an improved efficient
large-scale data compression algorithm, the key to our
algorithm is to give full consideration of the
characteristics of the volume data by histogram technique
and make the classfication sheme more nature. While
applying the proposed algorithm to the testing data sets,
the experimental results show that our algorithm can not
only obtain a better image reconstruction quality, but also
increase the compression rate significantly. What’s more,
our proposed algorithm can be more easier decompress
and do rendering on GPU. In the future, we will
investigate how to apply our algorithm to the
unstructured volume data.
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