image compression based on btc-dpcm and it ’ s data-driven parallel implementation author :...
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Image Compression Based On BTC-DPCM And
It’s Data-Driven ParallelImplementation
Author : Xiaoyan Yu 、 Iwata, M.Source : Image Processing, 2005. ICIP 2005. IEEE International Conference onSpeaker : Cheng-Jung WuAdvisor : Wen-Chien Chen
Outline Introduction Adaptive BTC on data-driven processing sy
stem Adaptive BTC algorithm Data-driven implementation
Experimental evalution Conclusion
Introduction Image compression standards endure too heavy com
putational load in spite of good reconstructed quality with a very low bit rate
The rate-distortion performance of the original BTC VQ、 DCT AMBTC、 ABTC
Reconstructed quality and computational complexity ABTC algorithm coupled with DPCM
Adaptive BTC on data-driven processing system
Most of the existing coding schemes do not care about their implementation in total
An image compression algorithm and its implementation are considered as an integrated system
ABTC Realize a fast coding on system-on-chip (SoC) Guarantee the reasonable image quality and compression ratio
Non-overlapping 4x4 pixel blocks
Mean value ( )
Absolute moment ( AM )
Adaptive BTC algorithm
l
iixl
x1
1
x
11
li xx
lAM
Adaptive BTC algorithm Each luminance block
a uniform block
a normal block
a pattern block
Decoder a uniform block
reproduce the image a normal block
a pattern block
AMTAM
AMTAM
MAMTMAE AMxxl AMxxh
'1xx
lMAE
xxE ii
''ii Exx
DPCM algorithm Improve the bit rate with very small distortion of
image quality
DPCM neighboring pixels possess a high degree of correlation
within an image
DPCM algorithm Three arbitral approaches
two uniform blocks
two consecutive normal blocks
two adjacent pattern blocks
1 ii MeanMeandifMean
1 ii AMAMdifAM
l
jjiji difMapdifMapdifMap
1,1,
l
jjiji EESAD
1,1,
Data-driven implementation (a) illustrates a dataflow graph that sums up 16
input pixels of a block and calculates its mean by a 4 bit right-shift operation.
In this case, an intermediate accumulated sum is fed back to the add operator repetitively
The longest critical path influences the total pixel rate of the ABTC program.
Data-driven implementation (b) shows data-driven implementation by which the
feedback path is distributively stuffed into each compound operator (read & add) so that the execution time of the critical path can be minimized at the software level
Accepts a stream of 8 packets (i=1, …,8) each of which holds two neighbor pixels in a 4 x 4 block
Data-driven implementation Response time ( )
( a): t the time of the second pixel in a block image arrivingat add function
( b): t’ the time of the second pixel in a block image arriving at add function
In case of DDMP
RT
tttT ccR 15
cR ttT 8''
6.3,42,7 ''
R
Rc T
Tthustttt
Experimental evalution Human is more
sensitive to luminance changes rather than chrominance variances in an image. Thus, as for every chrominance block, the mean value is only calculated
Experimental evalution Visual quality of the pro
posed algorithm is competitive to that of JPEG2000 while its computational complexity is much less than that of JPEG2000
Experimental evalution The data-driven implementation of ABTC algorithm
was performed using the variant number of processors on a single chip
Conclusion ABTC algorithm coupled with DPCM can achieve a
better trade-off between reconstructed quality and computational complexity
Both concurrent and pipelined parallelism inherent in the adaptive BTC were exploited and implemented on the DDMP chip