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