smooth side-match classified vector quantizer with variable block size ieee transaction on image...
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Smooth Side-Match Classified Vector Quantizer with Variable Block Size
IEEE Transaction on image processing, VOL. 10, NO. 5, MAY 2001Department of Applied MathematicsNational Chung Hsing UniversityShiueng Bien Yang and Lin Yu Tseng
Outline
Introduction Basic Algorithm Smooth Side-Match Method with
Variable Block Size Genetic Clustering algorithm Experimental Results Conclusion
Introduction
The evolution of SMVQ SMVQ SMVQ with CVQ SSM-CVQ
Feature of SSM-CVQ Variable block size Smooth side-match method Genetic clustering algorithm is applied
on codebooks generation
Basic algorithm SMVQ
n
jmjj uwwhd
1
21 )()(
m
iini lwwvd
1
21 )()(
)()()( wvdwhdwsmd
Basic algorithmSMVQ with CVQ (encoder)
Basic algorithmSMVQ with CVQ (decoder)
Smooth Side-Match Method with Variable Block Size
Variable Block Size Image Compression with Variable Block
Size Segmentation Quadtree is used to address blocks of
different sizes Smooth side-match method
Diagonal basic blocks Smooth side-match distortion
Image Compression with Variable Block Size Segmentation
Quadtree is used to address blocks of different sizes
Block size and codebooks
Blocks of sizes of 16x16 and 8x8 and 4x4 with low variance are low-detail blocks Use three master codebooks
4x4 8x8 16x16
Blocks of size of 4x4 with high variance are high-detail blocks Use CLUSTERING algorithm, we have q classes
and q master codebooks for each class Total : 3 + q master codebooks
Diagonal basic blocks
Diagonal blocks are encoded first. In the experiments, the number of the basic blocks
required is approximately 25% to 28% of that of the conventional SMVQ.
Smooth side-match distortion (1)
The encoded is divided into two parts Upper triangular region Lower triangular region
Problem of SMVQ
Different, dif(e, f) is defined as dif(e, f) = (gray level of e) – (gray level of f)
Smooth side-match distortion (2) Upper triangular region
n
iimi
imimii yddifyydifdddif
yvdUpper1
,,1,1,,1,,2 |),(
2
)),()((|)(_
n
ijnj
jjnjnj yldifyydiflldif
yhdUpper1
1,,2,1,,1, |),(
2
)),(),((|)(_
)(_)(_)( yhdUpperyvdUpperyD
Smooth side-match distortion (3) Lower triangular region
n
iiim
iiimim yudifyydifuudif
yvdLower1
,1,,2,1,,,1 |),(
2
)),()((|)(_
n
injj
njnjjj yrdifyydifrrdif
yhdLower1
,1,1,,1,2, |),(
2
)),(),((|)(_
)(_)(_)( yhdLoweryvdLoweryD
Genetic Clustering Algorithm (1)
First Stage Use nearest neighbor (NN) algorithm to reduce the
computation time and space in the second stage. (1)
(2)
(3)
(4) Let the connected components be denoted by
ijij
iNN OOOd
min)(
n
iiNNav Od
nd
1
)(1
1.5 be tochosen empirical is u *
otherwise ,0
if ,1),(
udd
dOOjiA
av
ji
mBBB ,...,, 21
Genetic Clustering Algorithm (2)
Second Stage Use genetic algorithm to find an appropriate number of
clusters.
Initialization Step chromosome (string): numbers of 1’s in the strings almost uniformly distributes within [1,m]
sm TTTTTBBB ,...,, ,,..., 2121
kijijj SVSVCB if
ij
iijj
jBC
BVCSS
**'
Genetic Clustering Algorithm Data Representation
Chromosome
Gene
Individual
......,,
......,,
......,,
222
111
nnn cba
cba
cba
N individuals
Population Size=N
N strings is randomly generated.
Genetic Clustering Algorithm Evolution Processes
1. Self Reproduction2. Crossover3. Mutation
Genetic Clustering Algorithm Fitness Function
......,,)(
......,,)2(
......,,)1(
222
111
nnn cbafnevaluation
cbafevaluation
cbafevaluation
kCB
ikji
iinter
kCB
ikiintra
BSVCD
BSVCD
ik
ik
*min)(
*)(
)(*)()( iintraiinter CDwCDRfitness
i
kki
ii
ii
Pq
xf
xfP
1
1
)(
)(
1P2P
3P
Genetic Clustering Algorithm Self Reproduction
if
iki qrq 1
k= ......,, iii cba
Genetic Clustering Algorithm Crossover
Set Probability of crossover Position q
Randomly generateIf
cP
ncc PP 1
cccc PPPPlk
......,,,......
......,,,......
kkll
llkk
dcba
dcba
Position=q
Genetic Clustering Algorithm Mutation
Set Probability of mutation Randomly generateIf
mP
nmm PP 1
mm PPq
......,, newqq cba
Experimental results
High-detailed Blocks :why 28 edge-classifiers
Outside image: Lena & F-16 The PSNRs of the coding for Lena SSM-CVQ outperforms the others in
both the PSNR & the bit rate
High-detailed Blocks :why 28 edge-classifiers
)(*)()( intint iraier CDwCDRFitness
Outside image: Lena & F-16
JPEG Lena 32.01 0.2681
SMVQ with CVQ
30.44 0.2704
The PSNRs of the coding for Lena
CLUSTERING is best !
SSM-CVQ outperforms the others in both the PSNR & the bit rate
Conclusion
The CLUSTERING clusters the appropriate number of clusters.
Low-detail blocks could reduce bit rates
High-detail blocks and smooth side-match distortion could increase quality
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