Upload
krishnagbpec
View
234
Download
0
Embed Size (px)
Citation preview
8/2/2019 Medical Image Compression Using SFQ
1/27
4/22/2012
1
THESIS PROGRESS PRESENTATION
O N
Implementation of Efficient Medical Image
Compression Using Space-Frequency Quantization
Presented byKrishna Kumar
R.N.- 2010EL27
M. tech 4rd sem.
(DIGITAL SYSTEM)
Under The Guidance Of
Dr. Basant Kumar
Assist. Prof., ECED
MNNIT, Allahabad
Outline
(1) INTRODUCTION.
(2) EZW CODING STEPS.
(2) MDL SFQ CODER DESIGN STEPS.
(3) MODELLING OF SUB-BAND WAVELET
COEFFICIENTS.
(4) RESULTS .
( 5)CONCLUSION.
8/2/2019 Medical Image Compression Using SFQ
2/27
4/22/2012
2
Data Compression in TelemedicineData Compression in Telemedicine
Medical images require :
Larger memory space for storage
Higher bandwidth
Higher transmission time
Types of compression:
Lossless compression
limited compression ratio
Lossy compression
can be used with compromising quality.
Compression Technique Used
WAVELET TRANSFORM METHODS.
(TRANSFORM BASED IMAGE COMPRESSION).
ADVANTAGES :
Avoids blocking artefacts.
Facilitates progressive transmission of images.
Better matched to the hvs(human visual system)
characteristics
Compression scalable to achieve high compression ratios.
Very efficient at low bit rates.
8/2/2019 Medical Image Compression Using SFQ
3/27
4/22/2012
3
Step 1:Compute DWT Coefficients of the medical image.
Step 2:Set threshold and Iteration count.
Step 3:Apply Zero-tree pruning algorithm.
Step 4:Divide the survivor set of DWT coefficients into two sets of
positive and negative coefficients.
Step 5:Apply Lloyd-Max algorithm based uniform scalar quantizer
on positive and negative survivor sets.
Step 6:Apply Huffman code for encoding quantized DWT
coefficients.
Steps Followed in MDL SFQ Coder Design
Sub-band decomposition of an N x M image
H0
HH
H1
H0
H1
H0
HL
LH
LL
H1 2
2
2
2
2
2
a0
a1
N
MN/2
M/2
M/2
N
8/2/2019 Medical Image Compression Using SFQ
4/27
4/22/2012
4
Subband Structure (4- Level)
N/8
N/4
N/2
N
Scanning of wavelet coefficients for encoding
using EZW
8/2/2019 Medical Image Compression Using SFQ
5/27
4/22/2012
5
2. EZW encoding:Lets use 2-Level decomposition
26 6
-7 7
4
46
-4
-22
-34
0-2
1013
2log 260T = 2 = 16
sp zr zr zr
26sL
EZW encoding (Continued..)
The reconstructed value of this coefficient is
24 0
0 0
0
00
0
00
00
00
00
01.5 T = 24
8/2/2019 Medical Image Compression Using SFQ
6/27
4/22/2012
6
EZW encoding (Continued..)
Correction of a two level quantizer with reconstruction level
T0/4.
28 0
0 0
0
00
0
00
00
00
00
EZW encoding (Continued..)
Now w e reduce the th reshold by a factor o f 2and repeat th e pro cess.
* 6
-7 7
4
46
-4
-22
-34
0-2
1013
01
TT = = 8
2
iz zr zr sp sp iz iz
8/2/2019 Medical Image Compression Using SFQ
7/27
4/22/2012
7
EZW encoding (Continued..)
Now th e t hreshold coeff icients are reconstr uctedw ith values 1.5T1=12.
28 0
0 0
0
00
0
00
00
00
1212
sL = 26,13,10
EZW encoding (Continued..)
26 0
0 0
0
00
0
00
00
00
1014
0TCorrection becomes = 24
8/2/2019 Medical Image Compression Using SFQ
8/27
4/22/2012
8
Scalar Quantization
M ean square quant izat ion error
To design of q uant izat ion m eans deter m ine t heboundar ies bj and level yj tha t m in im izesquant izat ion error .
(1) i
i-1
bM 22
q x
j=1 b
= f (x)dxix-y
Scalar Quantization(Continued..)
These values of quantization can be
determined with the help of Lloyd-Max
quantizer.
It is a pdf optimized quantizer
8/2/2019 Medical Image Compression Using SFQ
9/27
4/22/2012
9
Scalar Quantization(Continued..)
The design equation for Lloyd-Max quantizer is
j
j-1
j
j-1
b
xb
j b
xb
x f x dx
y =f x dx
j+1 j
j
y + yb =
2
Needs For Statistical Modelling of Medical
Image
To characterized the image in the transform domain.
Benefits in model dependent quantization scheme
Characteristic of medical image wavelet coefficients:
Peaky
Heavy-tailed
Non-gaussian statistics
8/2/2019 Medical Image Compression Using SFQ
10/27
4/22/2012
10
Some heavy-tailed distributions
Generalized Student-t distribution
Generalized Pareto distribution
Weibull distribution
Gamma distribution
Gamma Distribution
: standard deviat io n
8/2/2019 Medical Image Compression Using SFQ
11/27
4/22/2012
11
Comparison of Various Distribution
Level 2 Distribution Chi-square value
(positive data)
Chi-square value
(negativedata)
HL 2 Generalized Pareto 0.9980 0.5692
Weibull 1.0687 0.5417
Generalized student-t 3.1374 0.2028
Gamma 0.9631 0.5231
LH 2 Generalized Pareto 1.2523 0.5977
Weibull 1.2552 0.5714
Generalized student-t 4.4818 0.2280
Gamma 1.0240 0.4985
HH 2 Generalized Pareto 0.8593 0.5741
Weibull 0.8792 0.5473
Generalized student-t 1.3042 0.2239
Gamma 0.8315 0.4996
Chi- Square Value (+Ve Data)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
HL2 LH2 HH2
G.Pareto
W e ibu l l
G.Student- t
G amma
Sub-
bands
8/2/2019 Medical Image Compression Using SFQ
12/27
4/22/2012
12
Chi- Square Value (-Ve Data)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
HL2 LH2 HH2
G.Pareto
W e ibu l l
G.Student- t
G amma
Sub-bands
Jointly Optimization of SFQ Quantizer
M DL Criter ion is used
Best m odel is th at w hich g ives m in im umdescript ion length .
The code length for f ind ing the b in indices
W here: m- no. of quant izat ion levels- b in w i d t hCk- no. of coef f i c ients in Kth b insn- l engt h o f subband
2( , ) log kk
CL X m C
n
8/2/2019 Medical Image Compression Using SFQ
13/27
4/22/2012
13
Jointly Optimization of SFQ Quantizer(Continued..)
M DL cr iter ion can be w r i t ten as
2
2, 12
1( , ) ( ) ( , )
2 log
n
ij ij
i je
L X X x x L X m
Jointly Optimization of SFQ Quantizer
(Continued..)
To optimize jointly the two quantization we
use the principle of bit allocation,
The best performance is achieved when the
two quantizer operated on the same slope
(=R/D) on RD curve.
8/2/2019 Medical Image Compression Using SFQ
14/27
4/22/2012
14
RESULTSPerformance Comparison
IM AGES BPP=.25 BPP=.5 BPP=1
SPIHT M DL PROPOSEDSFQ M DL-SFQ
SPIHT M DL PROPOSEDSFQ M DL-SFQ
SPIHT M DL PROPOSEDSFQ M DL-SFQ
US-1 42.10 42 .92 4 4 . 8 2 46 .81 47.53 4 9 . 1 1 51 .05 51.40 5 2 . 9 0
US-2 43.27 45 .6 4 6 . 9 2 51 .7 52 .01 5 3 . 1 3 51 .90 52.40 5 4 . 1 1
CT-140.32 42 .47 4 3 . 2 3 43 .67 45.44 4 6 . 7 7 47 .82 49.00 5 0 . 4 2
CT-2 40.58 42 .24 4 2 . 9 6 42 .79 44.77 4 6 . 2 2 47 48 .35 4 9 . 6 7
MR-1 39.80 41 .27 4 2 . 0 7 43 .31 44.17 4 5 . 3 0 44 .98 46.27 4 7 . 5 3
MR-2 39.00 39 .81 4 0 . 0 1 41 .70 42.72 4 3 . 8 7 43 .30 44.82 4 6 . 4 2
PSNR Comparison for US-1 Image
34
36
38
40
42
44
46
48
BPP=0.25 BPP=0.5 BPP=1
SPIHT
M DL-SFQ
Proposed SFQ
8/2/2019 Medical Image Compression Using SFQ
15/27
4/22/2012
15
PSNR Comparison for US-2 Image
34
36
38
40
42
44
46
48
BPP=0.25 BPP=0.5 BPP=1
SPIHT
M DL-SFQ
Propo sed SFQ
PSNR Comparison for CT-1 Image
34
36
38
40
42
44
46
48
BPP=0.25 BPP=0.5 BPP=1
SPIHT
M DL-SFQ
Propo sed SFQ
8/2/2019 Medical Image Compression Using SFQ
16/27
4/22/2012
16
PSNR Comparison for CT-2 Image
34
36
38
40
42
44
46
48
BPP=0.25 BPP=0.5 BPP=1
SPIHT
M DL-SFQ
Proposed SFQ
PSNR Comparison for MR-1 Image
34
36
38
40
42
44
46
48
BPP=0.25 BPP=0.5 BPP=1
SPIHT
M DL-SFQ
Proposed SFQ
8/2/2019 Medical Image Compression Using SFQ
17/27
4/22/2012
17
PSNR Comparison for MR-2 Image
34
36
38
40
42
44
46
48
BPP=0.25 BPP=0.5 BPP=1
SPIHT
M DL-SFQ
Proposed SFQ
Image US-1
8/2/2019 Medical Image Compression Using SFQ
18/27
4/22/2012
18
Image US-1graph: psnr vs. bpp
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
5
10
15
20
25
30
35
40
45
50
bpp
PSNR
MDL SFQ
SFQ
Prosed MDL SFQ
IM AGE US-2
8/2/2019 Medical Image Compression Using SFQ
19/27
4/22/2012
19
IM AGE US-2
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
5
10
15
20
25
30
35
40
45
bpp
P
SNR
SPIHT
MDL SFQPropose MDL SFQ
IM AGE: CT-1
8/2/2019 Medical Image Compression Using SFQ
20/27
4/22/2012
20
IM AGE: CT-1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
5
10
15
20
25
30
35
40
45
50
bpp
PSNR
MDL SFQ
Propose MDL SFQ
IM AGE: CT-2
8/2/2019 Medical Image Compression Using SFQ
21/27
4/22/2012
21
IM AGE: CT-2
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
5
10
15
20
25
30
35
40
45
50
bpp
PSNR
MDL SFQ
Proposed MDL SFQ
IM AGE: M R-1
8/2/2019 Medical Image Compression Using SFQ
22/27
4/22/2012
22
IM AGE: M R-1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
5
10
15
20
25
30
35
40
45
50
bpp
PSNR
MDL SFQ
Propose MDL SFQ
IM AGE: M R-2
8/2/2019 Medical Image Compression Using SFQ
23/27
4/22/2012
23
IM AGE: M R-2
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
5
10
15
20
25
30
35
40
45
bpp
PSNR
Proposed MDL SFQ
MDL SFQ
Conclusions
PROPOSED MDL-SFQ GIVES 1.18dB(APPROX)
IMPROVEMENT OVER EXITING MDL-SFQ.
PROPOSED MDL-SFQ GIVES IMPROVED RESULT ON
US,CT and MR IMAGES.
IMPROVEMENT IN PSNR OF CT AND MR IMAGES ARE
VERY LESS AS COMPARED TO US IMAGE
8/2/2019 Medical Image Compression Using SFQ
24/27
4/22/2012
24
Fur t her Wo rk
Im plem ent ation of ROI SFQ Coder.
REFERENCES
[ 1] .XlON G, Z., RAM CHAN DRAN , K., an d ORCHARD, M . T. (1997):' Sp ace f req u en cy q uan t izat io n f or w ave let im a ge co din g' , IEEETra ns.Im ag e Pro cess., 6, p. 67 7 6 93.
[ 2] .XlON G, Z., RAM CHAN DRAN , K., an d ORCHARD, M . T. (1998):'W avelet packet im age cod ing using space-f requency
quant izat ion ' ,IEEE Tra ns. Im ag e Process., 7, p p. 8 92 898
[ 3 ]. RA JPO OT, N . M . , W ILSO N , R. G., M EYER, F. G. , an d CO IFM A N , R.
R.,(2 00 3): ' Ad ap t iv e w a ve le t p acke t b asis se le ct i on f o r ze ro t r eeimage cod ing ' , IEEE Tra ns. Im ag e Process., 12, p p. 1 460 147 1
8/2/2019 Medical Image Compression Using SFQ
25/27
4/22/2012
25
REFEREN CES (CON T.)
[ 4] .PRZELA SKOW SKI, A., KAZU BEK, M ., an d JA M ROGIEW I CZ, T.
(1997):,Ef fect ive w avelet based com pression m et hod w it hadapt ive quant izat ion t hresho ld and zero -t ree cod ing ' .Proc.SPIE, M ult im edia Sto ra ge a nd Arch iving Syste m -II, 3229 , pp . 348
[ 5] .PRZELA SKOW SKI, A . (1 998 ):,' Fit t in g q uan t izat io n sch em e t om u l t ir eso lu t io n d et ail p re se rv in g co m p r essio n al go r it h m ' , Pr oc.
IEEE, p . 485 488[ 6] .PEA RLM A N W A . A SAID A , A n ew f ast an d ef ficie nt im a ge co de c
b ase d o n se t p ar t it i on in g in h ie rar ch ical t r ee s. ieee t ran sac t ions on circuits and system s for video t echno logy 199 6;6:243 50
[ 7] .SH ARIPO,M .9 (1 99 3): Em b e dd ed im a ge co d in g u sin g ze rot rees o f w avelet coef f icien t s. I EEE Tr ans. Signa l
Process.41,pp.3445-3462.
[8 ] .GERSHO,A.(1992):Pr inc ip les of quant izat ion. IEEE Trans. Oncircuit s an d syst em s.vol ca s-25,n o.-7.
[9] .ANTONINI,M . BARLAUD,M . M ATHIEU,P.DAUBECHIES,I.(1992): Im age cod ing using w avelet
transform. IEEE Tra ns. On im ag e p ro cessing .vol-1,no .-2
REFEREN CES (CON T.)
8/2/2019 Medical Image Compression Using SFQ
26/27
4/22/2012
26
[10] . L. Kau r ,R.C. Ch au han & S.C. Saxen a: Sp ace-f req ue ncyq u an t ise r d esign f or u lt r aso u nd im a ge co m p re ssi on b ase d o nm i n im u m d escr ip t io n le n gt h cr it e rio n . M edical & Biological Eng ineerin g & Com pu t ing 2005 , Vol. 43
[ 11 ].Vlad an Ve lisav lj ev ic, Balt asar Bef er ull-Lo zan o & M ar t in
Ve t te rli: Sace Fr eq u an ct Q uan t izat i on u sin g D ir ect i on le t s.ICIP IEEE 2007 .
REFEREN CES (CON T.)
BOOKS
[1].A WAVELET TOUR OF SIGNAL PROCESSING
(FIRST EDITION).
AUTHOR STEPHAN M ALLAT
[2 ].I NTRODU CTION TO DATA COM PRESSION
(THIRD EDITION).
AUTHO R KHALID SAYOOD
8/2/2019 Medical Image Compression Using SFQ
27/27
4/22/2012
27
Than k you