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Improvements to the JPEG-LS prediction scheme
Authors: S. Bedi, E. A. Edirisinghe, and G. Grecos
Source : Image and Vision Computing. Vol. 22, No. 1, 2004, pp. 9-14
Speaker: Chia-Chun Wu (吳佳駿 )
Date : 2004/09/15
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Outline
• JPEG-LS prediction scheme
• Improvements JPEG-LS
• Our method
• Sample images
• Experimental results
• Comments
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JPEG-LS prediction scheme
ˆIn smooth, , when a = b = c = dx a
c b d
a x
In nonsmoth,
min , , if max ,
ˆ max , , if min ,
, if min a,b max ,
a b c a b
x a b c a b
a b c c a b
ˆ: the predictive valuex50 50 50
50 50
31 20 40
30 20
JPEG-LS predictive template
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Improvements JPEG-LS
ˆ3
a b dx
1 1
2
max , OR min a,b
AND
c a b T c T
abs a b T
1 2, Ideally T T
c b d
a x
200 50 5
50 30
200 50 8
50 36
ˆ (50+50+8)/3=108/3=36 x
Diagonal edge T1=20, T2=0
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Our method
ˆ3
a b cx
c b d
a x
200 50 8
50 30
200 50 8
50 100
ˆ (50+50+200)/3=300/3=100 x
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Sample images
Airplane Baboon
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Sample images
Barb Boat
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Sample images
Girl Gold
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Sample images
Lena Lenna
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Sample images
Pepper Sailboat
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Experimental results
N
2
ii 1
ˆxPMSE
ix
N
78 90 74
64 145 142
100 63 97
70 87 76
60 140 135
100 59 99
2 2 2 2 2 2 2 2 2PMSE=(8 3 2 4 5 7 0 4 2 ) / 9
(64 9 4 16 25 49 0 16 4) /
9
187 0 / 9 2 .7
Predictive mean squared error
N = 9
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Experimental resultsTable 1 Lossless compression ratios of all test images (Unit: Bytes)
Image 傳統JPEG-LS
作者提出的方法
我們的方法
Airplane 118204 118210 121769
Baboon 184298 184467 186268
Barb 156728 156830 159434
Boat 134376 134432 137859
Girl 123198 123199 125974
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Experimental resultsTable 2 Lossless compression ratios of all test images (Unit: Bytes)
Image 傳統JPEG-LS
作者提出的方法
我們的方法
Gold 138765 138778 141910
Lena 121322 121316 125340
Lenna 123200 123202 125604
Pepper 126636 126630 128687
Sailboat 156263 156286 157521
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Experimental resultsTable 3 PMSE values of all test images (T1=20, T2=0)
Image 傳統JPEG-LS
作者提出的方法
我們的方法
Airplane 33.135755 32.958340 113.812148
Baboon 249.629536 251.804470 393.657663
Barb 186.421616 185.490680 247.140415
Boat 49.250247 49.718750 148.811983
Girl 20.418006 20.301835 69.531317
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Experimental resultsTable 4PMSE values of all test images (T1=20, T2=0)
Image 傳統JPEG-LS
作者提出的方法
我們的方法
Gold 33.715391 33.942265 88.004221
Lena 31.184482 30.837186 90.242236
Lenna 32.613686 31.928698 86.730731
Pepper 29.134177 28.974733 85.146601
Sailboat 89.210825 89.294461 135.007134
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Comments
• 本篇論文的預測方法,獲得的 PMSE整較傳統的 JPEG-LS好,但是影像整體的壓縮率卻沒有明顯的提升。
• 跟傳統的 JPEG-LS及本篇論文的方法比較,目前我們提的預測方法,並沒有提升預測的準確度及影像的壓縮率。
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Source Code
if (Rc >= MAX(Ra,Rb))
Px = MIN(Ra,Rb);
else if (Rc <= MIN(Ra,Rb))
Px = MAX(Ra,Rb);
else Px = (Ra + Rb - Rc);
Errval = (Ix - Px);
PMSE = PMSE+ (Errval*Errval);
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Source Code if ( (((Rc -MAX(Ra,Rb)) > Threshold1) || ((MIN(Ra,Rb)-Rc) > Threshold1)) && (ABS(Ra-Rb) <= Threshold2) ) { Px=(Ra + Rb + Rd)/3; } else if (Rc >= MAX(Ra,Rb)) Px = MIN(Ra,Rb); else if (Rc <= MIN(Ra,Rb)) Px = MAX(Ra,Rb); else Px = (Ra + Rb - Rc); Errval = (Ix - Px); PMSE = PMSE+ (Errval*Errval);
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Source Code
Px = (Ra + Rb + Rc) /3;
Errval = (Ix - Px);
PMSE = PMSE+ (Errval*Errval);