1. Novel reversible data hiding scheme for Two-stage VQ compressed images based on search-order coding
Source :Journal of visual Communication and Image Representation
Volume 50, January 2018, Pages
Authors : Zhibin Pan, Lingfei Wang
Speaker : Chia-Shuo Shih
Date : 2017/12/21 1

2. Outline Introduction Proposed method Experimental results Conclusions
Vector-quantization (VQ)
Side-match vector quantization (SMVQ)
Search-order coding (SOC)
Reversible data hiding based on SOC
Proposed method
Experimental results
Conclusions 2

3. Introduction --- Vector Quantization (VQ)
VQ is a lossy image coding technique.
VQ consists of three procedures:
Codebook design
Image Encoding
Image Decoding
[34] Y. Linde, A. Buzo, R. Gray, “Algorithm for Vector Quantizer Design”, IEEE Transactions on Communications, vol. 28, no. 1, pp: 84-95, 1980. 3

4. Introduction --- Vector Quantization (VQ)
Image Encoding
Partition the image into non-overlapped image block
Find the closest codeword in the codebook for each image block x
The index of the closest codeword of x is recorded.
Image Decoding
Reconstruct each block by the codeword in the codebook of its index. 4

5. Introduction --- Vector Quantization (VQ)
Codebook 5

6. Introduction --- Side Match Vector Quantization(SMVQ)
State Codebook (SC)
,135,……134
,136,……135 2 3 . 31 .
[20] T. Kim, “Side match and overlap match vector quantizers for Images”. IEEE Transactions on Image Processing, vol. 1, no. 2, pp: 170～185, Apr 6

7. VQ vs SMVQ VQ
SMVQ
Quality
Better( )
Worse
Bit rate
Higher
Lower( ) 7

8. Introduction --- Search Order Coding (SOC)
30  (00)
21  (01)
31  (10)
18  (11)
30  (00)
21  (01)
31  (10)
18  (11) 31 29
= 010 =
C.H. Hsieh, J. C. Tsai, “Lossless compression of VQ index with search-order coding”. IEEE Transactions on Image Processing, vol. 5, no. 11, pp: 1579 ～ 1582, 1996. 8

9. Introduction --- Reversible Data Hiding based on SOC18
(OIV) 21 31 30
(SOC) 29 32 18
(OIV) 21
(SOC) 31 30 29 32
101101 1 1 1
000
000 1
010 9

10. Proposed method preceding operation: First-stage: Codebook C
Second-stage:
Difference Codebook DC 10

11. Proposed method n n VQ VQ 11

12. Proposed method -- Side-match vector quantization (SMVQ) 2/2
State Codebook SC
,135,135,134
,136,136,135
,155,154,153 . 31 U
(w-1,0)
(w-1,1)
155
183
185
154
184 98 99
120
122
123
(w-1,0)
(w-1,1)
155
154 l
(0,h-1)
(1,h-1)
(0,h-1)
(1,h-1) 12

13. Proposed method 13 First codeword in SC U 155 183 185 154 184 98 99
120
122
123
Difference image block
155,155,154,153
182,183,183,182
99,99,99,99
120,121,122,120 1 2 -1 l
(0,h-1)
(1,h-1)
State Codebook SC 13

14. Proposed method . 14 Difference image block Difference codebook DC
Two-stage VQ
index table 1 2 -1
,0,0,0
,2,-2,-2
,3,-3,-3 3 . 31 1
Encoded by . 14

15. Proposed method 00 10 001 00100 100100 10010010 Secret data
(01) 3
(10)
(00)
Secret data
001 00
00100 1 3 2 4
(01) 1
(10) 2
(00) 4
Secret data 10
100100 15

16. Experimental results
Table 1. The results of amount of SOC indices between Chang and the poposed scheme
(codebook size = 256).
Chang’s scheme 
Our proposed scheme
Improvement
Lena
8307
9896
19.1%
Baboon
2944
3517
19.5%
Airplane
10,177
10111 –
Boat
8592
9109 6%
Peppers
8739
9065
3.8%
Lake
7541
7737
2.6%
Goldhill
6653
9105
36.9%
Zelda
7642
8978
17.5%
Average
7146
8278
15.8% 16

17. Experimental results
Table 2. The comparison of compression rate (CR) between other listed schemes and
our proosed scheme (unit: bpp, codebook size = 256).
Chang et al. 
Kieu et al. 
Lee et al. 
Lin et al.
Pan et al.
Proposed scheme
Lena
0.378
0.367
0.373
0.342
0.358
0.336
Baboon
0.495
0.467
0.496
0.468
0.482
Airplane
0.330
0.327
0.333
0.321
0.324
0.331
Boat
0.366
0.362
0.364
0.359
0.354
Peppers
0.361
0.360
0.355
Lake
0.390
0.391
0.394
0.384
0.386
0.385
Goldhill
0.410
0.398
0.395
0.363
0.403
Zelda
0.388
0.357
Average
0.383
0.370
0.369 17

18. Experimental results
Table 11. The omparison of maximum embedding rate (ER) between other listed schemes
and our proposed scheme (unit: bpi, codebook size = 256).
Chang et al. 
Kieu et al. 
Lee et al. 
Lin et al. 
Pan et al. 
Proposed scheme
Lena
1.95
2.13
2.03
2.53
2.27
2.62
Baboon
0.08
0.53
0.06
0.54
0.29
Airplane
2.72
2.77
2.67
2.86
2.82
2.70
Boat
2.14
2.21
2.18
2.26
2.34
Peppers
2.22
2.24
2.32
Lake
1.76
1.74
1.70
1.86
1.82
1.84
Goldhill
1.44
1.63
1.68
2.19
1.56
Zelda
1.79
2.29
Average
1.87
2.08
1.88
2.09 18

19. Experimental results
Table 13. The comparison of maximum embedding efficiency (EE) between othr listed schemes and
our proposed scheme (codebook size = 256).
Chang et al. 
Kieu et al. 
Lee et al.
Lin et al.
Pan et al.
Chang et al.
Proposed scheme
Lena
0.244
0.266
0.254
0.316
0.283
0.328
Baboon
0.010
0.066
0.008
0.068
0.036
Airplane
0.340
0.346
0.334
0.358
0.352
0.338
Boat
0.268
0.276
0.273
0.282
0.293
Peppers
0.278
0.280
0.290
Lake
0.220
0.218
0.213
0.232
0.228
0.230
Goldhill
0.180
0.204
0.210
0.274
0.195
Zelda
0.224
0.272
0.286
Average
0.234
0.260
0.235
0.261 19

20. Experimental results 20 Scheme Chang et al. Kieu et al. Lee et al.
Table 10. Running time results of simulated schemes (unit: s, codebook size = 256).
Scheme
Chang et al. 
Kieu et al. 
Lee et al. 
Lin et al. 
Pan et al. 
Proposed scheme
Embedding phase
3.61
3.71
3.82
4.23
3.63
25.71
25.33
Extracting phase
0.85
0.71
0.91
0.75
1.05
24.42
23.92
Total time
4.46
4.42
4.73
4.98
4.68
50.13
49.25 20

21. Conclusions Better quality of reconstructed image.
Better compression performance.
The correlation of Two-stage VQ indices is better than that of VQ indices. 21

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