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 186-198 Authors : Zhibin Pan, Lingfei Wang Speaker : Chia-Shuo Shih Date : 2017/12/21 1

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

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

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

Introduction --- Vector Quantization (VQ) Codebook 5

Introduction --- Side Match Vector Quantization(SMVQ) State Codebook (SC) 0 135,135,……134 1 136,136,……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. 1992. 6

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

Introduction --- Search Order Coding (SOC) 30  (00) 21  (01) 31  (10) 18  (11) 30  (00) 21  (01) 31  (10) 18  (11) 31 29 = 010 = 100011101 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

Introduction --- Reversible Data Hiding based on SOC 18 (OIV) 21 31 30 (SOC) 29 32 18 (OIV) 21 (SOC) 31 30 29 32 101101 1 100010010 100010010 100010101 01100010101 1 100011111 100011111 1 100011110 100011110 000 000 1 010 100011111 9

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

Proposed method n n VQ VQ 11

Proposed method -- Side-match vector quantization (SMVQ) 2/2 State Codebook SC 0 135,135,135,134 1 136,136,136,135 2 . 3 . . . . 155,155,154,153 . 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

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

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

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 10010010 15

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

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

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

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

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

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

-END- 22