1. Mean quantization based image watermarking
Source: Image and Vision Computing, vol. 21,
no. 8, Aug. 2003, pp
Authors: L.-H. Chen and J.-J. Lin
Speaker: Wei-Liang Tai

2. Outline Introduction to Watermarking
Introduction to Wavelet Transforms
The Proposed Method
Experimental Results
Conclusions

3. Introduction - Watermarking
Visible
Invisible

4. Introduction - Watermarking (Cont.)
Invisible Watermarking
Robust Watermarking
It can against image processing such as filtering, lossy compression, sharpening, and so on.
Fragile Watermarking
It can detect any tiny alteration to the pixel value.
Semi-fragile Watermarking
It is moderately robust to lossy compression such as JPEG.

5. Introduction – Wavelet Transforms
Haar Wavelet Transform
Phase 1) Horizontal : ㄅ ㄆ ㄇ ㄈ ㄉ ㄊ ㄋ ㄌ ㄍ ㄎ ㄏ ㄐ ㄑ ㄒ ㄓ ㄔ A B C D E F G H I J K L M N O P
A+B
C+D
A-B
C-D
E+F
G+H
E-F
G-H
I+J
K+L
I-J
K-L
M+N
O+P
M-N
O-P
Phase 2) Vertical : ㄅ ㄆ ㄇ ㄈ ㄉ ㄊ ㄋ ㄌ ㄍ ㄎ ㄏ ㄐ ㄑ ㄒ ㄓ ㄔ
ㄅ+ㄉ
ㄆ+ㄊ
ㄇ+ㄋ
ㄈ+ㄌ
ㄍ+ㄑ
ㄎ+ㄒ
ㄏ+ㄓ
ㄐ+ㄔ
ㄅ-ㄉ
ㄆ-ㄊ
ㄇ-ㄋ
ㄈ-ㄌ
ㄍ-ㄑ
ㄎ-ㄒ
ㄏ-ㄓ
ㄐ-ㄔ

6. Introduction – Wavelet Transforms (Cont.)
JND (Just Noticeable Difference)
Range = [ T-JND, T+JND ]

7. The Proposed Method Watermark W=w1w2…wL Embed in subbands LL1 and HH1
N where is the number of coefficients used to encode a watermark bit
ex: N=3, w1=0
Normalize, and compute mean value
HH1
JND 6 7 9 4 6 7 4 6 7 2 3 -2 -4 16 10 20 25 18 26 16 10 20
Extract
Inverse - normalize

8. Experimental Results
Original image
Watermarked image

9. Experimental Results (Cont.)

10. Conclusions Exploiting human visual system (HVS)
Robust to common attacks