1. Pei Qi ECE at UW-Madison pqi@cae.wisc.edu
ECE738 Advanced image processing
Instructor Dr. Hu
Impact analysis of digital watermarking on perceptual quality using HVS models
Pei Qi
ECE at UW-Madison 1

2. Outline Backgrounds Perceptual quality metrics Simulation and analysis
Conclusions

3. Backgrounds A often neglected issue - More focus on robustness
- ignore how to evaluate the quality of the watermarked image
Perceptual quality requirement for watermarking approaches
Transparency - the distortion introduced by embedding watermarks must be imperceptible
How to evaluate the perceptual quality of image
- Subjective Assessments
- Objective Assessments

4. Current perceptual quality metrics
Current two types of visual quality measures
- Subjective assessment
Visual quality is judged by human observers
A. Two alternative, Forced Choice (2AFC)
Key points:
1) A pair of images (original and watermarked)
2) Decide which one has higher quality
3) Statistical analysis (tiny difference)
50% : 50%

5. Current perceptual quality metrics
Current two types of visual quality measures
- Subjective assessment
Visual quality is judged by human observer
B. Five – Scale Rating System (ITU-R Rec.500)
Observers are request to rate the quality of the watermarked image to the different quality scale.

6. Current perceptual quality metrics
Feature
- Decision made by human observer
Disadvantage of subjective assessments
- Expensive, non-repeatable,
- Hard to distinguish very small difference between original image and watermarked image
Objective measures needed

7. Current perceptual quality metrics
Objective assessment
- Currently popular metric: SNR, PSNR
1. Error Function
e(x,y) = I(x,y) – I’(x,y)
2. Mean Square Error (MSE)
3. Signal to noise ratio (SNR)
4. Peak Signal to noise ratio (PSNR)
Maximum pixel’s luminance
value (0~255)

8. Current perceptual quality metrics
Feature
1) Mean Square Error (MSE) based
2) Independent of images
Disadvantages
- Ignore the fact that judging the perceptual quality of image significantly depends on the human observers
- Don’t take into account the effect of HVS

9. Requirements for improved measures
Ideally, the improved methods are ought to combine merits of the two types of assessments
Automated, objective measure
Consider Human visual System (HVS)

10. Two Improved measures Weighted PSNR (WPSNR)
- Based on the fact that human eye is less sensitive to changes in textured areas than in smooth areas
- WPSNR use an additional parameter called Noise visibility Function (NVF), a texture masking function, as a weight factor
Any VNF < 1, WPSNR will
be slightly higher than PSNR.
Reflect human eye is less sen-
sitive to changes in textured
areas.

11. Two Improved measures What’s NVF?
- NVF uses a Gaussian model to estimate how much
texture exists in any area of an image
Behavior:
- flat regions NVF -> 1
- edge and textured regions NVF -> 0
- Simplified NVF Function
Where is the luminance variance for the block of pixels used to
estimate NVF and NORM is a normalization function.

12. Two Improved measures Based on Watson visual models
- HVS Consists of JND thresholds (consider the properties of HVS)
- Generate a 8 X 8 perceptual threshold matrix of DCT transform
- Compare watermarked image with original block by block using perceptual threshold matrix as reference
- Compute perceptual error (average of all blocks error)

13. Two Improved measures Compare 8*8 blocks of the watermarked image with
the corresponding blocks of the original image to
determine if the block has been modified to the
extent that the modification can be perceptible
to human observers. Here we compute the average
perceptual error of all blocks error
Block(i)
Block(i)
Perceptual threshold matrix
derived from Batson visual
models

14. Simulation and Analysis
All test images are 8-bit grayscale images of size 256x256 pixels
Cox watermarking approach
Original images are:
1) baboon.bmp heavily textured image
2) lena.bmp common image
baboon
lena

15. Simulation and Analysis
General
accept
threshold
35~40 dB
a=0.15
psnr=32.5
wpsnr=36.1
a=0.1
psnr=34.9
wpsnr=38.2
original
a=0.1
psnr=33.6
wpsnr=35.3
a=0.15
psnr=30.3
wpsnr=31.6
original

16. Simulation and Analysis
When scaling factor fixed, textured image ‘Baboon’ has higher psnr and wpsnr
The wpsrn value is more close to psnr in lena (because of nvf)
3) This indicate that for textured images, we can increase more watermark energe without sacrificing perceptual quality.

17. Simulation and Analysis
We can observe 1) Moretextured image ‘baboon’ has much smaller
perceptual error than ‘lena’.
2) In other words, image ‘baboon’ is more far from JND
threshold, while image ‘pool’ is more likely approach
JND threshold.
3) Therefore, ‘baboon’ has higher perceptual capacity

18. Conclusions
Two improved methods (WPSNR and JND-based) taking into account the effect of HVS are more effectively and precisely in evaluating the perceptual quality of watermarked image.
However, due to the complexity of HVS, so far we cannot take full advantage of HVS, therefore yell the limitation of using these visual models.
Thank you