1.
Modeling the effects of blurriness in mobile ads
Abhijith C.

2.
What the user sees?
.... the advertisement

3.
1. To look at the effect of an ad creative’s specifications and quality of render in
performance campaigns
2. An offline study on an ad creative’s specifications such as resolution, aspect
ratio, handset density, device orientation, etc. on fit (slot size x creative size)
and quality of the render (blurriness / sharpness, etc.) to provide digital
marketers with helpful insights.

4.
Our World
… world of slots and creatives

5.
Our World
● Different and top creative sizes that we have (image)
● Different and top slots that we have (image)

6.
Takeaway
… what we can actually do with this

7.
- Takeaway 1
- Takeaway 2

8.
Superresolution
… a class of techniques that enhance the resolution of an
imaging system

9.
Example of super resolution

10.
Existing methods for super resolution
1. Interpolation (produces blurry images)
2. Example-Based SR algorithms, the Sparse-Coding-Based method is one of
the most popular.
Deep learning provides better solution to get optimized images
3. SRCNN
4. VDSR
5. SRGAN

11.
Generative Adversarial Network - Crash Course

12.
Super Resolution GAN (SRGAN)
GAN Loss = Content Loss + Adversarial Loss

13.
1. A common optimisation target for SR algorithms is minimisation of the mean-
squared error on a pixel-by-pixel basis between the recovered image and the
ground truth. Using MSE also has the advantage of maximising the peak
signal-to-noise ratio (PSNR), a common measure used to evaluate SR
algorithms.
2. Pixel-wise loss functions such as MSE struggle to handle the uncertainty
inherent in recovering lost high-frequency details such as texture; minimizing
MSE encourages finding pixel-wise averages of plausible solutions which are
typically overly-smooth and thus have poor perceptual quality.

14.
It’s a weighted sum of a content loss component, and an adversarial loss
component.
The Loss Function
It’s a weighted sum of a content loss component, and an adversarial loss
component.
A difference sum of the
feature space from the
VGG network instead of the
pixels
Standard
adversarial loss

15.
What the SRGAN model is trained on?

16.
Super resolving our creatives
Examples

17.
Examples

18.
Where it goes wrong
Examples

19.
Examples

20.
Conclusion
Takeaways