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Modeling the effects of blurriness in mobile ads

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The creative is the image of the ad that the user sees and engages with upon viewing. This talk studies the effect of an ad creative’s specifications and quality of render in performance campaigns and suggests a playbook for digital marketers based on the findings and insights. 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.

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Modeling the effects of blurriness in mobile ads

  1. 1. Modeling the effects of blurriness in mobile ads Abhijith C.
  2. 2. What the user sees? .... the advertisement
  3. 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. 4. Our World … world of slots and creatives
  5. 5. Our World ● Different and top creative sizes that we have (image) ● Different and top slots that we have (image)
  6. 6. Takeaway … what we can actually do with this
  7. 7. - Takeaway 1 - Takeaway 2
  8. 8. Superresolution … a class of techniques that enhance the resolution of an imaging system
  9. 9. Example of super resolution
  10. 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. 11. Generative Adversarial Network - Crash Course
  12. 12. Super Resolution GAN (SRGAN) GAN Loss = Content Loss + Adversarial Loss
  13. 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. 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. 15. What the SRGAN model is trained on?
  16. 16. Super resolving our creatives Examples
  17. 17. Examples
  18. 18. Where it goes wrong Examples
  19. 19. Examples
  20. 20. Conclusion Takeaways

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