Affine Transformation-Based Deep Frame Prediction

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Affine Transformation-Based Deep Frame Prediction

By: 
Hyomin Choi; Ivan V. Bajić

We propose a neural network model to estimate the current frame from two reference frames, using affine transformation and adaptive spatially-varying filters. The estimated affine transformation allows for using shorter filters compared to existing approaches for deep frame prediction. The predicted frame is used as a reference for coding the current frame. Since the proposed model is available at both encoder and decoder, there is no need to code or transmit motion information for the predicted frame. By making use of dilated convolutions and reduced filter length, our model is significantly smaller, yet more accurate, than any of the neural networks in prior works on this topic. Two versions of the proposed model - one for unidirectional, and one for bi-directional prediction - are trained using a combination of Discrete Cosine Transform (DCT)-based ℓ 1 -loss with various transform sizes, multi-scale Mean Squared Error (MSE) loss, and an object context reconstruction loss. The trained models are integrated with the HEVC video coding pipeline. The experiments show that the proposed models achieve about 7.3%, 5.4%, and 4.2% bit savings for the luminance component on average in the Low delay P, Low delay, and Random access configurations, respectively.

SPS on Twitter

  • SPS is proud to participate in IEEE's new Multiple Society Discount Program! Join two or more participating societi… https://t.co/BnwcM7O7iu
  • IEEE Day is October 4th. Celebrate IEEE Day by attending a local event. Visit the IEEE Day site for a complete list… https://t.co/mESJHTn7ek
  • The Biomedical Imaging and Signal Processing Webinar Series continues on Tuesday, 4 October when Selin Aviyente pre… https://t.co/Gl4bHlWbqh
  • On Wednesday, 26 October, join Dr. DeLiang Wang for a new SPS webinar, "Neural Spectrospatial Filter" - register no… https://t.co/vUkiWC4Am8
  • Join Dr. Peilan Wang and Dr Jun Fang for "Channel State Information Acquisition for Intelligent Reflecting Surface-… https://t.co/jOhyA10xuG

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar