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

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

  • CALL FOR PROPOSALS: The IEEE Workshop on Automatic Speech Recognition and Understanding is now soliciting proposals…
  • authors have started uploading their conference slides and posters to IEEE SPS SigPort! Get a sneak pea…
  • DEADLINE EXTENDED: The IEEE Journal of Selected Topics in Signal Processing is accepting papers for a Special Issue…
  • Voting for the IEEE SPS 5-Minute Video Clip Contest is now live! Check out the three finalists and cast your vote f…
  • CALL FOR PROPOSALS: Now seeking proposals for the 2024 IEEE International Workshop on Machine Learning for Signal P…

SPS Videos

Signal Processing in Home Assistants


Multimedia Forensics

Careers in Signal Processing             


Under the Radar