Search Results for author:
Found 9 papers, 4 papers with code
Motion-Adaptive Inference for Flexible Learned B-Frame Compression
As a remedy, we propose controlling the motion range for flow prediction during inference (to approximately match the range of motions in the training data) by downsampling video frames adaptively according to amount of motion and level of hierarchy in order to compress all B-frames using a single flexible-rate model.
Multi-Scale Deformable Alignment and Content-Adaptive Inference for Flexible-Rate Bi-Directional Video Compression
The lack of ability to adapt the motion compensation model to video content is an important limitation of current end-to-end learned video compression models.
Flexible-Rate Learned Hierarchical Bi-Directional Video Compression With Motion Refinement and Frame-Level Bit Allocation
This paper presents improvements and novel additions to our recent work on end-to-end optimized hierarchical bi-directional video compression to further advance the state-of-the-art in learned video compression.
End-to-End Rate-Distortion Optimized Learned Hierarchical Bi-Directional Video Compression
Conventional video compression (VC) methods are based on motion compensated transform coding, and the steps of motion estimation, mode and quantization parameter selection, and entropy coding are optimized individually due to the combinatorial nature of the end-to-end optimization problem.
DFPN: Deformable Frame Prediction Network
Learned frame prediction is a current problem of interest in computer vision and video compression.
Self-Organized Variational Autoencoders (Self-VAE) for Learned Image Compression
In end-to-end optimized learned image compression, it is standard practice to use a convolutional variational autoencoder with generalized divisive normalization (GDN) to transform images into a latent space.
On the Computation of PSNR for a Set of Images or Video
Others compute a single PSNR from the arithmetic mean of frame MSEs for each video.
Effect of Architectures and Training Methods on the Performance of Learned Video Frame Prediction
We analyze the performance of feedforward vs. recurrent neural network (RNN) architectures and associated training methods for learned frame prediction.
End-to-End Rate-Distortion Optimization for Bi-Directional Learned Video Compression
Conventional video compression methods employ a linear transform and block motion model, and the steps of motion estimation, mode and quantization parameter selection, and entropy coding are optimized individually due to combinatorial nature of the end-to-end optimization problem.
