Video Compression
102 papers with code • 0 benchmarks • 4 datasets
Video Compression is a process of reducing the size of an image or video file by exploiting spatial and temporal redundancies within an image or video frame and across multiple video frames. The ultimate goal of a successful Video Compression system is to reduce data volume while retaining the perceptual quality of the decompressed data.
Source: Adversarial Video Compression Guided by Soft Edge Detection
Benchmarks
These leaderboards are used to track progress in Video Compression
Libraries
Use these libraries to find Video Compression models and implementations
BVI-DVC
YT-UGC
Deep Fakes Dataset
Latest papers with no code
One-Click Upgrade from 2D to 3D: Sandwiched RGB-D Video Compression for Stereoscopic Teleconferencing
In this paper, we propose a new approach to upgrade a 2D video codec to support stereo RGB-D video compression, by wrapping it with a neural pre- and post-processor pair.
Image and Video Compression using Generative Sparse Representation with Fidelity Controls
Our framework can be conveniently used for both learned image compression (LIC) and learned video compression (LVC).
Encoder-Quantization-Motion-based Video Quality Metrics
In this work we merge several datasets into one to support the creation of a metric tailored for video compression and scaling.
Task-Aware Encoder Control for Deep Video Compression
Prior research on deep video compression (DVC) for machine tasks typically necessitates training a unique codec for each specific task, mandating a dedicated decoder per task.
Uncertainty-Aware Deep Video Compression with Ensembles
Deep learning-based video compression is a challenging task, and many previous state-of-the-art learning-based video codecs use optical flows to exploit the temporal correlation between successive frames and then compress the residual error.
Low-Latency Neural Stereo Streaming
The rise of new video modalities like virtual reality or autonomous driving has increased the demand for efficient multi-view video compression methods, both in terms of rate-distortion (R-D) performance and in terms of delay and runtime.
Impact of Video Compression Artifacts on Fisheye Camera Visual Perception Tasks
It is essential to prove that lossy video compression artifacts do not impact the performance of the perception algorithms.
Hierarchical Frequency-based Upsampling and Refining for Compressed Video Quality Enhancement
The goal of video quality enhancement is to reduce compression artifacts and reconstruct a visually-pleasant result.
Channel-wise Feature Decorrelation for Enhanced Learned Image Compression
The emerging Learned Compression (LC) replaces the traditional codec modules with Deep Neural Networks (DNN), which are trained end-to-end for rate-distortion performance.
Image Coding for Machines with Edge Information Learning Using Segment Anything
We also show that SA-NeRV is superior to ordinary NeRV in video compression for machines.
