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
Spatial Decomposition and Temporal Fusion based Inter Prediction for Learned Video Compression
With the SDD-based motion model and long short-term temporal contexts fusion, our proposed learned video codec can obtain more accurate inter prediction.
ColorVideoVDP: A visual difference predictor for image, video and display distortions
ColorVideoVDP is a video and image quality metric that models spatial and temporal aspects of vision, for both luminance and color.
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions
We call the aggregate of these contribution maps the Implicit Neural Canvas and we use this concept to demonstrate that the INRs which we study learn to ''see'' the frames they represent in surprising ways.
Motion Guided Token Compression for Efficient Masked Video Modeling
By implementing MGTC with the masking ratio of 25\%, we further augment accuracy by 0. 1 and simultaneously reduce computational costs by over 31\% on Kinetics-400.
NU-Class Net: A Novel Deep Learning-based Approach for Video Quality Enhancement
By employing the NU-Class Net, the video encoder within the video-capturing node can reduce output quality, thereby generating low-bit-rate videos and effectively curtailing both computation and bandwidth requirements at the edge.
MaskCRT: Masked Conditional Residual Transformer for Learned Video Compression
Conditional coding has lately emerged as the mainstream approach to learned video compression.
Comparative Study of Hardware and Software Power Measurements in Video Compression
The environmental impact of video streaming services has been discussed as part of the strategies towards sustainable information and communication technologies.
A Computationally Efficient Neural Video Compression Accelerator Based on a Sparse CNN-Transformer Hybrid Network
Video compression is widely used in digital television, surveillance systems, and virtual reality.
Deep Hierarchical Video Compression
Recently, probabilistic predictive coding that directly models the conditional distribution of latent features across successive frames for temporal redundancy removal has yielded promising results.
C3: High-performance and low-complexity neural compression from a single image or video
On the UVG video benchmark, we match the RD performance of the Video Compression Transformer (Mentzer et al.), a well-established neural video codec, with less than 5k MACs/pixel for decoding.
