Video Compression
101 papers with code • 0 benchmarks • 3 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 implementationsLatest papers
IBVC: Interpolation-driven B-frame Video Compression
Learned B-frame video compression aims to adopt bi-directional motion estimation and motion compensation (MEMC) coding for middle frame reconstruction.
PacketGame: Multi-Stream Packet Gating for Concurrent Video Inference at Scale
The resource efficiency of video analytics workloads is critical for large-scale deployments on edge nodes and cloud clusters.
Neural radiance fields in the industrial and robotics domain: applications, research opportunities and use cases
These experiments include NeRF-based video compression techniques and using NeRFs for 3D motion estimation in the context of collision avoidance.
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.
HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation
Learning-based video compression is currently a popular research topic, offering the potential to compete with conventional standard video codecs.
The Bjøntegaard Bible -- Why your Way of Comparing Video Codecs May Be Wrong
Using additional supporting points inbetween standard points defined by parameters such as the quantization parameter, we assess the interpolation error of the Bj{\o}ntegaard-Delta (BD) calculus and its impact on the final BD value.
Perceptual Quality Assessment of Face Video Compression: A Benchmark and An Effective Method
In this paper, we introduce the large-scale Compressed Face Video Quality Assessment (CFVQA) database, which is the first attempt to systematically understand the perceptual quality and diversified compression distortions in face videos.
HNeRV: A Hybrid Neural Representation for Videos
Such embedding largely limits the regression capacity and internal generalization for video interpolation.
MMVC: Learned Multi-Mode Video Compression with Block-based Prediction Mode Selection and Density-Adaptive Entropy Coding
In this paper, we propose multi-mode video compression (MMVC), a block wise mode ensemble deep video compression framework that selects the optimal mode for feature domain prediction adapting to different motion patterns.
Low-complexity Deep Video Compression with A Distributed Coding Architecture
This has inspired a distributed coding architecture aiming at reducing the encoding complexity.