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
Scalable Neural Video Representations with Learnable Positional Features
subin-kim-cv/NVP
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Succinct representation of complex signals using coordinate-based neural representations (CNRs) has seen great progress, and several recent efforts focus on extending them for handling videos.
Compressing Video Calls using Synthetic Talking Heads
berlin0610/awesome-generative-face-video-coding
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We use a state-of-the-art face reenactment network to detect key points in the non-pivot frames and transmit them to the receiver.
Bit Allocation using Optimization
tongdaxu/bit-allocation-using-optimization
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In this paper, we consider the problem of bit allocation in Neural Video Compression (NVC).
B-CANF: Adaptive B-frame Coding with Conditional Augmented Normalizing Flows
NYCU-MAPL/B-CANF
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Our B*-frames allow greater flexibility in specifying the group-of-pictures (GOP) structure by reusing the B-frame codec to mimic P-frame coding, without the need for an additional, separate P-frame codec.
Extreme-scale Talking-Face Video Upsampling with Audio-Visual Priors
We show that when we process this $8\times8$ video with the right set of audio and image priors, we can obtain a full-length, $256\times256$ video.
Exploring Long- and Short-Range Temporal Information for Learned Video Compression
huairui/lstvc
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Learned video compression methods have gained a variety of interest in the video coding community since they have matched or even exceeded the rate-distortion (RD) performance of traditional video codecs.
Explaining Deepfake Detection by Analysing Image Matching
Besides the supervision of binary labels, deepfake detection models implicitly learn artifact-relevant visual concepts through the FST-Matching (i. e. the matching fake, source, target images) in the training set.
Enhancing HDR Video Compression through CNN-based Effective Bit Depth Adaptation
In this work, we modify the MFRNet network architecture to enable multiple frame processing, and the new network, multi-frame MFRNet, has been integrated into the EBDA framework using two Versatile Video Coding (VVC) host codecs: VTM 16. 2 and the Fraunhofer Versatile Video Encoder (VVenC 1. 4. 0).
Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression
microsoft/dcvc
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Besides estimating the probability distribution, our entropy model also generates the quantization step at spatial-channel-wise.
CANF-VC: Conditional Augmented Normalizing Flows for Video Compression
CANF-VC represents a new attempt that leverages the conditional ANF to learn a video generative model for conditional inter-frame coding.
