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Video Compression

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

Papers

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Showing 226–250 of 496 papers

TitleStatusHype
NERV++: An Enhanced Implicit Neural Video Representation—0
NetFlick: Adversarial Flickering Attacks on Deep Learning Based Video Compression—0
Neural-based Video Compression on Solar Dynamics Observatory Images—0
Neural Face Video Compression using Multiple Views—0
Neural Stereo Video Compression with Hybrid Disparity Compensation—0
Neural Video Coding using Multiscale Motion Compensation and Spatiotemporal Context Model—0
Neural Video Compression using 2D Gaussian Splatting—0
Neural Video Compression using Spatio-Temporal Priors—0
Neural Video Compression with Feature Modulation—0
Neural Video Compression with Temporal Layer-Adaptive Hierarchical B-frame Coding—0
Neural Video Representation for Redundancy Reduction and Consistency Preservation—0
Neural Weight Step Video Compression—0
Non-Local ConvLSTM for Video Compression Artifact Reduction—0
"Notic My Speech" -- Blending Speech Patterns With Multimedia—0
NU-Class Net: A Novel Approach for Video Quality Enhancement—0
NVC-1B: A Large Neural Video Coding Model—0
NVRC: Neural Video Representation Compression—0
Objective video quality metrics application to video codecs comparisons: choosing the best for subjective quality estimation—0
Offline and Online Optical Flow Enhancement for Deep Video Compression—0
One-Click Upgrade from 2D to 3D: Sandwiched RGB-D Video Compression for Stereoscopic Teleconferencing—0
One Transform To Compute Them All: Efficient Fusion-Based Full-Reference Video Quality Assessment—0
Online-Trained Upsampler for Deep Low Complexity Video Compression—0
On Recoverability of Randomly Compressed Tensors with Low CP Rank—0
On the benefit of parameter-driven approaches for the modeling and the prediction of Satisfied User Ratio for compressed video—0
On the Choice of Perception Loss Function for Learned Video Compression—0
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