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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 351–400 of 496 papers

TitleStatusHype
MetaNeRV: Meta Neural Representations for Videos with Spatial-Temporal Guidance—0
MFRNet: A New CNN Architecture for Post-Processing and In-loop Filtering—0
Microdosing: Knowledge Distillation for GAN based Compression—0
MobileCodec: Neural Inter-frame Video Compression on Mobile Devices—0
MobileNVC: Real-time 1080p Neural Video Compression on a Mobile Device—0
Modeling the HEVC Encoding Energy Using the Encoder Processing Time—0
ModeNet: Mode Selection Network For Learned Video Coding—0
Motion-Adaptive Inference for Flexible Learned B-Frame Compression—0
A motion-based compression algorithm for resource-constrained video camera traps—0
Motion Free B-frame Coding for Neural Video Compression—0
Motion Guided Token Compression for Efficient Masked Video Modeling—0
Motion Information Propagation for Neural Video Compression—0
MSNeRV: Neural Video Representation with Multi-Scale Feature Fusion—0
Multi-Cue Vehicle Detection for Semantic Video Compression In Georegistered Aerial Videos—0
Multi-Density Attention Network for Loop Filtering in Video Compression—0
Multi-rate adaptive transform coding for video compression—0
Multi-Scale Feature Prediction with Auxiliary-Info for Neural Image Compression—0
Multiscale Motion-Aware and Spatial-Temporal-Channel Contextual Coding Network for Learned Video Compression—0
Multi-Scale Saliency Detection using Dictionary Learning—0
Multiview Video Compression Using Advanced HEVC Screen Content Coding—0
MuZero with Self-competition for Rate Control in VP9 Video Compression—0
Near Optimal Per-Clip Lagrangian Multiplier Prediction in HEVC—0
Neighbor Correspondence Matching for Flow-based Video Frame Synthesis—0
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 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
The Practice of Averaging Rate-Distortion Curves over Testsets to Compare Learned Video Codecs Can Cause Misleading Conclusions—0
On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures—0
Optical Flow Regularization of Implicit Neural Representations for Video Frame Interpolation—0
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