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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 151–200 of 496 papers

TitleStatusHype
Content-Adaptive Motion Rate Adaption for Learned Video Compression—0
Beyond Bjøntegaard: Limits of Video Compression Performance Comparisons—0
Content Adaptive and Error Propagation Aware Deep Video Compression—0
Contactless Pulse Estimation Leveraging Pseudo Labels and Self-Supervision—0
Benchmarking Conventional and Learned Video Codecs with a Low-Delay Configuration—0
A Multiplierless Pruned DCT-like Transformation for Image and Video Compression that Requires 10 Additions Only—0
Accelerating Learnt Video Codecs with Gradient Decay and Layer-wise Distillation—0
Conditional Entropy Coding for Efficient Video Compression—0
AlphaVC: High-Performance and Efficient Learned Video Compression—0
Rethinking Generative Human Video Coding with Implicit Motion Transformation—0
Exploiting Change Blindness for Video Coding: Perspectives from a Less Promising User Study—0
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions—0
Conditional Coding for Flexible Learned Video Compression—0
Exploring the Distributed Video Coding in a Quality Assessment Context—0
Conditional Residual Coding: A Remedy for Bottleneck Problems in Conditional Inter Frame Coding—0
Expanding Synthetic Real-World Degradations for Blind Video Super Resolution—0
FAIVConf: Face enhancement for AI-based Video Conference with Low Bit-rate—0
FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals—0
FANeRV: Frequency Separation and Augmentation based Neural Representation for Video—0
Fast Convolutional Sparse Coding in the Dual Domain—0
Fast Data-independent KLT Approximations Based on Integer Functions—0
Fast Encoding and Decoding for Implicit Video Representation—0
Fast Object Detection in Compressed Video—0
Fast optimization of common basis for matrix set through Common Singular Value Decomposition—0
Compression of High Dynamic Range Video Using the HEVC and H.264/AVC Standards—0
Feedback Recurrent Autoencoder for Video Compression—0
High efficiency compression for object detection—0
Evaluating the Performance of Existing Full-Reference Quality Metrics on High Dynamic Range (HDR) Video Content—0
FLAVC: Learned Video Compression with Feature Level Attention—0
Evaluating Foveated Video Quality Using Entropic Differencing—0
Compressing Video Calls using Synthetic Talking Heads—0
Foveation-based Deep Video Compression without Motion Search—0
EVA^2: Exploiting Temporal Redundancy in Live Computer Vision—0
FrameCorr: Adaptive, Autoencoder-based Neural Compression for Video Reconstruction in Resource and Timing Constrained Network Settings—0
Estimating the HEVC Decoding Energy Using the Decoder Processing Time—0
GaussianVideo: Efficient Video Representation via Hierarchical Gaussian Splatting—0
BBAND Index: A No-Reference Banding Artifact Predictor—0
Generalized Gaussian Entropy Model for Point Cloud Attribute Compression with Dynamic Likelihood Intervals—0
Generative Compression—0
Data-independent Low-complexity KLT Approximations for Image and Video Coding—0
A Low-Parametric Model for Bit-Rate Estimation of VVC Residual Coding—0
Entropy Coding Improvement for Low-complexity Compressive Auto-encoders—0
Generative Video Compression as Hierarchical Variational Inference—0
Geometry-based spherical JND modeling for 360^ display—0
Compressible Motion Fields—0
GIViC: Generative Implicit Video Compression—0
Graph-based Transforms for Video Coding—0
Greedy Sampling of Graph Signals—0
Group-aware Parameter-efficient Updating for Content-Adaptive Neural Video Compression—0
Enhancing 3D Gaussian Splatting Compression via Spatial Condition-based Prediction—0
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