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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

Showing 126–150 of 496 papers

TitleStatusHype
Variable-Rate Learned Image Compression with Multi-Objective Optimization and Quantization-Reconstruction Offsets—0
NERV++: An Enhanced Implicit Neural Video Representation—0
Neural Video Compression with Feature Modulation—0
Resolution-Agnostic Neural Compression for High-Fidelity Portrait Video Conferencing via Implicit Radiance Fields—0
Distributed Radiance Fields for Edge Video Compression and Metaverse Integration in Autonomous Driving—0
Analysis of Neural Video Compression Networks for 360-Degree Video Coding—0
Extreme Video Compression with Pre-trained Diffusion ModelsCode2
Motion-Adaptive Inference for Flexible Learned B-Frame Compression—0
A Neural-network Enhanced Video Coding Framework beyond ECM—0
Sandwiched Compression: Repurposing Standard Codecs with Neural Network WrappersCode2
Immersive Video Compression using Implicit Neural RepresentationsCode0
UCVC: A Unified Contextual Video Compression Framework with Joint P-frame and B-frame Coding—0
Efficient Dynamic-NeRF Based Volumetric Video Coding with Rate Distortion Optimization—0
LVC-LGMC: Joint Local and Global Motion Compensation for Learned Video Compression—0
A Neural Enhancement Post-Processor with a Dynamic AV1 Encoder Configuration Strategy for CLIC 2024—0
Spatial Decomposition and Temporal Fusion based Inter Prediction for Learned Video Compression—0
ColorVideoVDP: A visual difference predictor for image, video and display distortionsCode2
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions—0
Motion Guided Token Compression for Efficient Masked Video Modeling—0
NU-Class Net: A Novel Approach for Video Quality Enhancement—0
Combining Frame and GOP Embeddings for Neural Video Representation—0
MaskCRT: Masked Conditional Residual Transformer for Learned Video Compression—0
Comparative Study of Hardware and Software Power Measurements in Video CompressionCode0
A Computationally Efficient Neural Video Compression Accelerator Based on a Sparse CNN-Transformer Hybrid Network—0
Geometry-Corrected Geodesic Motion Modeling with Per-Frame Camera Motion for 360-Degree Video CompressionCode0
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