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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 251–275 of 496 papers

TitleStatusHype
Contactless Pulse Estimation Leveraging Pseudo Labels and Self-Supervision—0
Content Adaptive and Error Propagation Aware Deep Video Compression—0
Content-Adaptive Motion Rate Adaption for Learned Video Compression—0
Context-Aware Neural Video Compression on Solar Dynamics Observatory—0
Convolutional Block Design for Learned Fractional Downsampling—0
CoordFlow: Coordinate Flow for Pixel-wise Neural Video Representation—0
Correcting the Sub-optimal Bit Allocation—0
Cross Modal Compression: Towards Human-comprehensible Semantic Compression—0
Data-independent Low-complexity KLT Approximations for Image and Video Coding—0
DAVD-Net: Deep Audio-Aided Video Decompression of Talking Heads—0
Decision Trees for Complexity Reduction in Video Compression—0
Decomposition, Compression, and Synthesis (DCS)-based Video Coding: A Neural Exploration via Resolution-Adaptive Learning—0
Deep Hierarchical Video Compression—0
Deep Implicit Volume Compression—0
Deep Learned Frame Prediction for Video Compression—0
Deep Learning-Based Real-Time Quality Control of Standard Video Compression for Live Streaming—0
Deep Multi-modality Soft-decoding of Very Low Bit-rate Face Videos—0
Sandwiched Video Compression: Efficiently Extending the Reach of Standard Codecs with Neural Wrappers—0
Scale-Space Flow for End-to-End Optimized Video Compression—0
SCALE SPACE FLOW WITH AUTOREGRESSIVE PRIORS—0
Scene Matters: Model-based Deep Video Compression—0
Screen Content Image Segmentation Using Sparse-Smooth Decomposition—0
Seeing the Arrow of Time—0
Self-Supervised Graph Transformer for Deepfake Detection—0
Self-Supervised Learning of Perceptually Optimized Block Motion Estimates for Video Compression—0
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