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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 251–300 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
SFU-HW-Tracks-v1: Object Tracking Dataset on Raw Video Sequences—0
Slimmable Video Codec—0
Sparse Input View Synthesis: 3D Representations and Reliable Priors—0
Spatial Decomposition and Temporal Fusion based Inter Prediction for Learned Video Compression—0
Spatially-Adaptive Learning-Based Image Compression with Hierarchical Multi-Scale Latent Spaces—0
Spatial-Temporal Transformer based Video Compression Framework—0
SR-NeRV: Improving Embedding Efficiency of Neural Video Representation via Super-Resolution—0
SSNVC: Single Stream Neural Video Compression with Implicit Temporal Information—0
Standardizing Generative Face Video Compression using Supplemental Enhancement Information—0
Subjective and Objective Quality Assessment of Rendered Human Avatar Videos in Virtual Reality—0
Task-Aware Encoder Control for Deep Video Compression—0
Temporal Context Mining for Learned Video Compression—0
Text-Audio-Visual-conditioned Diffusion Model for Video Saliency Prediction—0
Texture-aware Video Frame Interpolation—0
Texture Segmentation Based Video Compression Using Convolutional Neural Networks—0
The evolution of volumetric video: A survey of smart transcoding and compression approaches—0
The First Comprehensive Dataset with Multiple Distortion Types for Visual Just-Noticeable Differences—0
The Need for Medically Aware Video Compression in Gastroenterology—0
Neural Video Compression using GANs for Detail Synthesis and Propagation—0
Towards Practical Real-Time Neural Video Compression—0
Towards Scalable Neural Representation for Diverse Videos—0
Towards Transparent Application of Machine Learning in Video Processing—0
Toward Super-Resolution for Appearance-Based Gaze Estimation—0
Tracking Single-Cells in Overcrowded Bacterial Colonies—0
TVC: Tokenized Video Compression with Ultra-Low Bitrate—0
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