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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
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
Optimized learned entropy coding parameters for practical neural-based image and video compression—0
Parallelized Rate-Distortion Optimized Quantization Using Deep Learning—0
PEA265: Perceptual Assessment of Video Compression Artifacts—0
Perceptually-inspired super-resolution of compressed videos—0
Perceptual Quality Assessment of HEVC and VVC Standards for 8K Video—0
Perceptual Video Compression with Neural Wrapping—0
Per Clip Lagrangian Multiplier Optimisation for HEVC—0
PIM: Video Coding using Perceptual Importance Maps—0
Pleno-Generation: A Scalable Generative Face Video Compression Framework with Bandwidth Intelligence—0
Plug-and-Play Versatile Compressed Video Enhancement—0
PNVC: Towards Practical INR-based Video Compression—0
Predicting total time to compress a video corpus using online inference systems—0
Prediction and Reference Quality Adaptation for Learned Video Compression—0
PromptMobile: Efficient Promptus for Low Bandwidth Mobile Video Streaming—0
Pruned Lightweight Encoders for Computer Vision—0
PS-NeRV: Patch-wise Stylized Neural Representations for Videos—0
Lossy Event Compression based on Image-derived Quad Trees and Poisson Disk Sampling—0
Quality assessment methods for perceptual video compression—0
Rate-Distortion Optimization With Alternative References For UGC Video Compression—0
Rate-Perception Optimized Preprocessing for Video Coding—0
Real-Time Object Detection and Localization in Compressive Sensed Video on Embedded Hardware—0
Real Time Object Tracking Based on Inter-frame Coding: A Review—0
Reducing The Mismatch Between Marginal and Learned Distributions in Neural Video Compression—0
Region-of-Interest Based Neural Video Compression—0
Residual Learning and Filtering Networks for End-to-End Lossless Video Compression—0
Resolution-Agnostic Neural Compression for High-Fidelity Portrait Video Conferencing via Implicit Radiance Fields—0
Rethinking Implicit Neural Representations for Vision Learners—0
Revisiting the Sample Adaptive Offset post-filter of VVC with Neural-Networks—0
Revisit Visual Representation in Analytics Taxonomy: A Compression Perspective—0
RivuletMLP: An MLP-based Architecture for Efficient Compressed Video Quality Enhancement—0
RL-RC-DoT: A Block-level RL agent for Task-Aware Video Compression—0
Robo-DM: Data Management For Large Robot Datasets—0
Robust Watermarking for Video Forgery Detection with Improved Imperceptibility and Robustness—0
RQAT-INR: Improved Implicit Neural Image Compression—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
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