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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 451–475 of 496 papers

TitleStatusHype
Efficient Feature Extraction, Encoding and Classification for Action Recognition—0
Efficient Learned Wavelet Image and Video Coding—0
Efficient video indexing for monitoring disease activity and progression in the upper gastrointestinal tract—0
Effortless Cross-Platform Video Codec: A Codebook-Based Method—0
ELF-VC: Efficient Learned Flexible-Rate Video Coding—0
Emerging Advances in Learned Video Compression: Models, Systems and Beyond—0
Encoder-Quantization-Motion-based Video Quality Metrics—0
End-to-End Learned Block-Based Image Compression with Block-Level Masked Convolutions and Asymptotic Closed Loop Training—0
End-to-End Learning of Video Compression Using Spatio-Temporal Autoencoders—0
End-to-end Optimized Video Compression with MV-Residual Prediction—0
End-to-End Rate-Distortion Optimization for Bi-Directional Learned Video Compression—0
Enhancement or Super-Resolution: Learning-based Adaptive Video Streaming with Client-Side Video Processing—0
Enhancing 3D Gaussian Splatting Compression via Spatial Condition-based Prediction—0
Entropy Coding Improvement for Low-complexity Compressive Auto-encoders—0
Estimating the HEVC Decoding Energy Using the Decoder Processing Time—0
EVA^2: Exploiting Temporal Redundancy in Live Computer Vision—0
Evaluating Foveated Video Quality Using Entropic Differencing—0
Evaluating the Performance of Existing Full-Reference Quality Metrics on High Dynamic Range (HDR) Video Content—0
Expanding Synthetic Real-World Degradations for Blind Video Super Resolution—0
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions—0
Exploiting Change Blindness for Video Coding: Perspectives from a Less Promising User Study—0
Exploring the Distributed Video Coding in a Quality Assessment Context—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
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