https://github.com/AdaptiveVC/SRVC
공식 코드Efficient Video Compression via Content-Adaptive Super-Resolution
Papers with Code 아카이브
Video Compression
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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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/CandleLabAI/MotionAura-ICLR-2025
공식 코드MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete Diffusion
https://github.com/CompressTeam/TransformCodingInference
공식 코드Feature Map Transform Coding for Energy-Efficient CNN Inference
https://github.com/DeepMC-DCVC/DCVC
공식 코드https://github.com/FireFYF/modulatedautoencoder
공식 코드Variable Rate Deep Image Compression with Modulated Autoencoder
https://github.com/GuoLusjtu/DVC
공식 코드https://github.com/HoangTrinh/B_DRRN
공식 코드https://github.com/InterDigitalInc/CompressAI
공식 코드CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
https://github.com/JiangkaiWu/Promptus
공식 코드Promptus: Can Prompts Streaming Replace Video Streaming with Stable Diffusion
https://github.com/JianpingLin/M-LVC_CVPR2020
공식 코드M-LVC: Multiple Frames Prediction for Learned Video Compression
https://github.com/KUIS-AI-Tekalp-Research-Group/frame-prediction
공식 코드https://github.com/KUIS-AI-Tekalp-Research-Group/video-compression
공식 코드End-to-End Rate-Distortion Optimized Learned Hierarchical Bi-Directional Video Compression
https://github.com/KUIS-AI-Tekalp-Research-Group/video-compression/tree/master/Flex-Rate-Hier-Bidir-Video-Compression
공식 코드https://github.com/Meixu-Chen/MOVI-Codec
공식 코드https://github.com/NJUVISION/DHVC
공식 코드High-Efficiency Neural Video Compression via Hierarchical Predictive Learning
https://github.com/NVlabs/conv-tt-lstm
공식 코드Convolutional Tensor-Train LSTM for Spatio-temporal Learning
https://github.com/NYCU-MAPL/B-CANF
공식 코드B-CANF: Adaptive B-frame Coding with Conditional Augmented Normalizing Flows
https://github.com/RenYang-home/HLVC
공식 코드Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement
https://github.com/RenYang-home/OpenDVC
공식 코드OpenDVC: An Open Source Implementation of the DVC Video Compression Method
https://github.com/RenYang-home/RLVC
공식 코드Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model
https://github.com/Simplar/Effect-of-temporal-window-on-HR-estimation
공식 코드https://github.com/Sindhu-Hegde/video-super-resolver
공식 코드Extreme-scale Talking-Face Video Upsampling with Audio-Visual Priors
https://github.com/ZitongYu/STVEN_rPPGNet
공식 코드https://github.com/aaronbtb/mnerv
공식 코드https://github.com/aaronkandel/wasserstein-nonlinear-mpc
공식 코드https://github.com/abhyantrika/nirvana
공식 코드https://github.com/adnortje/deepvideo
공식 코드Deep motion estimation for parallel inter-frame prediction in video compression
https://github.com/ali-vilab/cdt
공식 코드Rethinking Video Tokenization: A Conditioned Diffusion-based Approach
https://github.com/alibaba/Tora
공식 코드Tora: Trajectory-oriented Diffusion Transformer for Video Generation
https://github.com/bbc/cnn-fractional-motion-compensation
공식 코드Interpreting CNN for Low Complexity Learned Sub-pixel Motion Compensation in Video Coding
https://github.com/berlin0610/pfvc
공식 코드Beyond GFVC: A Progressive Face Video Compression Framework with Adaptive Visual Tokens
https://github.com/bowenl0218/mmvc_video_codec
공식 코드https://github.com/buggyyang/hier-video-compression
공식 코드Hierarchical Autoregressive Modeling for Neural Video Compression
https://github.com/buggyyang/rvd
공식 코드https://github.com/buggyyang/youtube-nt
공식 코드Hierarchical Autoregressive Modeling for Neural Video Compression
https://github.com/chaoliu18/rplvc
공식 코드Learned Video Compression with Residual Prediction and Loop Filter
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.