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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/maftej/rfdvc
공식 코드Radiance Field Delta Video Compression in Edge-Enabled Vehicular Metaverse
https://github.com/magicffourier/vq-nerv
공식 코드VQ-NeRV: A Vector Quantized Neural Representation for Videos
https://github.com/maincold2/CAM
공식 코드https://github.com/maincold2/FFNeRV
공식 코드FFNeRV: Flow-Guided Frame-Wise Neural Representations for Videos
https://github.com/makinyilmaz/DFPN
공식 코드https://github.com/megvii-research/fst-matching
공식 코드https://github.com/mengab/MGANet
공식 코드MGANet: A Robust Model for Quality Enhancement of Compressed Video
https://github.com/mengab/MGANet-DCC2020
공식 코드MGANet: A Robust Model for Quality Enhancement of Compressed Video
https://github.com/mengab/SDTS
공식 코드https://github.com/microsoft/dcvc
공식 코드Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression
https://github.com/microsoft/vcd
공식 코드https://github.com/msu-video-group/ECCVW24_Saliency_Prediction
공식 코드AIM 2024 Challenge on Video Saliency Prediction: Methods and Results
https://github.com/msu-video-group/msu_vqm_compression_benchmark
공식 코드Video compression dataset and benchmark of learning-based video-quality metrics
https://github.com/nycu-mapl/canf-vc
공식 코드CANF-VC: Conditional Augmented Normalizing Flows for Video Compression
https://github.com/ohsngjun/PEVC
공식 코드Parameter-Efficient Instance-Adaptive Neural Video Compression
https://github.com/privateyoung/Youtube-NT
공식 코드Hierarchical Autoregressive Modeling for Neural Video Compression
https://github.com/renyang-home/alvc
공식 코드Advancing Learned Video Compression with In-loop Frame Prediction
https://github.com/renyang-home/plvc
공식 코드Perceptual Learned Video Compression with Recurrent Conditional GAN
https://github.com/roberto1648/adversarial-perturbations-on-images-and-videos
공식 코드Targeted Nonlinear Adversarial Perturbations in Images and Videos
https://github.com/ruhig6/ibvc
공식 코드https://github.com/sch971111/mof
공식 코드Efficient Cross-Modal Video Retrieval with Meta-Optimized Frames
https://github.com/sczwangxiao/video-retake
공식 코드ReTaKe: Reducing Temporal and Knowledge Redundancy for Long Video Understanding
https://github.com/sdkinda/hdr-learned-video-coding
공식 코드https://github.com/subin-kim-cv/NVP
공식 코드Scalable Neural Video Representations with Learnable Positional Features
https://github.com/tianyuan168326/SelfC
공식 코드https://github.com/tianyuan168326/vcs-pytorch
공식 코드A Coding Framework and Benchmark towards Low-Bitrate Video Understanding
https://github.com/tianyuan168326/videosemanticcompression-pytorch
공식 코드SMC++: Masked Learning of Unsupervised Video Semantic Compression
https://github.com/tinyvision/IPCodec
공식 코드Spatiotemporal Entropy Model is All You Need for Learned Video Compression
https://github.com/tongdaxu/bit-allocation-using-optimization
공식 코드https://github.com/tpulkit/txt2vid
공식 코드Txt2Vid: Ultra-Low Bitrate Compression of Talking-Head Videos via Text
https://github.com/ustc-imcl/envc
공식 코드Learning Cross-Scale Weighted Prediction for Efficient Neural Video Compression
https://github.com/villawang/team-net
공식 코드TEAM-Net: Multi-modal Learning for Video Action Recognition with Partial Decoding
https://github.com/wangty1/DCPT
공식 코드Deep Convolutional Pooling Transformer for Deepfake Detection
https://github.com/xinjie-q/distributed-dvc
공식 코드Low-complexity Deep Video Compression with A Distributed Coding Architecture
https://github.com/xinyiw915/quality-energy
공식 코드Comparative Study of Hardware and Software Power Measurements in Video Compression
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.