https://github.com/xinyiw915/relax-vqa
공식 코드ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment
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/xumengwei/DeepCache
공식 코드https://github.com/xuyang-liu16/vidcom2
공식 코드Video Compression Commander: Plug-and-Play Inference Acceleration for Video Large Language Models
https://github.com/yixuan423/compressed-face-videos-quality-assessment
공식 코드Perceptual Quality Assessment of Face Video Compression: A Benchmark and An Effective Method
https://github.com/ywwynm/smr
공식 코드Perceptual Video Coding for Machines via Satisfied Machine Ratio Modeling
https://github.com/zc-lynen/mv-ierv
공식 코드Implicit-explicit Integrated Representations for Multi-view Video Compression
https://github.com/zhaoyue-zephyrus/bsq-vit
공식 코드Image and Video Tokenization with Binary Spherical Quantization
https://github.com/zhihaohu/pytorchvideocompression
공식 코드FVC: A New Framework towards Deep Video Compression in Feature Space
https://github.com/CODARcode/MGARD
Scalable Hybrid Learning Techniques for Scientific Data Compression
https://github.com/HoangTrinh/ROI_Online_Meeting_Codec
RCLC: ROI-based joint conventional and learning video compression
https://github.com/Liusifei/UVC
https://github.com/MindSpore-scientific/code-11/tree/main/mgan-mindspore
MGANet: A Robust Model for Quality Enhancement of Compressed Video
https://github.com/MindSpore-scientific/code-8/tree/main/mgan-mindspore
MGANet: A Robust Model for Quality Enhancement of Compressed Video
https://github.com/SaipingZhang/DVC_P
https://github.com/YingzhenLi/Sprites
https://github.com/anant95/K_means-image-compression
Semantic Perceptual Image Compression using Deep Convolution Networks
https://github.com/binzzheng/DVC-PyTorch
https://github.com/bornfly-detachment/asymmetric_magvitv2
Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation
https://github.com/chaoyuaw/pytorch-coviar
https://github.com/chaoyuaw/pytorch-vcii
https://github.com/fan-aaron-zhang/cvegan
CVEGAN: A Perceptually-inspired GAN for Compressed Video Enhancement
https://github.com/final-0/icm-v1
Image Coding for Machines with Edge Information Learning Using Segment Anything
https://github.com/goluck-konuko/animation-based-codecs
Ultra-low bitrate video conferencing using deep image animation
https://github.com/hmkx/hinerv
HiNeRV: Video Compression with Hierarchical Encoding-based Neural Representation
https://github.com/ihaeyong/pfnr
https://github.com/ihaeyong/pnr
https://github.com/jerrywn121/TianChi_AIEarth
Convolutional Tensor-Train LSTM for Spatio-temporal Learning
https://github.com/jy0205/Pyramid-Flow
Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation
https://github.com/lucidrains/magvit2-pytorch
Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation
https://github.com/lucidrains/vector-quantize-pytorch
Image and Video Tokenization with Binary Spherical Quantization
https://github.com/makinyilmaz/lhbdc
End-to-End Rate-Distortion Optimized Learned Hierarchical Bi-Directional Video Compression
https://github.com/mazzzystar/Disentangled-Sequential-Autoencoder
https://github.com/mengab/Versatile-Video-Coding
https://github.com/microsoft/activemlp
https://github.com/mullisd1/CV_Heartrate
https://github.com/orange-opensource/cool-chic
C3: High-performance and low-complexity neural compression from a single image or video
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.