https://github.com/05kashyap/AutoEncoded_Image_Transfer
공식 코드Autoencoded Image Compression for Secure and Fast Transmission
Papers with Code 아카이브
Image Compression
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 11
- 보존된 평가 기록
- 22
Image Compression is an application of data compression for digital images to lower their storage and/or transmission requirements.
Source: Variable Rate Deep Image Compression With a Conditional Autoencoder
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/BRN-Hub/anon-compression
공식 코드Region of Interest Loss for Anonymizing Learned Image Compression
https://github.com/CaiShilv/HiFi-VRIC
공식 코드High-Fidelity Variable-Rate Image Compression via Invertible Activation Transformation
https://github.com/CangxiongChen/understanding_CLIP_vulnerability
공식 코드Understanding the Vulnerability of CLIP to Image Compression
https://github.com/ChajinShin/EASN
공식 코드Expanded Adaptive Scaling Normalization for End to End Image Compression
https://github.com/CrashedBboy/SGGIC
공식 코드https://github.com/DS3Lab/odlc
공식 코드https://github.com/EIDOSLAB/Efficient-PIC-with-Variance-Aware-Masking
공식 코드Efficient Progressive Image Compression with Variance-aware Masking
https://github.com/EIDOSLAB/GABIC
공식 코드https://github.com/EmilienDupont/coin
공식 코드https://github.com/FireFYF/SlimCAE
공식 코드Slimmable Compressive Autoencoders for Practical Neural Image Compression
https://github.com/FireFYF/modulatedautoencoder
공식 코드Variable Rate Deep Image Compression with Modulated Autoencoder
https://github.com/GityuxiLiu/Region-Adaptive-Transform-with-Segmentation-Prior-for-Image-Compression
공식 코드Region-Adaptive Transform with Segmentation Prior for Image Compression
https://github.com/Hao840/MAHPPO
공식 코드https://github.com/HoangTrinh/EDMS
공식 코드Image Compression with Encoder-Decoder Matched Semantic Segmentation
https://github.com/InterDigitalInc/CompressAI
공식 코드CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
https://github.com/JasonZHM/CAE-ADMM
공식 코드CAE-ADMM: Implicit Bitrate Optimization via ADMM-based Pruning in Compressive Autoencoders
https://github.com/JasonZHM/CAEP
공식 코드CAE-ADMM: Implicit Bitrate Optimization via ADMM-based Pruning in Compressive Autoencoders
https://github.com/JooyoungLeeETRI/CA_Entropy_Model
공식 코드Context-adaptive Entropy Model for End-to-end Optimized Image Compression
https://github.com/Jozdien/Metise
공식 코드https://github.com/Jyonn/VQ4Rec
공식 코드Vector Quantization for Recommender Systems: A Review and Outlook
https://github.com/KUIS-AI-Tekalp-Research-Group/video-compression/tree/master/Flex-Rate-Hier-Bidir-Video-Compression
공식 코드https://github.com/Koukyosyumei/secure_ml
공식 코드Video in 10 Bits: Few-Bit VideoQA for Efficiency and Privacy
https://github.com/LabShuHangGU/CCA
공식 코드Causal Context Adjustment Loss for Learned Image Compression
https://github.com/LinHanhe/SUR-FeatNet
공식 코드https://github.com/M0574F4/LIC_TX
공식 코드https://github.com/NJUVISION/MPA
공식 코드All-in-One Image Coding for Joint Human-Machine Vision with Multi-Path Aggregation
https://github.com/NJUVISION/POLC
공식 코드Perception-Oriented Latent Coding for High-Performance Compressed Domain Semantic Inference
https://github.com/NJUVISION/rho-vision
공식 코드https://github.com/NYCU-MAPL/TransTIC
공식 코드https://github.com/NYCU-MAPL/Transformer_VariableROI
공식 코드Transformer-based Variable-rate Image Compression with Region-of-interest Control
https://github.com/Nikolai10/PerCo
공식 코드https://github.com/PaddlePaddle/Quantum
공식 코드https://github.com/QingXin96/Intrinsic_image_transfer
공식 코드https://github.com/RenYang-home/HLVC
공식 코드Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement
https://github.com/SIJIEJI/CLNet
공식 코드CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.