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Image Compression
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Perception-Oriented Latent Coding for High-Performance Compressed Domain Semantic Inference
Explicit Residual-Based Scalable Image Coding for Humans and Machines
NIC-RobustBench: A Comprehensive Open-Source Toolkit for Neural Image Compression and Robustness Analysis
LVPNet: A Latent-variable-based Prediction-driven End-to-end Framework for Lossless Compression of Medical Images
Fast Training-free Perceptual Image Compression
DiffO: Single-step Diffusion for Image Compression at Ultra-Low Bitrates
ABC: Adaptive BayesNet Structure Learning for Computational Scalable Multi-task Image Compression
Breaking the Multi-Enhancement Bottleneck: Domain-Consistent Quality Enhancement for Compressed Images
Cross-architecture universal feature coding via distribution alignment
Optimizing Learned Image Compression on Scalar and Entropy-Constraint Quantization
Flexible Mixed Precision Quantization for Learned Image Compression
Structured Pruning and Quantization for Learned Image Compression
Fourier-Modulated Implicit Neural Representation for Multispectral Satellite Image Compression
Your Demands Deserve More Bits: Referring Semantic Image Compression at Ultra-low Bitrate
Enhancing Drug Discovery: Autoencoder-Based Latent Space Augmentation for Improved Molecular Solubility Prediction using LatMixSol
Semantics-Guided Generative Image Compression
Synonymous Variational Inference for Perceptual Image Compression
Generative Image Compression by Estimating Gradients of the Rate-variable Feature Distribution
SEMFED: Semantic-Aware Resource-Efficient Federated Learning for Heterogeneous NLP Tasks
Low-Rank Adaptation of Pre-trained Vision Backbones for Energy-Efficient Image Coding for Machine
Accelerating Learned Image Compression Through Modeling Neural Training Dynamics
TokBench: Evaluating Your Visual Tokenizer before Visual Generation
OSCAR: One-Step Diffusion Codec for Image Compression Across Multiple Bit-rates
Generative Latent Coding for Ultra-Low Bitrate Image and Video Compression
One-Step Diffusion-Based Image Compression with Semantic Distillation
Combining Progressive Image Compression and Random Access in DNA Data Storage
Higher fidelity perceptual image and video compression with a latent conditioned residual denoising diffusion model
GANCompress: GAN-Enhanced Neural Image Compression with Binary Spherical Quantization
High Quality Underwater Image Compression with Adaptive Correction and Codebook-based Augmentation
Ultra Lowrate Image Compression with Semantic Residual Coding and Compression-aware Diffusion
PICD: Versatile Perceptual Image Compression with Diffusion Rendering
Towards Facial Image Compression with Consistency Preserving Diffusion Prior
Gone With the Bits: Revealing Racial Bias in Low-Rate Neural Compression for Facial Images
Easz: An Agile Transformer-based Image Compression Framework for Resource-constrained IoTs
LoC-LIC: Low Complexity Learned Image Coding Using Hierarchical Feature Transforms
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.