Papers Image Compression
“Image Compression” 태그가 달린 논문 1,163편 · 필터 해제
Histogram Driven Amplitude Embedding for Qubit Efficient Quantum Image Compression
This work introduces a compact and hardware efficient method for compressing color images using near term quantum devices. The approach segments the image into fixed size blocks called bixels, and computes the total inte…
Image CompressionComp-X: On Defining an Interactive Learned Image Compression Paradigm With Expert-driven LLM Agent
We present Comp-X, the first intelligently interactive image compression paradigm empowered by the impressive reasoning capability of large language model (LLM) agent. Notably, commonly used image codecs usually suffer f…
Image CompressionScaling Learned Image Compression Models up to 1 Billion
Recent advances in large language models (LLMs) highlight a strong connection between intelligence and compression. Learned image compression, a fundamental task in modern data compression, has made significant progress …
Image CompressionSODiff: Semantic-Oriented Diffusion Model for JPEG Compression Artifacts Removal
JPEG, as a widely used image compression standard, often introduces severe visual artifacts when achieving high compression ratios. Although existing deep learning-based restoration methods have made considerable progres…
Image CompressionFourier Compressor: Frequency-Domain Visual Token Compression for Vision-Language Models
Vision-Language Models (VLMs) incur substantial computational overhead and inference latency due to the large number of vision tokens introduced by high-resolution image and video inputs. Existing parameter-free token co…
Image CompressionSteering One-Step Diffusion Model with Fidelity-Rich Decoder for Fast Image Compression
Diffusion-based image compression has demonstrated impressive perceptual performance. However, it suffers from two critical drawbacks: (1) excessive decoding latency due to multi-step sampling, and (2) poor fidelity resu…
Image CompressionHCF: Hierarchical Cascade Framework for Distributed Multi-Stage Image Compression
Distributed multi-stage image compression -- where visual content traverses multiple processing nodes under varying quality requirements -- poses challenges. Progressive methods enable bitstream truncation but underutili…
Computational EfficiencyImage CompressionContent-Aware Mamba for Learned Image Compression
Recent learned image compression (LIC) leverages Mamba-style state-space models (SSMs) for global receptive fields with linear complexity. However, the standard Mamba adopts content-agnostic, predefined raster (or multi-…
Computational EfficiencyImage CompressionJoint Lossless Compression and Steganography for Medical Images via Large Language Models
Recently, large language models (LLMs) have driven promising progress in lossless image compression. However, directly adopting existing paradigms for medical images suffers from an unsatisfactory trade-off between compr…
Image CompressionAdjustable Spatio-Spectral Hyperspectral Image Compression Network
With the rapid growth of hyperspectral data archives in remote sensing (RS), the need for efficient storage has become essential, driving significant attention toward learning-based hyperspectral image (HSI) compression.…
Image CompressionJPEG Processing Neural Operator for Backward-Compatible Coding
Despite significant advances in learning-based lossy compression algorithms, standardizing codecs remains a critical challenge. In this paper, we present the JPEG Processing Neural Operator (JPNeO), a next-generation JPE…
Image CompressionLearned Image Compression with Hierarchical Progressive Context Modeling
Context modeling is essential in learned image compression for accurately estimating the distribution of latents. While recent advanced methods have expanded context modeling capacity, they still struggle to efficiently …
Image CompressionLongSplat: Online Generalizable 3D Gaussian Splatting from Long Sequence Images
3D Gaussian Splatting achieves high-fidelity novel view synthesis, but its application to online long-sequence scenarios is still limited. Existing methods either rely on slow per-scene optimization or fail to provide ef…
Novel View SynthesisImage CompressionCompress-Align-Detect: onboard change detection from unregistered images
Change detection from satellite images typically incurs a delay ranging from several hours up to days because of latency in downlinking the acquired images and generating orthorectified image products at the ground stati…
Image RegistrationImage CompressionChange DetectionPerception-Oriented Latent Coding for High-Performance Compressed Domain Semantic Inference
In recent years, compressed domain semantic inference has primarily relied on learned image coding models optimized for mean squared error (MSE). However, MSE-oriented optimization tends to yield latent spaces with limit…
Image ClassificationImage CompressionSemantic SegmentationCustomizable ROI-Based Deep Image Compression
Region of Interest (ROI)-based image compression optimizes bit allocation by prioritizing ROI for higher-quality reconstruction. However, as the users (including human clients and downstream machine tasks) become more di…
Image CompressionExplicit Residual-Based Scalable Image Coding for Humans and Machines
Scalable image compression is a technique that progressively reconstructs multiple versions of an image for different requirements. In recent years, images have increasingly been consumed not only by humans but also by i…
Image CompressionNIC-RobustBench: A Comprehensive Open-Source Toolkit for Neural Image Compression and Robustness Analysis
Adversarial robustness of neural networks is an increasingly important area of research, combining studies on computer vision models, large language models (LLMs), and others. With the release of JPEG AI -- the first sta…
Adversarial RobustnessImage CompressionLVPNet: A Latent-variable-based Prediction-driven End-to-end Framework for Lossless Compression of Medical Images
Autoregressive Initial Bits is a framework that integrates sub-image autoregression and latent variable modeling, demonstrating its advantages in lossless medical image compression. However, in existing methods, the imag…
Image CompressionImage SegmentationQuantizationSemantic SegmentationDiffO: Single-step Diffusion for Image Compression at Ultra-Low Bitrates
Although image compression is fundamental to visual data processing and has inspired numerous standard and learned codecs, these methods still suffer severe quality degradation at extremely low bits per pixel. While rece…
DenoisingImage Compression