Papers Image Compression
“Image Compression” 태그가 달린 논문 1,163편 · 필터 해제
T-MLA: A targeted multiscale log-exponential attack framework for neural image compression
Neural image compression (NIC) has become the state-of-the-art for rate-distortion performance, yet its security vulnerabilities remain significantly less understood than those of classifiers. Existing adversarial attack…
Image CompressionDetecting AI-Generated Images via Diffusion Snap-Back Reconstruction: A Forensic Approach
The rapid advancement of generative image models has transformed digital media to the point where AI generated images can no longer be reliably distinguished from authentic photographs by human observers or many conventi…
Image CompressionGenerative Semantic Coding for Ultra-Low Bitrate Visual Communication and Analysis
We consider the problem of ultra-low bit rate visual communication for remote vision analysis, human interactions and control in challenging scenarios with very low communication bandwidth, such as deep space exploration…
Text-to-Image GenerationImage ReconstructionImage CompressionRobot NavigationTowards 1000-fold Electron Microscopy Image Compression for Connectomics via VQ-VAE with Transformer Prior
Petascale electron microscopy (EM) datasets push storage, transfer, and downstream analysis toward their current limits. We present a vector-quantized variational autoencoder-based (VQ-VAE) compression framework for EM t…
Image CompressionDeeply-Conditioned Image Compression via Self-Generated Priors
Learned image compression (LIC) has shown great promise for achieving high rate-distortion performance. However, current LIC methods are often limited in their capability to model the complex correlation structures inher…
Image CompressionSwitchable Token-Specific Codebook Quantization For Face Image Compression
With the ever-increasing volume of visual data, the efficient and lossless transmission, along with its subsequent interpretation and understanding, has become a critical bottleneck in modern information systems. The eme…
Representation LearningImage CompressionFace RecognitionNoise is All You Need: Solving Linear Inverse Problems by Noise Combination Sampling with Diffusion Models
Pretrained diffusion models have demonstrated strong capabilities in zero-shot inverse problem solving by incorporating observation information into the generation process of the diffusion models. However, this presents …
Image CompressionSANR: Scene-Aware Neural Representation for Light Field Image Compression with Rate-Distortion Optimization
Light field images capture multi-view scene information and play a crucial role in 3D scene reconstruction. However, their high-dimensional nature results in enormous data volumes, posing a significant challenge for effi…
Image CompressionTerraCodec: Compressing Optical Earth Observation Data
Earth observation (EO) satellites produce massive streams of multispectral image time series, posing pressing challenges for storage and transmission. Yet, learned EO compression remains fragmented and lacks publicly ava…
Image CompressionViCO: A Training Strategy towards Semantic Aware Dynamic High-Resolution
Existing Multimodal Large Language Models (MLLMs) suffer from increased inference costs due to the additional vision tokens introduced by image inputs. In this work, we propose Visual Consistency Learning (ViCO), a novel…
Image CompressionChannel Simulation and Distributed Compression with Ensemble Rejection Sampling
We study channel simulation and distributed matching, two fundamental problems with several applications to machine learning, using a recently introduced generalization of the standard rejection sampling (RS) algorithm k…
Image CompressionUltra-Efficient Decoding for End-to-End Neural Compression and Reconstruction
Image compression and reconstruction are crucial for various digital applications. While contemporary neural compression methods achieve impressive compression rates, the adoption of such technology has been largely hind…
Image CompressionDiVeQ: Differentiable Vector Quantization Using the Reparameterization Trick
Vector quantization is common in deep models, yet its hard assignments block gradients and hinder end-to-end training. We propose DiVeQ, which treats quantization as adding an error vector that mimics the quantization di…
Image CompressionImage GenerationVariable Rate Image Compression via N-Gram Context based Swin-transformer
This paper presents an N-gram context-based Swin Transformer for learned image compression. Our method achieves variable-rate compression with a single model. By incorporating N-gram context into the Swin Transformer, we…
Image ReconstructionImage CompressionRaw-JPEG Adapter: Efficient Raw Image Compression with JPEG
Digital cameras digitize scene light into linear raw representations, which the image signal processor (ISP) converts into display-ready outputs. While raw data preserves full sensor information--valuable for editing and…
Image CompressionOptimized Learned Image Compression for Facial Expression Recognition
Efficient data compression is crucial for the storage and transmission of visual data. However, in facial expression recognition (FER) tasks, lossy compression often leads to feature degradation and reduced accuracy. To …
Facial Expression RecognitionImage CompressionEvent-Based Visual Teach-and-Repeat via Fast Fourier-Domain Cross-Correlation
Visual teach-and-repeat (VT&R) navigation enables robots to autonomously traverse previously demonstrated paths using visual feedback. We present a novel event-camera-based VT\&R system. Our system formulates event-strea…
Image CompressionEfficient Learned Image Compression Through Knowledge Distillation
Learned image compression sits at the intersection of machine learning and image processing. With advances in deep learning, neural network-based compression methods have emerged. In this process, an encoder maps the ima…
Knowledge DistillationImage CompressionSEEC: Segmentation-Assisted Multi-Entropy Models for Learned Lossless Image Compression
Recently, learned image compression has attracted considerable attention due to its superior performance over traditional methods. However, most existing approaches employ a single entropy model to estimate the probabili…
Semantic SegmentationImage CompressionCompression Beyond Pixels: Semantic Compression with Multimodal Foundation Models
Recent deep learning-based methods for lossy image compression achieve competitive rate-distortion performance through extensive end-to-end training and advanced architectures. However, emerging applications increasingly…
Image Compression