Papers Image Compression
“Image Compression” 태그가 달린 논문 1,163편 · 필터 해제
What and Where to Adapt: Structure-Semantics Co-Tuning for Machine Vision Compression via Synergistic Adapters
Parameter-efficient fine-tuning of pre-trained codecs is a promising direction in image compression for human and machine vision. While most existing works have primarily focused on tuning the feature structure within th…
parameter-efficient fine-tuningImage CompressionA Noise Constrained Diffusion (NC-Diffusion) Framework for High Fidelity Image Compression
With the great success of diffusion models in image generation, diffusion-based image compression is attracting increasing interests. However, due to the random noise introduced in the diffusion learning, they usually pr…
Image CompressionImage GenerationCompression as an Adversarial Amplifier Through Decision Space Reduction
Image compression is a ubiquitous component of modern visual pipelines, routinely applied by social media platforms and resource-constrained systems prior to inference. Despite its prevalence, the impact of compression o…
Adversarial RobustnessImage CompressionCI-ICM: Channel Importance-driven Learned Image Coding for Machines
Traditional human vision-centric image compression methods are suboptimal for machine vision centric compression due to different visual properties and feature characteristics. To address this problem, we propose a Chann…
Instance SegmentationImage CompressionObject DetectionOn the Robustness of Diffusion-Based Image Compression to Bit-Flip Errors
Modern image compression methods are typically optimized for the rate--distortion--perception trade-off, whereas their robustness to bit-level corruption is rarely examined. We show that diffusion-based compressors built…
Image CompressionNeuralLVC: Neural Lossless Video Compression via Masked Diffusion with Temporal Conditioning
While neural lossless image compression has advanced significantly with learned entropy models, lossless video compression remains largely unexplored in the neural setting. We present NeuralLVC, a neural lossless video c…
Image CompressionSeeing the Evidence, Missing the Answer: Tool-Guided Vision-Language Models on Visual Illusions
Vision-language models (VLMs) exhibit a systematic bias when confronted with classic optical illusions: they overwhelmingly predict the illusion as "real" regardless of whether the image has been counterfactually modifie…
Image ManipulationSpatial ReasoningImage CompressionEnd-to-End Image Compression with Segmentation Guided Dual Coding for Wind Turbines
Transferring large volumes of high-resolution images during wind turbine inspections introduces a bottleneck in assessing and detecting severe defects. Efficient coding must preserve high fidelity in blade regions while …
Image CompressionPrompt-Guided Prefiltering for VLM Image Compression
The rapid progress of large Vision-Language Models (VLMs) has enabled a wide range of applications, such as image understanding and Visual Question Answering (VQA). Query images are often uploaded to the cloud, where VLM…
Visual Question AnsweringImage CompressionRAWIC: Bit-Depth Adaptive Lossless Raw Image Compression
Raw images preserve linear sensor measurements and high bit-depth information crucial for advanced vision tasks and photography applications, yet their storage remains challenging due to large file sizes, varying bit dep…
Image CompressionMotiMem: Motion-Aware Approximate Memory for Energy-Efficient Neural Perception in Autonomous Vehicles
High-resolution sensors are critical for robust autonomous perception but impose a severe memory wall on battery-constrained electric vehicles. In these systems, data movement energy often outweighs computation. Traditio…
Autonomous VehiclesImage CompressionObject DetectionHyVIC: A Metric-Driven Spatio-Spectral Hyperspectral Image Compression Architecture Based on Variational Autoencoders
The rapid growth of hyperspectral data archives in remote sensing (RS) necessitates effective compression methods for storage and transmission. Recent advances in learning-based hyperspectral image (HSI) compression have…
Spectral ReconstructionImage CompressionAdaptive Learned Image Compression with Graph Neural Networks
Efficient image compression relies on modeling both local and global redundancy. Most state-of-the-art (SOTA) learned image compression (LIC) methods are based on CNNs or Transformers, which are inherently rigid. Standar…
Image CompressionViewport-based Neural 360° Image Compression
Given the popularity of 360° images on social media platforms, 360° image compression becomes a critical technology for media storage and transmission. Conventional 360° image compression pipeline projects the spherical …
Image CompressionMultimodal Model for Computational Pathology:Representation Learning and Image Compression
Whole slide imaging (WSI) has transformed digital pathology by enabling computational analysis of gigapixel histopathology images. Recent foundation model advances have accelerated progress in computational pathology, fa…
Representation LearningImage CompressionFew-Shot LearningJoint Degradation-Aware Arbitrary-Scale Super-Resolution for Variable-Rate Extreme Image Compression
Recent diffusion-based extreme image compression methods have demonstrated remarkable performance at ultra-low bitrates. However, most approaches require training separate diffusion models for each target bitrate, result…
Image CompressionNext-Frame Decoding for Ultra-Low-Bitrate Image Compression with Video Diffusion Priors
We present a novel paradigm for ultra-low-bitrate image compression (ULB-IC) that exploits the ``temporal'' evolution in generative image compression. Specifically, we define an explicit intermediate state during decodin…
Image CompressionA PPO-Based Bitrate Allocation Conditional Diffusion Model for Remote Sensing Image Compression
Existing remote sensing image compression methods still explore to balance high compression efficiency with the preservation of fine details and task-relevant information. Meanwhile, high-resolution drone imagery offers …
Image CompressionObject DetectionD-Compress: Detail-Preserving LiDAR Range Image Compression for Real-Time Streaming on Resource-Constrained Robots
Efficient 3D LiDAR point cloud compression (LPCC) and streaming are critical for edge server-assisted robotic systems, enabling real-time communication with compact data representations. A widely adopted approach represe…
Image CompressionObject DetectionPoint CloudsInevitable Encounters: Backdoor Attacks Involving Lossy Compression
Real-world backdoor attacks often require poisoned datasets to be stored and transmitted before being used to compromise deep learning systems. However, in the era of big data, the inevitable use of lossy compression pos…
Image Compression