Papers Image Compression
“Image Compression” 태그가 달린 논문 1,163편 · 필터 해제
Multi-Scale Invertible Neural Network for Wide-Range Variable-Rate Learned Image Compression
Autoencoder-based structures have dominated recent learned image compression methods. However, the inherent information loss associated with autoencoders limits their rate-distortion performance at high bit rates and res…
Image CompressionF-INR: Functional Tensor Decomposition for Implicit Neural Representations
Implicit Neural Representation (INR) has emerged as a powerful tool for encoding discrete signals into continuous, differentiable functions using neural networks. However, these models often have an unfortunate reliance …
3D geometryImage CompressionTensor DecompositionBitstream Collisions in Neural Image Compression via Adversarial Perturbations
Neural image compression (NIC) has emerged as a promising alternative to classical compression techniques, offering improved compression ratios. Despite its progress towards standardization and practical deployment, ther…
Adversarial AttackImage CompressionMultiscale Feature Importance-based Bit Allocation for End-to-End Feature Coding for Machines
Feature Coding for Machines (FCM) aims to compress intermediate features effectively for remote intelligent analytics, which is crucial for future intelligent visual applications. In this paper, we propose a Multiscale F…
Feature ImportanceImage CompressionInstance SegmentationKeypoint Detection+1TFIC: End-to-End Text-Focused Image Compression for Coding for Machines
Traditional image compression methods aim to faithfully reconstruct images for human perception. In contrast, Coding for Machines focuses on compressing images to preserve information relevant to a specific machine task.…
Image CompressionOptical Character RecognitionOptical Character Recognition (OCR)Guided Diffusion for the Extension of Machine Vision to Human Visual Perception
Image compression technology eliminates redundant information to enable efficient transmission and storage of images, serving both machine vision and human visual perception. For years, image coding focused on human perc…
Image CompressionOverview of Variable Rate Coding in JPEG AI
Empirical evidence has demonstrated that learning-based image compression can outperform classical compression frameworks. This has led to the ongoing standardization of learned-based image codecs, namely Joint Photograp…
Image CompressionOSLO-IC: On-the-Sphere Learned Omnidirectional Image Compression with Attention Modules and Spatial Context
Developing effective 360-degree (spherical) image compression techniques is crucial for technologies like virtual reality and automated driving. This paper advances the state-of-the-art in on-the-sphere learning (OSLO) f…
Image CompressionTask-Oriented Feature Compression for Multimodal Understanding via Device-Edge Co-Inference
With the rapid development of large multimodal models (LMMs), multimodal understanding applications are emerging. As most LMM inference requests originate from edge devices with limited computational capabilities, the pr…
Feature CompressionImage CompressionQuestion AnsweringVisual Question AnsweringMambaIC: State Space Models for High-Performance Learned Image Compression
A high-performance image compression algorithm is crucial for real-time information transmission across numerous fields. Despite rapid progress in image compression, computational inefficiency and poor redundancy modelin…
Image CompressionState Space ModelsPathology Image Compression with Pre-trained Autoencoders
The growing volume of high-resolution Whole Slide Images in digital histopathology poses significant storage, transmission, and computational efficiency challenges. Standard compression methods, such as JPEG, reduce file…
Computational EfficiencyImage CompressionMultiple Instance LearningQuantization+1Enhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning
Accurate diagnosis of disease often depends on the exhaustive examination of Whole Slide Images (WSI) at microscopic resolution. Efficient handling of these data-intensive images requires lossy compression techniques. Th…
DiagnosticImage CompressionMS-SSIMSSIM+1Leveraging Diffusion Knowledge for Generative Image Compression with Fractal Frequency-Aware Band Learning
By optimizing the rate-distortion-realism trade-off, generative image compression approaches produce detailed, realistic images instead of the only sharp-looking reconstructions produced by rate-distortion-optimized mode…
Image CompressionImage ReconstructionNavigateDeep Lossless Image Compression via Masked Sampling and Coarse-to-Fine Auto-Regression
Learning-based lossless image compression employs pixel-based or subimage-based auto-regression for probability estimation, which achieves desirable performances. However, the existing works only consider context depende…
Image CompressionregressionFlow to the Mode: Mode-Seeking Diffusion Autoencoders for State-of-the-Art Image Tokenization
Since the advent of popular visual generation frameworks like VQGAN and latent diffusion models, state-of-the-art image generation systems have generally been two-stage systems that first tokenize or compress visual data…
Image CompressionImage GenerationGain-MLP: Improving HDR Gain Map Encoding via a Lightweight MLP
While most images shared on the web and social media platforms are encoded in standard dynamic range (SDR), many displays now can accommodate high dynamic range (HDR) content. Additionally, modern cameras can capture ima…
HDR ReconstructionImage CompressionJPEG Compliant Compression for Both Human and Machine, A Report
Deep Neural Networks (DNNs) have become an integral part of our daily lives, especially in vision-related applications. However, the conventional lossy image compression algorithms are primarily designed for the Human Vi…
Image CompressionPerCoV2: Improved Ultra-Low Bit-Rate Perceptual Image Compression with Implicit Hierarchical Masked Image Modeling
We introduce PerCoV2, a novel and open ultra-low bit-rate perceptual image compression system designed for bandwidth- and storage-constrained applications. Building upon prior work by Careil et al., PerCoV2 extends the o…
Image CompressionSeeing Delta Parameters as JPEG Images: Data-Free Delta Compression with Discrete Cosine Transform
With transformer-based models and the pretrain-finetune paradigm becoming mainstream, the high storage and deployment costs of individual finetuned models on multiple tasks pose critical challenges. Delta compression att…
Image CompressionQuantizationFEDS: Feature and Entropy-Based Distillation Strategy for Efficient Learned Image Compression
Learned image compression (LIC) methods have recently outperformed traditional codecs such as VVC in rate-distortion performance. However, their large models and high computational costs have limited their practical adop…
Image Compression