Papers Image Compression
“Image Compression” 태그가 달린 논문 1,163편 · 필터 해제
CALLIC: Content Adaptive Learning for Lossless Image Compression
Learned lossless image compression has achieved significant advancements in recent years. However, existing methods often rely on training amortized generative models on massive datasets, resulting in sub-optimal probabi…
Image CompressionTransfer LearningAsymLLIC: Asymmetric Lightweight Learned Image Compression
Learned image compression (LIC) methods often employ symmetrical encoder and decoder architectures, evitably increasing decoding time. However, practical scenarios demand an asymmetric design, where the decoder requires …
DecoderImage CompressionSparse Point Clouds Assisted Learned Image Compression
In the field of autonomous driving, a variety of sensor data types exist, each representing different modalities of the same scene. Therefore, it is feasible to utilize data from other sensors to facilitate image compres…
Autonomous DrivingImage CompressionQuantum Implicit Neural Compression
Signal compression based on implicit neural representation (INR) is an emerging technique to represent multimedia signals with a small number of bits. While INR-based signal compression achieves high-quality reconstructi…
Data CompressionImage CompressionStable Diffusion is a Natural Cross-Modal Decoder for Layered AI-generated Image Compression
Recent advances in Artificial Intelligence Generated Content (AIGC) have garnered significant interest, accompanied by an increasing need to transmit and compress the vast number of AI-generated images (AIGIs). However, …
DecoderImage CompressionImage GenerationPoint Cloud-Assisted Neural Image Compression
High-efficient image compression is a critical requirement. In several scenarios where multiple modalities of data are captured by different sensors, the auxiliary information from other modalities are not fully leverage…
Autonomous DrivingImage CompressionControllable Distortion-Perception Tradeoff Through Latent Diffusion for Neural Image Compression
Neural image compression often faces a challenging trade-off among rate, distortion and perception. While most existing methods typically focus on either achieving high pixel-level fidelity or optimizing for perceptual m…
DecoderImage CompressionProgressive Compression with Universally Quantized Diffusion Models
Diffusion probabilistic models have achieved mainstream success in many generative modeling tasks, from image generation to inverse problem solving. A distinct feature of these models is that they correspond to deep hier…
Image CompressionImage GenerationQuantizationReal-time Identity Defenses against Malicious Personalization of Diffusion Models
Personalized generative diffusion models, capable of synthesizing highly realistic images based on a few reference portraits, may pose substantial social, ethical, and legal risks via identity replication. Existing defen…
CPUGPUImage CompressionVersatile Volumetric Medical Image Coding for Human-Machine Vision
Neural image compression (NIC) has received considerable attention due to its significant advantages in feature representation and data optimization. However, most existing NIC methods for volumetric medical images focus…
Image CompressionImage ReconstructionUnicorn: Unified Neural Image Compression with One Number Reconstruction
Prevalent lossy image compression schemes can be divided into: 1) explicit image compression (EIC), including traditional standards and neural end-to-end algorithms; 2) implicit image compression (IIC) based on implicit …
DecoderImage CompressionVision Transformer-based Semantic Communications With Importance-Aware Quantization
Semantic communications provide significant performance gains over traditional communications by transmitting task-relevant semantic features through wireless channels. However, most existing studies rely on end-to-end (…
image-classificationImage ClassificationImage CompressionQuantization+1UniMIC: Towards Universal Multi-modality Perceptual Image Compression
We present UniMIC, a universal multi-modality image compression framework, intending to unify the rate-distortion-perception (RDP) optimization for multiple image codecs simultaneously through excavating cross-modality g…
DecoderImage CompressionLL-ICM: Image Compression for Low-level Machine Vision via Large Vision-Language Model
Image Compression for Machines (ICM) aims to compress images for machine vision tasks rather than human viewing. Current works predominantly concentrate on high-level tasks like object detection and semantic segmentation…
Image CompressionImage RestorationLanguage ModelingLanguage Modelling+3Is JPEG AI going to change image forensics?
In this paper, we investigate the counter-forensic effects of the new JPEG AI standard based on neural image compression, focusing on two critical areas: deepfake image detection and image splicing localization. Neural i…
Face SwappingImage CompressionImage ForensicsImage GenerationRobust and Transferable Backdoor Attacks Against Deep Image Compression With Selective Frequency Prior
Recent advancements in deep learning-based compression techniques have surpassed traditional methods. However, deep neural networks remain vulnerable to backdoor attacks, where pre-defined triggers induce malicious behav…
Face RecognitionImage CompressionSemantic SegmentationGood, Cheap, and Fast: Overfitted Image Compression with Wasserstein Distortion
Inspired by the success of generative image models, recent work on learned image compression increasingly focuses on better probabilistic models of the natural image distribution, leading to excellent image quality. This…
Image CompressionMS-SSIMSSIMDeepFGS: Fine-Grained Scalable Coding for Learned Image Compression
Scalable coding, which can adapt to channel bandwidth variation, performs well in today's complex network environment. However, most existing scalable compression methods face two challenges: reduced compression performa…
DecoderImage CompressionMS-SSIMSSIMGeneralized Gaussian Model for Learned Image Compression
In learned image compression, probabilistic models play an essential role in characterizing the distribution of latent variables. The Gaussian model with mean and scale parameters has been widely used for its simplicity …
Image CompressionmodelUpsampling Improvement for Overfitted Neural Coding
Neural image compression, based on auto-encoders and overfitted representations, relies on a latent representation of the coded signal. This representation needs to be compact and uses low resolution feature maps. In the…
Image Compression