Papers Image Compression
“Image Compression” 태그가 달린 논문 1,161편 · 필터 해제
CrossMambaTuning: Synergistic Spatial and Cross-Layer Adaptation for Machine Vision Compression
To reduce deployment cost and retraining overhead, adapting pretrained learned image compression (LIC) models to downstream machine vision tasks has attracted growing attention. However, existing methods typically insert…
parameter-efficient fine-tuningImage CompressionToken-Oriented Semantic Communication with Pretrained Vision Transformers
Token communications realize the semantic communication principle at the granularity of transformer tokens, providing a promising direction for client--server collaborative inference in resource-constrained edge systems.…
Semantic CommunicationImage CompressionPractical Lossless Volumetric Medical Image Compression via Tri-plane Context Tree Learning
Lossless compression of volumetric medical images is of paramount importance for clinical and research applications where data fidelity is essential. Traditional compression methods are often limited in efficiency due to…
Image CompressionGVCCTurbo: Rate-Compute Quality Scheduling for Codebook Driven Generative Compression
Codebook-driven generative compression uses a pretrained image or video generator as a zero-shot visual prior and transmits compact codebook indices to guide reconstruction at ultra-low bitrate. Current codecs tie each f…
Image CompressionMixCompress: Mixture of Experts for Variable Rate Learned Image Compression
Learned image compression (LIC) is bottlenecked by the need to store independent models for each rate-distortion operating point. Existing variable bit-rate (VBR) methods aim to reduce this overhead via dense parameter m…
Image CompressionLUMI: Tokenizer-Agnostic LLM-Based Lossless Image Compression
Large language model (LLM)-based lossless image compression methods typically represent pixel data through the native text interface of a pretrained model, converting pixel values into token sequences that the LLM proces…
Image CompressionCollaborative Synthetic Data Generation for Knowledge Transfer in Federated Learning
One-shot federated learning (OSFL) addresses the communication overhead of federated learning by limiting training to a single round, but doing so without sacrificing model quality is non-trivial, particularly when clien…
Synthetic Data GenerationFederated LearningImage CompressionClustered Codebook Quantization for 2D Gaussian-based Image Compression
Gaussian-based image representations effectively model image content using compact parametric primitives while preserving high visual fidelity, yet storing a large number of floating-point parameters per primitive degrad…
Image CompressionOptimizing Image Preparation and Compression for Face Recognition within 1024 Bytes
ICAO-compliant machine readable travel documents enable automated biometric face verification. The biometric reference is stored on an RFID chip included in form of a JPEG or JPEG 2000 compressed facial image. In contras…
Face VerificationImage CompressionFace RecognitionFlowCodec: One-Step Flow Prior for Generative Image Compression
Diffusion-based image compression methods, leveraging powerful generative priors, have demonstrated remarkable perceptual quality at ultra-low bitrates. However, adapting modern generative models to image compression oft…
Image CompressionMoECodec: Image Compression for joint human and machine perception via Mixture-of-Experts
Image compression for machines calls for a unified codec that serves multiple downstream vision tasks. Existing approaches either adopt task-specific end-to-end designs, raising parameter and deployment overhead, or rely…
Image ReconstructionImage CompressionPaaF: Raising the perceived quality of INR-Based Image Compression
Implicit Neural Representations (INRs) have recently emerged as a promising paradigm for image compression, offering a fundamentally different approach from traditional and learned codecs. Nevertheless, INR-based methods…
Image CompressionVariable-Rate Deep Image Compression based on Low-Rank Adaptation by Progressive Learning
In the digital age, image compression is crucial for numerous applications, including web media, streaming services, high-resolution medical imaging, and connected vehicle networks, enabling efficient data storage and tr…
parameter-efficient fine-tuningImage CompressionLearned JPEG Compression for DNN Vision
JPEG, a lossy image compression technique designed for human viewers, has maintained its dominance for decades. However, in the era of artificial intelligence (AI), a substantial portion of image data, often compressed b…
Image CompressionLearned Image Compression for Vision-Language-Action Models
Vision-language-action (VLA) models increasingly rely on high-frequency multi-camera observations, making visual communication a major bottleneck for real-time robotic control in bandwidth-constrained or distributed depl…
Image CompressionDual-Constrained Diffusion Image Compression for Operational Rate-Distortion-Perception Optimization
The rate-distortion-perception (RDP) trade-off extends classical rate--distortion theory by imposing a distributional constraint on reconstructions, providing a unified framework for neural image compression that jointly…
Image CompressionBalancing Image Compression and Generation with Bootstrapped Tokenization
Despite progress in image tokenization, standard methods encode redundant information by mixing all granularities within each token, thus redundancy persists between tokens. The mix of information of different granularit…
Representation LearningImage CompressionExploiting Semantic and Pixel Representations for Ultra-Low Bitrate Image Compression
Most existing extreme compression methods fail to achieve an optimal rate-distortion-perception trade-off, as they typically prioritize perceptual fidelity and visual realism over pixel-level accuracy. Consequently, the …
Image CompressionLow-Rank Adaptation of Frozen Vision-Language Models for Blind Image Quality Assessment
Blind image quality assessment (BIQA) predicts perceived image quality without access to a pristine reference and is fundamental to applications such as image compression, transmission, and restoration. Recent BIQA metho…
Image Quality AssessmentImage CompressionPIXELRAG: Web Screenshots Beat Text for Retrieval-Augmented Generation
Augmenting large language models (LLMs) with retrieved web text has become a dominant paradigm, yet the web is not natively textual: existing systems depend on complex parsing pipelines that linearize HTML and discard la…
Image Compression