Papers Image Compression
“Image Compression” 태그가 달린 논문 1,163편 · 필터 해제
Low-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 CompressionChWDTA: Channel-wise Wavelet-Domain Transformer Attention and Entropy Modeling for Learned Image Compression
State-of-the-art learned image compression (LIC) schemes are increasingly based on hybrid CNN-transformer architectures. To further improve rate-distortion performance, we introduce channel-wise wavelet transforms into b…
Image CompressionEfficient Learned Image Compression without Entropy Coding
Entropy coding is widely used in typical learned image compression (LIC) that converts latents into a compact bitstream. However, entropy coding is typically sequential and becomes the coding latency bottleneck. To overc…
Image CompressionDistributed Image Compression with Multimodal Side Information at Extremely Low Bitrates
Distributed Image Compression (DIC) is crucial for multi-view transmission, especially when operating at extremely low bitrates (< 0.1 bpp). Its core challenge is effectively utilizing side information to achieve high-qu…
Image CompressionDeep learning-based compression of giga-resolution whole slide images
Implementation of digital pathology leads to an increased number of whole slide images (WSIs). The large size of WSIs is challenging. Today, WSIs are compressed with codecs like JPEG resulting in several gigabytes per WS…
Image CompressionAdaptive Fused Prior Transfer for Controllable Generative Image Compression
Learned image compression achieves competitive rate-distortion performance, but very-low-bitrate reconstruction remains challenging because the transmitted representation cannot preserve fine textures and local structure…
Image CompressionDual-Latent Collaborative Decoding for Fidelity-Perception Balanced Image Compression
Learned image compression (LIC) increasingly requires reconstructions that balance distortion fidelity and perceptual realism across a wide range of bitrates. However, most existing methods still rely on a single compres…
Image CompressionML-CLIPSim: Multi-Layer CLIP Similarity for Machine-Oriented Image Quality
We study full-reference image quality assessment from a machine-centric perspective, where images are evaluated by how well they preserve information for downstream models. We formulate machine-oriented quality as a late…
Image Quality AssessmentImage CompressionControl Your View: High-Resolution Global Semantic Manipulation in Learned Image Compression
Learned image compression (LIC) integrates deep neural networks (DNNs) to map high-dimensional images into compact latent representations, reducing redundancy and achieving superior rate-distortion (RD) performance in be…
Image CompressionAQMP: Image compression through Adaptive Quadtree Refinement and Matching Pursuit with Hyperparameter Optimization
We present AQMP, a novel image codec combining Adaptive Quadtree Refinement with Matching Pursuit. Unlike conventional Matching Pursuit methods that operate on fixed-size sub-images, AQMP dynamically adapts block sizes t…
Hyperparameter OptimizationImage CompressionCoarse-to-Fine: Progressive Image Compression for Semantically Hierarchical Classification
Recent advances in learned image compression (LIC) have enabled practical deployments, spurring active research into image compression for machines and progressive coding schemes. However, their integration remains under…
Image CompressionSAMIC: A Lightweight Semantic-Aware Mamba for Efficient Perceptual Image Compression
Perceptual image compression focuses on preserving high visual quality under low-bitrate constraints. Most existing approaches to perceptual compression leverage the strong generative capabilities of generative adversari…
Long-range modelingImage CompressionWhat Matters in Practical Learned Image Compression
One of the major differentiators unlocked by learned codecs relative to their hard-coded traditional counterparts is their ability to be optimized directly to appeal to the human visual system. Despite this potential, a …
Neural Architecture SearchImage CompressionDiffusion-OAMP for Joint Image Compression and Wireless Transmission
Joint image compression and wireless transmission remain relatively underexplored compared to generic image restoration, despite its importance in practical communication systems. We formulate this problem under an equiv…
Image CompressionImage RestorationImage Compression with Bubble-Aware Frame Rate Adaptation for Energy-Efficient Video Capsule Endoscopy
Video Capsule Endoscopy (VCE) is a promising method for improving the medical examination of the small intestine in the gastrointestinal tract. A key challenge is their limited size, resulting in a short battery lifetime…
Image CompressionShared-kernel Wavelet Neural Networks for Poisson Image Reconstruction
The Laplacian operator transforms the image into its Laplacian field, which usually is sparse and satisfies a stable distribution. On the other hand, an image can be uniquely reconstructed from its Laplacian field via so…
Image ReconstructionImage CompressionObject TrackingSecure Rate-Distortion-Perception: A Randomized Distributed Function Computation Approach for Realism
Fundamental rate-distortion-perception (RDP) trade-offs arise in applications requiring maintained perceptual quality of reconstructed data, such as neural image compression. When compressed data is transmitted over publ…
Image CompressionCoD-Lite: Real-Time Diffusion-Based Generative Image Compression
Recent advanced diffusion methods typically derive strong generative priors by scaling diffusion transformers. However, scaling fails to generalize when adapted for real-time compression scenarios that demand lightweight…
Image CompressionDifferentiable Vector Quantization for Rate-Distortion Optimization of Generative Image Compression
The rapid growth of visual data under stringent storage and bandwidth constraints makes extremely low-bitrate image compression increasingly important. While Vector Quantization (VQ) offers strong structural fidelity, ex…
Representation LearningImage Compression