Papers Image Compression
“Image Compression” 태그가 달린 논문 1,163편 · 필터 해제
HyperVQ: Enabling Hyperprior Entropy Modeling for VQ-Based Generative Image Compression
Vector Quantization (VQ) based generative image compression has achieved remarkable perceptual quality. However, existing VQ codecs suffer from two fundamental limitations. First, they lack efficient content-adaptive ent…
Image CompressionRevisiting SVD and Wavelet Difference Reduction for Lossy Image Compression: A Reproducibility Study
This work presents an independent reproducibility study of a lossy image compression technique that integrates singular value decomposition (SVD) and wavelet difference reduction (WDR). The original paper claims that com…
Image CompressionSDGIC: A Semantic Disambiguation-Guided Generative Image Compression Method for Ultra-Low Bitrates
Generative image compression has recently shown impressive perceptual quality, but often suffers from semantic inconsistency at ultra-low bitrates (bpp < 0.05), limiting its reliable deployment in bandwidth-constrained s…
Image CompressionLearned Image Compression for Earth Observation: Implications for Downstream Segmentation Tasks
The rapid growth of data from satellite-based Earth observation (EO) systems poses significant challenges in data transmission and storage. We evaluate the potential of task-specific learned compression algorithms in thi…
Image CompressionJoint Multi-scale Gated Transformer and Prior-guided Convolutional Network for Learned Image Compression
Recently, learned image compression methods have made remarkable achievements, some of which have outperformed the traditional image codec VVC. The advantages of learned image compression methods over traditional image c…
Image CompressionDiff-ICMH: Harmonizing Machine and Human Vision in Image Compression with Generative Prior
Image compression methods are usually optimized isolatedly for human perception or machine analysis tasks. We reveal fundamental commonalities between these objectives: preserving accurate semantic information is paramou…
Image CompressionA Compact Hybrid Convolution--Frequency State Space Network for Learned Image Compression
Learned image compression (LIC) has recently benefited from Transformer- and state space models (SSM)- based backbones for modeling long-range dependencies. However, the former typically incurs quadratic complexity, wher…
Image CompressionCoD: A Diffusion Foundation Model for Image Compression
Existing diffusion codecs typically build on text-to-image diffusion foundation models like Stable Diffusion. However, text conditioning is suboptimal from a compression perspective, hindering the potential of downstream…
Image CompressionAVERY: Intent-Driven Adaptive VLM Split Computing via Embodied Self-Awareness for Efficient Disaster Response Systems
Unmanned Aerial Vehicles (UAVs) in disaster response require complex, queryable intelligence that onboard CNNs cannot provide. While Vision-Language Models (VLMs) offer this semantic reasoning, their high resource demand…
Image CompressionDReX: Pure Vision Fusion of Self-Supervised and Convolutional Representations for Image Complexity Prediction
Visual complexity prediction is a fundamental problem in computer vision with applications in image compression, retrieval, and classification. Understanding what makes humans perceive an image as complex is also a long-…
Image CompressionA Multi-Stage Optimization Framework for Deploying Learned Image Compression on FPGAs
Deep learning-based image compression (LIC) has achieved state-of-the-art rate-distortion (RD) performance, yet deploying these models on resource-constrained FPGAs remains a major challenge. This work presents a complet…
Image CompressionFlowRoI A Fast Optical Flow Driven Region of Interest Extraction Framework for High-Throughput Image Compression in Immune Cell Migration Analysis
Autonomous migration is essential for the function of immune cells such as neutrophils and plays a pivotal role in diverse diseases. Recently, we introduced ComplexEye, a multi-lens array microscope comprising 16 indepen…
Computational EfficiencyImage CompressionRethinking Autoregressive Models for Lossless Image Compression via Hierarchical Parallelism and Progressive Adaptation
Autoregressive (AR) models, the theoretical performance benchmark for learned lossless image compression, are often dismissed as impractical due to prohibitive computational cost. This work re-thinks this paradigm, intro…
Image CompressionOptimizing Input of Denoising Score Matching is Biased Towards Higher Score Norm
Many recent works utilize denoising score matching to optimize the conditional input of diffusion models. In this workshop paper, we demonstrate that such optimization breaks the equivalence between denoising score match…
Image CompressionText to 3DROI-based Deep Image Compression with Implicit Bit Allocation
Region of Interest (ROI)-based image compression has rapidly developed due to its ability to maintain high fidelity in important regions while reducing data redundancy. However, existing compression methods primarily app…
Image CompressionDynaQuant: Dynamic Mixed-Precision Quantization for Learned Image Compression
Prevailing quantization techniques in Learned Image Compression (LIC) typically employ a static, uniform bit-width across all layers, failing to adapt to the highly diverse data distributions and sensitivity characterist…
Image CompressionFrom Noise to Latent: Generating Gaussian Latents for INR-Based Image Compression
Recent implicit neural representation (INR)-based image compression methods have shown competitive performance by overfitting image-specific latent codes. However, they remain inferior to end-to-end (E2E) compression app…
Parameter PredictionImage CompressionMRT: Learning Compact Representations with Mixed RWKV-Transformer for Extreme Image Compression
Recent advances in extreme image compression have revealed that mapping pixel data into highly compact latent representations can significantly improve coding efficiency. However, most existing methods compress images in…
Representation LearningImage CompressionTurbo-DDCM: Fast and Flexible Zero-Shot Diffusion-Based Image Compression
While zero-shot diffusion-based compression methods have seen significant progress in recent years, they remain notoriously slow and computationally demanding. This paper presents an efficient zero-shot diffusion-based c…
Image CompressionTraining-Free Adaptive Quantization for Variable Rate Image Coding for Machines
Image Coding for Machines (ICM) has become increasingly important with the rapid integration of computer vision technology into real-world applications. However, most neural network-based ICM frameworks operate at a fixe…
Image Compression