Papers Image Compression
“Image Compression” 태그가 달린 논문 1,163편 · 필터 해제
Lightweight Embedded FPGA Deployment of Learned Image Compression with Knowledge Distillation and Hybrid Quantization
Learnable Image Compression (LIC) has shown the potential to outperform standardized video codecs in RD efficiency, prompting the research for hardware-friendly implementations. Most existing LIC hardware implementations…
Image CompressionKnowledge DistillationQuantizationDLF: Extreme Image Compression with Dual-generative Latent Fusion
Recent studies in extreme image compression have achieved remarkable performance by compressing the tokens from generative tokenizers. However, these methods often prioritize clustering common semantics within the datase…
Image CompressionCAT-3DGS: A Context-Adaptive Triplane Approach to Rate-Distortion-Optimized 3DGS Compression
3D Gaussian Splatting (3DGS) has recently emerged as a promising 3D representation. Much research has been focused on reducing its storage requirements and memory footprint. However, the needs to compress and transmit th…
3DGSImage CompressionTaming Large Multimodal Agents for Ultra-low Bitrate Semantically Disentangled Image Compression
It remains a significant challenge to compress images at ultra-low bitrate while achieving both semantic consistency and high perceptual quality. We propose a novel image compression framework, Semantically Disentangled …
DecoderImage CompressionObjectTowards Lossless Implicit Neural Representation via Bit Plane Decomposition
We quantify the upper bound on the size of the implicit neural representation (INR) model from a digital perspective. The upper bound of the model size increases exponentially as the required bit-precision increases. To …
Image CompressionQuantizationBalanced Rate-Distortion Optimization in Learned Image Compression
Learned image compression (LIC) using deep learning architectures has seen significant advancements, yet standard rate-distortion (R-D) optimization often encounters imbalanced updates due to diverse gradients of the rat…
Image CompressionTransformer-Based Nonlinear Transform Coding for Multi-Rate CSI Compression in MIMO-OFDM Systems
We propose a novel approach for channel state information (CSI) compression in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, where the frequency-domain channel matrix is t…
Image CompressionQuantizationContinuous Patch Stitching for Block-wise Image Compression
Most recently, learned image compression methods have outpaced traditional hand-crafted standard codecs. However, their inference typically requires to input the whole image at the cost of heavy computing resources, espe…
Image CompressionLarge Language Model for Lossless Image Compression with Visual Prompts
Recent advancements in deep learning have driven significant progress in lossless image compression. With the emergence of Large Language Models (LLMs), preliminary attempts have been made to leverage the extensive prior…
Image CompressionLanguage ModelingLanguage ModellingLarge Language ModelInterleaved Block-based Learned Image Compression with Feature Enhancement and Quantization Error Compensation
In recent years, learned image compression (LIC) methods have achieved significant performance improvements. However, obtaining a more compact latent representation and reducing the impact of quantization errors remain k…
Image CompressionMS-SSIMQuantizationSSIMFD-LSCIC: Frequency Decomposition-based Learned Screen Content Image Compression
The learned image compression (LIC) methods have already surpassed traditional techniques in compressing natural scene (NS) images. However, directly applying these methods to screen content (SC) images, which possess di…
Image CompressionMS-SSIMQuantizationSSIMStereo Image Coding for Machines with Joint Visual Feature Compression
2D image coding for machines (ICM) has achieved great success in coding efficiency, while less effort has been devoted to stereo image fields. To promote the efficiency of stereo image compression (SIC) and intelligent a…
Feature CompressionImage CompressionCompact Latent Representation for Image Compression (CLRIC)
Current image compression models often require separate models for each quality level, making them resource-intensive in terms of both training and storage. To address these limitations, we propose an innovative approach…
Image CompressionA Lightweight Model for Perceptual Image Compression via Implicit Priors
Perceptual image compression has shown strong potential for producing visually appealing results at low bitrates, surpassing classical standards and pixel-wise distortion-oriented neural methods. However, existing method…
DecoderImage CompressionResiComp: Loss-Resilient Image Compression via Dual-Functional Masked Visual Token Modeling
Recent advancements in neural image codecs (NICs) are of significant compression performance, but limited attention has been paid to their error resilience. These resulting NICs tend to be sensitive to packet losses, whi…
Image CompressionMissing ValuesPacket Loss ConcealmentA Fast Quantum Image Compression Algorithm based on Taylor Expansion
With the increasing demand for storing images, traditional image compression methods face challenges in balancing the compressed size and image quality. However, the hybrid quantum-classical model can recover this weakne…
Image CompressionOptimizing CNN Architectures for Advanced Thoracic Disease Classification
Machine learning, particularly convolutional neural networks (CNNs), has shown promise in medical image analysis, especially for thoracic disease detection using chest X-ray images. In this study, we evaluate various CNN…
Binary ClassificationClassificationImage CompressionMedical Image Analysis+3SQ-GAN: Semantic Image Communications Using Masked Vector Quantization
This work introduces Semantically Masked VQ-GAN (SQ-GAN), a novel approach integrating generative models to optimize image compression for semantic/task-oriented communications. SQ-GAN employs off-the-shelf semantic sema…
Image CompressionQuantizationSegmentationSemantic SegmentationHDCompression: Hybrid-Diffusion Image Compression for Ultra-Low Bitrates
Image compression under ultra-low bitrates remains challenging for both conventional learned image compression (LIC) and generative vector-quantized (VQ) modeling. Conventional LIC suffers from severe artifacts due to he…
Image CompressionImage ReconstructionQuantizationLinear Attention Modeling for Learned Image Compression
Recent years, learned image compression has made tremendous progress to achieve impressive coding efficiency. Its coding gain mainly comes from non-linear neural network-based transform and learnable entropy modeling. Ho…
Image Compression