Papers Image Compression
“Image Compression” 태그가 달린 논문 1,163편 · 필터 해제
High Quality Underwater Image Compression with Adaptive Correction and Codebook-based Augmentation
With the increasing exploration and exploitation of the underwater world, underwater images have become a critical medium for human interaction with marine environments, driving extensive research into their efficient tr…
Image CompressionUltra Lowrate Image Compression with Semantic Residual Coding and Compression-aware Diffusion
Existing multimodal large model-based image compression frameworks often rely on a fragmented integration of semantic retrieval, latent compression, and generative models, resulting in suboptimal performance in both reco…
Image CompressionRetrievalSemantic RetrievalTowards Facial Image Compression with Consistency Preserving Diffusion Prior
With the widespread application of facial image data across various domains, the efficient storage and transmission of facial images has garnered significant attention. However, the existing learned face image compressio…
Image CompressionPICD: Versatile Perceptual Image Compression with Diffusion Rendering
Recently, perceptual image compression has achieved significant advancements, delivering high visual quality at low bitrates for natural images. However, for screen content, existing methods often produce noticeable arti…
Image CompressionGone With the Bits: Revealing Racial Bias in Low-Rate Neural Compression for Facial Images
Neural compression methods are gaining popularity due to their superior rate-distortion performance over traditional methods, even at extremely low bitrates below 0.1 bpp. As deep learning architectures, these models are…
Image CompressionEasz: An Agile Transformer-based Image Compression Framework for Resource-constrained IoTs
Neural image compression, necessary in various machine-to-machine communication scenarios, suffers from its heavy encode-decode structures and inflexibility in switching between different compression levels. Consequently…
Computational EfficiencyImage CompressionImage ReconstructionLoC-LIC: Low Complexity Learned Image Coding Using Hierarchical Feature Transforms
Current learned image compression models typically exhibit high complexity, which demands significant computational resources. To overcome these challenges, we propose an innovative approach that employs hierarchical fea…
Image CompressionWhy Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields
The rapid development of AIGC foundation models has revolutionized the paradigm of image compression, which paves the way for the abandonment of most pixel-level transform and coding, compelling us to ask: why compress w…
Image CompressionImage GenerationPrompt EngineeringMLICv2: Enhanced Multi-Reference Entropy Modeling for Learned Image Compression
Recent advancements in learned image compression (LIC) have yielded impressive performance gains. Notably, the learned image compression models with multi-reference entropy models (MLIC series) have significantly outperf…
Computational EfficiencyImage CompressionTVC: Tokenized Video Compression with Ultra-Low Bitrate
Tokenized visual representations have shown great promise in image compression, yet their extension to video remains underexplored due to the challenges posed by complex temporal dynamics and stringent bitrate constraint…
DecoderImage CompressionVideo CompressionLimitNet: Progressive, Content-Aware Image Offloading for Extremely Weak Devices & Networks
IoT devices have limited hardware capabilities and are often deployed in remote areas. Consequently, advanced vision models surpass such devices' processing and storage capabilities, requiring offloading of such tasks to…
Image CompressionEfficient Masked Image Compression with Position-Indexed Self-Attention
In recent years, image compression for high-level vision tasks has attracted considerable attention from researchers. Given that object information in images plays a far more crucial role in downstream tasks than backgro…
Image CompressionPositionAll-in-One Transferring Image Compression from Human Perception to Multi-Machine Perception
Efficiently transferring Learned Image Compression (LIC) model from human perception to machine perception is an emerging challenge in vision-centric representation learning. Existing approaches typically adapt LIC to do…
AllImage CompressionRepresentation LearningUniversal Representations for Classification-enhanced Lossy Compression
In lossy compression, the classical tradeoff between compression rate and reconstruction distortion has traditionally guided algorithm design. However, Blau and Michaeli [5] introduced a generalized framework, known as t…
ClassificationImage CompressionSubjective Visual Quality Assessment for High-Fidelity Learning-Based Image Compression
Learning-based image compression methods have recently emerged as promising alternatives to traditional codecs, offering improved rate-distortion performance and perceptual quality. JPEG AI represents the latest standard…
BenchmarkingImage CompressionImage ReconstructionTriplet3DM-WeConvene: Learned Image Compression with 3D Multi-Level Wavelet-Domain Convolution and Entropy Model
Learned image compression (LIC) has recently made significant progress, surpassing traditional methods. However, most LIC approaches operate mainly in the spatial domain and lack mechanisms for reducing frequency-domain …
Image CompressionBridging the Gap between Gaussian Diffusion Models and Universal Quantization for Image Compression
Generative neural image compression supports data representation at extremely low bitrate, synthesizing details at the client and consistently producing highly realistic images. By leveraging the similarities between qua…
Image CompressionQuantizationLearned Image Compression with Dictionary-based Entropy Model
Learned image compression methods have attracted great research interest and exhibited superior rate-distortion performance to the best classical image compression standards of the present. The entropy model plays a key …
Image CompressionmodelLearned Image Compression and Restoration for Digital Pathology
Digital pathology images play a crucial role in medical diagnostics, but their ultra-high resolution and large file sizes pose significant challenges for storage, transmission, and real-time visualization. To address the…
DiagnosticImage CompressionImage Reconstructionwhole slide imagesShiftLIC: Lightweight Learned Image Compression with Spatial-Channel Shift Operations
Learned Image Compression (LIC) has attracted considerable attention due to their outstanding rate-distortion (R-D) performance and flexibility. However, the substantial computational cost poses challenges for practical …
Image Compression