Papers Image Compression
“Image Compression” 태그가 달린 논문 1,163편 · 필터 해제
Fast Training-free Perceptual Image Compression
Training-free perceptual image codec adopt pre-trained unconditional generative model during decoding to avoid training new conditional generative model. However, they heavily rely on diffusion inversion or sample commun…
Image CompressionABC: Adaptive BayesNet Structure Learning for Computational Scalable Multi-task Image Compression
Neural Image Compression (NIC) has revolutionized image compression with its superior rate-distortion performance and multi-task capabilities, supporting both human visual perception and machine vision tasks. However, it…
Image CompressionBreaking the Multi-Enhancement Bottleneck: Domain-Consistent Quality Enhancement for Compressed Images
Quality enhancement methods have been widely integrated into visual communication pipelines to mitigate artifacts in compressed images. Ideally, these quality enhancement methods should perform robustly when applied to i…
Image CompressionOptimizing Learned Image Compression on Scalar and Entropy-Constraint Quantization
The continuous improvements on image compression with variational autoencoders have lead to learned codecs competitive with conventional approaches in terms of rate-distortion efficiency. Nonetheless, taking the quantiza…
Image CompressionQuantizationFourier-Modulated Implicit Neural Representation for Multispectral Satellite Image Compression
Multispectral satellite images play a vital role in agriculture, fisheries, and environmental monitoring. However, their high dimensionality, large data volumes, and diverse spatial resolutions across multiple channels p…
Data CompressionImage CompressionStructured Pruning and Quantization for Learned Image Compression
The high computational costs associated with large deep learning models significantly hinder their practical deployment. Model pruning has been widely explored in deep learning literature to reduce their computational bu…
image-classificationImage ClassificationImage CompressionModel Compression+4Flexible Mixed Precision Quantization for Learned Image Compression
Despite its improvements in coding performance compared to traditional codecs, Learned Image Compression (LIC) suffers from large computational costs for storage and deployment. Model quantization offers an effective sol…
Image CompressionQuantizationYour Demands Deserve More Bits: Referring Semantic Image Compression at Ultra-low Bitrate
With the help of powerful generative models, Semantic Image Compression (SIC) has achieved impressive performance at ultra-low bitrate. However, due to coarse-grained visual-semantic alignment and inherent randomness, th…
Image CompressionSemantics-Guided Generative Image Compression
Advancements in text-to-image generative AI with large multimodal models are spreading into the field of image compression, creating high-quality representation of images at extremely low bit rates. This work introduces …
DecoderImage CompressionSemantic CompressionSemantic SegmentationSynonymous Variational Inference for Perceptual Image Compression
Recent contributions of semantic information theory reveal the set-element relationship between semantic and syntactic information, represented as synonymous relationships. In this paper, we propose a synonymous variatio…
Image CompressionVariational InferenceGenerative Image Compression by Estimating Gradients of the Rate-variable Feature Distribution
While learned image compression (LIC) focuses on efficient data transmission, generative image compression (GIC) extends this framework by integrating generative modeling to produce photo-realistic reconstructed images. …
Image CompressionTokBench: Evaluating Your Visual Tokenizer before Visual Generation
In this work, we reveal the limitations of visual tokenizers and VAEs in preserving fine-grained features, and propose a benchmark to evaluate reconstruction performance for two challenging visual contents: text and face…
Face RecognitionFace ReconstructionImage CompressionOptical Character Recognition (OCR)Accelerating Learned Image Compression Through Modeling Neural Training Dynamics
As learned image compression (LIC) methods become increasingly computationally demanding, enhancing their training efficiency is crucial. This paper takes a step forward in accelerating the training of LIC methods by mod…
Image CompressionLow-Rank Adaptation of Pre-trained Vision Backbones for Energy-Efficient Image Coding for Machine
Image Coding for Machines (ICM) focuses on optimizing image compression for AI-driven analysis rather than human perception. Existing ICM frameworks often rely on separate codecs for specific tasks, leading to significan…
Image CompressionOne-Step Diffusion-Based Image Compression with Semantic Distillation
While recent diffusion-based generative image codecs have shown impressive performance, their iterative sampling process introduces unpleasing latency. In this work, we revisit the design of a diffusion-based codec and a…
Image CompressionGenerative Latent Coding for Ultra-Low Bitrate Image and Video Compression
Most existing approaches for image and video compression perform transform coding in the pixel space to reduce redundancy. However, due to the misalignment between the pixel-space distortion and human perception, such sc…
Image CompressionVideo CompressionOSCAR: One-Step Diffusion Codec for Image Compression Across Multiple Bit-rates
Pretrained latent diffusion models have shown strong potential for lossy image compression, owing to their powerful generative priors. Most existing diffusion-based methods reconstruct images by iteratively denoising fro…
DenoisingImage CompressionCombining Progressive Image Compression and Random Access in DNA Data Storage
The exponential increase in storage demand and low lifespan of data storage devices has resulted in long-term archival and preservation emerging as a critical bottlenecks in data storage. In order to meet this demand, re…
Data CompressionImage CompressionHigher fidelity perceptual image and video compression with a latent conditioned residual denoising diffusion model
Denoising diffusion models achieved impressive results on several image generation tasks often outperforming GAN based models. Recently, the generative capabilities of diffusion models have been employed for perceptual i…
DecoderDenoisingImage CompressionImage Generation+1GANCompress: GAN-Enhanced Neural Image Compression with Binary Spherical Quantization
The exponential growth of visual data in digital communications has intensified the need for efficient compression techniques that balance rate-distortion performance with computational feasibility. While recent neural c…
Computational EfficiencyImage CompressionQuantizationSSIM