Papers Image Compression
“Image Compression” 태그가 달린 논문 1,163편 · 필터 해제
Learning Optimal Linear Block Transform by Rate Distortion Minimization
Linear block transform coding remains a fundamental component of image and video compression. Although the Discrete Cosine Transform (DCT) is widely employed in all current compression standards, its sub-optimality has s…
Image CompressionVideo CompressionLearning Optimal Lattice Vector Quantizers for End-to-end Neural Image Compression
It is customary to deploy uniform scalar quantization in the end-to-end optimized Neural image compression methods, instead of more powerful vector quantization, due to the high complexity of the latter. Lattice vector q…
Computational EfficiencyImage CompressionQuantizationVariable-size Symmetry-based Graph Fourier Transforms for image compression
Modern compression systems use linear transformations in their encoding and decoding processes, with transforms providing compact signal representations. While multiple data-dependent transforms for image/video coding ca…
Image CompressionAn Information-Theoretic Regularizer for Lossy Neural Image Compression
Lossy image compression networks aim to minimize the latent entropy of images while adhering to specific distortion constraints. However, optimizing the neural network can be challenging due to its nature of learning qua…
Image CompressionImage Compression Using Novel View Synthesis Priors
Real-time visual feedback is essential for tetherless control of remotely operated vehicles, particularly during inspection and manipulation tasks. Though acoustic communication is the preferred choice for medium-range c…
Image CompressionNovel View SynthesisLMM-driven Semantic Image-Text Coding for Ultra Low-bitrate Learned Image Compression
Supported by powerful generative models, low-bitrate learned image compression (LIC) models utilizing perceptual metrics have become feasible. Some of the most advanced models achieve high compression rates and superior …
Image CaptioningImage CompressionLarge Language Models for Lossless Image Compression: Next-Pixel Prediction in Language Space is All You Need
We have recently witnessed that ``Intelligence" and `` Compression" are the two sides of the same coin, where the language large model (LLM) with unprecedented intelligence is a general-purpose lossless compressor for va…
AllAttributeImage CompressionExploring adversarial robustness of JPEG AI: methodology, comparison and new methods
Adversarial robustness of neural networks is an increasingly important area of research, combining studies on computer vision models, large language models (LLMs), and others. With the release of JPEG AI - the first stan…
Adversarial RobustnessImage CompressionAn End-to-End Real-World Camera Imaging Pipeline
Recent advances in neural camera imaging pipelines have demonstrated notable progress. Nevertheless, the real-world imaging pipeline still faces challenges including the lack of joint optimization in system components, c…
Image CompressionTone MappingDeep Learning-Based Image Compression for Wireless Communications: Impacts on Reliability,Throughput, and Latency
In wireless communications, efficient image transmission must balance reliability, throughput, and latency, especially under dynamic channel conditions. This paper presents an adaptive and progressive pipeline for learne…
Generative Adversarial NetworkImage CompressionImage ReconstructionEfficient Progressive Image Compression with Variance-aware Masking
Learned progressive image compression is gaining momentum as it allows improved image reconstruction as more bits are decoded at the receiver. We propose a progressive image compression method in which an image is first …
DecoderImage CompressionImage ReconstructionJPEG AI Image Compression Visual Artifacts: Detection Methods and Dataset
Learning-based image compression methods have improved in recent years and started to outperform traditional codecs. However, neural-network approaches can unexpectedly introduce visual artifacts in some images. We there…
Image CompressionEnhancing Learned Image Compression via Cross Window-based Attention
In recent years, learned image compression methods have demonstrated superior rate-distortion performance compared to traditional image compression methods. Recent methods utilize convolutional neural networks (CNN), var…
Image CompressionConditional Hallucinations for Image Compression
In lossy image compression, models face the challenge of either hallucinating details or generating out-of-distribution samples due to the information bottleneck. This implies that at times, introducing hallucinations is…
HallucinationImage CompressionFIPER: Generalizable Factorized Features for Robust Low-Level Vision Models
In this work, we propose using a unified representation, termed Factorized Features, for low-level vision tasks, where we test on Single Image Super-Resolution (SISR) and Image Compression. Motivated by the shared princi…
Image CompressionImage Super-ResolutionSuper-ResolutionLearning Lossless Compression for High Bit-Depth Volumetric Medical Image
Recent advances in learning-based methods have markedly enhanced the capabilities of image compression. However, these methods struggle with high bit-depth volumetric medical images, facing issues such as degraded perfor…
Image CompressionTest-time adaptation for image compression with distribution regularization
Current test- or compression-time adaptation image compression (TTA-IC) approaches, which leverage both latent and decoder refinements as a two-step adaptation scheme, have potentially enhanced the rate-distortion (R-D) …
DecoderImage CompressionTest-time AdaptationTowards Reproducible Learning-based Compression
A deep learning system typically suffers from a lack of reproducibility that is partially rooted in hardware or software implementation details. The irreproducibility leads to skepticism in deep learning technologies and…
Deep LearningImage CompressionFine-grained subjective visual quality assessment for high-fidelity compressed images
Advances in image compression, storage, and display technologies have made high-quality images and videos widely accessible. At this level of quality, distinguishing between compressed and original content becomes diffic…
Image CompressionImage Quality AssessmentFast Data-independent KLT Approximations Based on Integer Functions
The Karhunen-Lo\`eve transform (KLT) stands as a well-established discrete transform, demonstrating optimal characteristics in data decorrelation and dimensionality reduction. Its ability to condense energy compression i…
Dimensionality ReductionImage CompressionVideo Compression