Papers MS-SSIM
“MS-SSIM” 태그가 달린 논문 179편 · 필터 해제
Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation
AI requires extensive datasets, while medical data is subject to high data protection. Anonymization is essential, but poses a challenge for some regions, such as the head, as identifying structures overlap with regions …
MS-SSIMSegmentationSSIMTumor SegmentationMRI Image Generation Based on Text Prompts
This study explores the use of text-prompted MRI image generation with the Stable Diffusion (SD) model to address challenges in acquiring real MRI datasets, such as high costs, limited rare case samples, and privacy conc…
Image GenerationMS-SSIMSSIMHow Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings
Neural networks that map between low dimensional spaces are ubiquitous in computer graphics and scientific computing; however, in their naive implementation, they are unable to learn high frequency information. We presen…
MS-SSIMregressionSSIMDo image and video quality metrics model low-level human vision?
Image and video quality metrics, such as SSIM, LPIPS, and VMAF, are aimed to predict the perceived quality of the evaluated content and are often claimed to be "perceptual". Yet, few metrics directly model human visual p…
MS-SSIMSSIMEnhanced Diagnostic Fidelity in Pathology Whole Slide Image Compression via Deep Learning
Accurate diagnosis of disease often depends on the exhaustive examination of Whole Slide Images (WSI) at microscopic resolution. Efficient handling of these data-intensive images requires lossy compression techniques. Th…
DiagnosticImage CompressionMS-SSIMSSIM+1Pretext Task Adversarial Learning for Unpaired Low-field to Ultra High-field MRI Synthesis
Given the scarcity and cost of high-field MRI, the synthesis of high-field MRI from low-field MRI holds significant potential when there is limited data for training downstream tasks (e.g. segmentation). Low-field MRI of…
Contrastive LearningMS-SSIMSSIMSeeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization
Judging the similarity of visualizations is crucial to various applications, such as visualization-based search and visualization recommendation systems. Recent studies show deep-feature-based similarity metrics correlat…
Image Super-ResolutionMS-SSIMRecommendation SystemsSSIM+2Interleaved 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-SSIMQuantizationSSIMPerceptual Video Compression with Neural Wrapping
Standard video codecs are rate-distortion optimization machines, where distortion is typically quantified using PSNR versus the source. However, it is now widely accepted that increasing PSNR does not necessarily tra…
MS-SSIMSSIMVideo CompressionEnhancing Amyloid PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion Models
Amyloid PET imaging plays a crucial role in the diagnosis and research of Alzheimer’s disease (AD), allowing non-invasive detection of amyloid-β plaques in the brain. However, the low spatial resolution of PET scans limi…
MS-SSIMSSIMSuper-ResolutionSynthetic Data GenerationGood, Cheap, and Fast: Overfitted Image Compression with Wasserstein Distortion
Inspired by the success of generative image models, recent work on learned image compression increasingly focuses on better probabilistic models of the natural image distribution, leading to excellent image quality. This…
Image CompressionMS-SSIMSSIMDeepFGS: Fine-Grained Scalable Coding for Learned Image Compression
Scalable coding, which can adapt to channel bandwidth variation, performs well in today's complex network environment. However, most existing scalable compression methods face two challenges: reduced compression performa…
DecoderImage CompressionMS-SSIMSSIMMCUCoder: Adaptive Bitrate Learned Video Compression for IoT Devices
The rapid growth of camera-based IoT devices demands the need for efficient video compression, particularly for edge applications where devices face hardware constraints, often with only 1 or 2 MB of RAM and unstable int…
MS-SSIMSSIMVideo CompressionUSTC-TD: A Test Dataset and Benchmark for Image and Video Coding in 2020s
Image/video coding has been a remarkable research area for both academia and industry for many years. Testing datasets, especially high-quality image/video datasets are desirable for the justified evaluation of coding-re…
4kMS-SSIMSSIMVisual Verity in AI-Generated Imagery: Computational Metrics and Human-Centric Analysis
The rapid advancements in AI technologies have revolutionized the production of graphical content across various sectors, including entertainment, advertising, and e-commerce. These developments have spurred the need for…
MS-SSIMSSIMMicroSSIM: Improved Structural Similarity for Comparing Microscopy Data
Microscopy is routinely used to image biological structures of interest. Due to imaging constraints, acquired images, also called as micrographs, are typically low-SNR and contain noise. Over the last few years, regressi…
DenoisingMS-SSIMSSIMBidirectional Stereo Image Compression with Cross-Dimensional Entropy Model
With the rapid advancement of stereo vision technologies, stereo image compression has emerged as a crucial field that continues to draw significant attention. Previous approaches have primarily employed a unidirectional…
Image CompressionMS-SSIMSSIMVideo CompressionA Study on the Effect of Color Spaces in Learned Image Compression
In this work, we present a comparison between color spaces namely YUV, LAB, RGB and their effect on learned image compression. For this we use the structure and color based learned image codec (SLIC) from our prior work,…
Image CompressionMS-SSIMSSIMLearned Image Compression for HE-stained Histopathological Images via Stain Deconvolution
Processing histopathological Whole Slide Images (WSI) leads to massive storage requirements for clinics worldwide. Even after lossy image compression during image acquisition, additional lossy compression is frequently p…
Data CompressionImage CompressionMS-SSIMSSIM+1