MS-SSIM
53 papers with code • 1 benchmarks • 0 datasets
A MS-SSIM score helps to analyze how much a De-warping module has been able to de-warp a document image from its initial distorted view.
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Use these libraries to find MS-SSIM models and implementationsLatest papers with no code
RADIANCE: Radio-Frequency Adversarial Deep-learning Inference for Automated Network Coverage Estimation
We introduce a new gradient-based loss function that computes the magnitude and direction of change in received signal strength (RSS) values from a point within the environment.
GAN-based Image Compression with Improved RDO Process
GAN-based image compression schemes have shown remarkable progress lately due to their high perceptual quality at low bit rates.
Impact of Image Compression on In Vitro Cell Migration Analysis
We aim to identify the most suitable compression algorithm that can be used for this purpose, relating rate–distortion performance as measured in terms of peak signal-to-noise ratio (PSNR) and the multiscale structural similarity index (MS-SSIM) to the segmentation accuracy obtained by the segmentation algorithms.
ProgDTD: Progressive Learned Image Compression with Double-Tail-Drop Training
Today, most approaches for image compression, both classical and learned ones, are designed to be non-progressive.
Image Coding via Perceptually Inspired Graph Learning
Most codec designs rely on the mean squared error (MSE) as a fidelity metric in rate-distortion optimization, which allows to choose the optimal parameters in the transform domain but may fail to reflect perceptual quality.
JND-Based Perceptual Optimization For Learned Image Compression
After combining them together, we can better assign the distortion in the compressed image with the guidance of JND to preserve the high perceptual quality.
Deep Selector-JPEG: Adaptive JPEG Image Compression for Computer Vision in Image classification with Human Vision Criteria
Experimental results show that in comparison with JPEG at the same CR, Deep Selector-JPEG achieves better Rate-Accuracy performance over the ImageNet validation set for all tested DNN classifiers with gains in classification accuracy between 0. 2% and 1% at the same CRs while satisfying HV constraints.
Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models
Lossy image compression aims to represent images in as few bits as possible while maintaining fidelity to the original.
Generalizability of Deep Adult Lung Segmentation Models to the Pediatric Population: A Retrospective Study
In this work, our goal is to (i) analyze the generalizability of deep adult lung segmentation models to the pediatric population and (ii) improve performance through a stage-wise, systematic approach consisting of CXR modality-specific weight initializations, stacked ensembles, and an ensemble of stacked ensembles.
A Self-attention Guided Multi-scale Gradient GAN for Diversified X-ray Image Synthesis
The proposed architecture is compared with the multi-scale gradient GAN (MSG-GAN) to assess the diversity of generated synthetic images.
