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.
Libraries
Use these libraries to find MS-SSIM models and implementationsMost implemented papers
Deep Image Compression via End-to-End Learning
pkorus/l3ic
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We present a lossy image compression method based on deep convolutional neural networks (CNNs), which outperforms the existing BPG, WebP, JPEG2000 and JPEG as measured via multi-scale structural similarity (MS-SSIM), at the same bit rate.
Image Quality Assessment and Color Difference
In this work, we combine these approaches by extending CIEDE2000 formula with perceptual color difference to assess image quality.
DewarpNet: Single-Image Document Unwarping With Stacked 3D and 2D Regression Networks
cvlab-stonybrook/DewarpNet
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ICCV 2019
In this work, we propose DewarpNet, a deep-learning approach for document image unwarping from a single image.
Image Super-Resolution Improved by Edge Information
eldrey/eesr-masters_project
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SMC 2019
As well as in other knowledge domains, deep learning techniques have revolutionized the development of image super-resolution approaches.
Observer Dependent Lossy Image Compression
DS3Lab/odlc
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To that end, we use a family of loss functions that allows to optimize deep image compression depending on the observer and to interpolate between human perceived visual quality and classification accuracy, enabling a more unified view on image compression.
Neural Image Compression via Non-Local Attention Optimization and Improved Context Modeling
njuvision/nic
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This paper proposes a novel Non-Local Attention optmization and Improved Context modeling-based image compression (NLAIC) algorithm, which is built on top of the deep nerual network (DNN)-based variational auto-encoder (VAE) structure.
An End-to-End Joint Learning Scheme of Image Compression and Quality Enhancement with Improved Entropy Minimization
JooyoungLeeETRI/CA_Entropy_Model
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In order to show the effectiveness of our proposed JointIQ-Net, extensive experiments have been performed, and showed that the JointIQ-Net achieves a remarkable performance improvement in coding efficiency in terms of both PSNR and MS-SSIM, compared to the previous learned image compression methods and the conventional codecs such as VVC Intra (VTM 7. 1), BPG, and JPEG2000.
Generalized Octave Convolutions for Learned Multi-Frequency Image Compression
rezafuru/generalized-octave-convolution-compression
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Learned image compression has recently shown the potential to outperform the standard codecs.
Asymmetric Gained Deep Image Compression With Continuous Rate Adaptation
With the development of deep learning techniques, the combination of deep learning with image compression has drawn lots of attention.
M-LVC: Multiple Frames Prediction for Learned Video Compression
JianpingLin/M-LVC_CVPR2020
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CVPR 2020
To compensate for the compression error of the auto-encoders, we further design a MV refinement network and a residual refinement network, taking use of the multiple reference frames as well.
