MS-SSIM Edit Task
Computer Vision • Document Layout Analysis
About
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.
Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
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Greatest papers with code
End-to-end Optimized Image Compression
We describe an image compression method, consisting of a nonlinear analysis transformation, a uniform quantizer, and a nonlinear synthesis transformation.
CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
• InterDigitalInc/CompressAI
• 
This paper presents CompressAI, a platform that provides custom operations, layers, models and tools to research, develop and evaluate end-to-end image and video compression codecs.
Joint Autoregressive and Hierarchical Priors for Learned Image Compression
NeurIPS 2018
• InterDigitalInc/CompressAI
•
While it is well known that autoregressive models come with a significant computational penalty, we find that in terms of compression performance, autoregressive and hierarchical priors are complementary and, together, exploit the probabilistic structure in the latents better than all previous learned models.
DVC: An End-to-end Deep Video Compression Framework
Conventional video compression approaches use the predictive coding architecture and encode the corresponding motion information and residual information.
DewarpNet: Single-Image Document Unwarping With Stacked 3D and 2D Regression Networks
ICCV 2019
• cvlab-stonybrook/DewarpNet
•
In this work, we propose DewarpNet, a deep-learning approach for document image unwarping from a single image.
Ranked #1 on
MS-SSIM
on DocUNet
(using extra training data)
Conditional Probability Models for Deep Image Compression
CVPR 2018
• fab-jul/imgcomp-cvpr
• 
During training, the auto-encoder makes use of the context model to estimate the entropy of its representation, and the context model is concurrently updated to learn the dependencies between the symbols in the latent representation.
An End-to-End Joint Learning Scheme of Image Compression and Quality Enhancement with Improved Entropy Minimization
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.
TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network
In this work, we present the Text Conditioned Auxiliary Classifier Generative Adversarial Network, (TAC-GAN) a text to image Generative Adversarial Network (GAN) for synthesizing images from their text descriptions.
OpenDVC: An Open Source Implementation of the DVC Video Compression Method
At the time of writing this report, several learned video compression methods are superior to DVC, but currently none of them provides open source codes.
Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model
The experiments show that our approach achieves the state-of-the-art learned video compression performance in terms of both PSNR and MS-SSIM.