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 implementationsLatest papers
A Person Re-identification Data Augmentation Method with Adversarial Defense Effect
This method can not only improve the accuracy of the model, but also help the model defend against adversarial examples; 2) Multi-Modal Defense, it integrates three homogeneous modal images of visible, grayscale and sketch, and further strengthens the defense ability of the model.
A Hitchhiker's Guide to Structural Similarity
The Structural Similarity (SSIM) Index is a very widely used image/video quality model that continues to play an important role in the perceptual evaluation of compression algorithms, encoding recipes and numerous other image/video processing algorithms.
CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
InterDigitalInc/CompressAI
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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.
Learning to Compress Videos without Computing Motion
Our framework exploits the regularities inherent to video motion, which we capture by using displaced frame differences as video representations to train the neural network.
Learning to Improve Image Compression without Changing the Standard Decoder
YannickStruempler/LearnedJPEG
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Therefore, we propose learning to improve the encoding performance with the standard decoder.
A Gated and Bifurcated Stacked U-Net Module for Document Image Dewarping
Capturing images of documents is one of the easiest and most used methods of recording them.
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.
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.
Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement
RenYang-home/OpenDVC
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CVPR 2020
In our HLVC approach, the hierarchical quality benefits the coding efficiency, since the high quality information facilitates the compression and enhancement of low quality frames at encoder and decoder sides, respectively.
