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
PO-ELIC: Perception-Oriented Efficient Learned Image Coding
beiluo97/hflic
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In the past years, learned image compression (LIC) has achieved remarkable performance.
ADC-Net: An Open-Source Deep Learning Network for Automated Dispersion Compensation in Optical Coherence Tomography
dleninja/adcnet
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Chromatic dispersion is a common problem to degrade the system resolution in optical coherence tomography (OCT).
End-to-End Rate-Distortion Optimized Learned Hierarchical Bi-Directional Video Compression
KUIS-AI-Tekalp-Research-Group/video-compression
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Conventional video compression (VC) methods are based on motion compensated transform coding, and the steps of motion estimation, mode and quantization parameter selection, and entropy coding are optimized individually due to the combinatorial nature of the end-to-end optimization problem.
Temporal Context Mining for Learned Video Compression
microsoft/dcvc
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From the stored propagated features, we propose to learn multi-scale temporal contexts, and re-fill the learned temporal contexts into the modules of our compression scheme, including the contextual encoder-decoder, the frame generator, and the temporal context encoder.
Unified Signal Compression Using a GAN with Iterative Latent Representation Optimization
bowenl0218/bpgan-signal-compression
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We propose a unified signal compression framework that uses a generative adversarial network (GAN) to compress heterogeneous signals.
Interpolation variable rate image compression
Compression standards have been used to reduce the cost of image storage and transmission for decades.
Learned Image Compression with Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modules
fengyurenpingsheng/Learned-image-compression-with-GLLMM
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However, due to the vast diversity of images, it is not optimal to use one model for all images, even different regions within one image.
Learning-Based Practical Light Field Image Compression Using A Disparity-Aware Model
moha23/LF-DAAE
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Hence, there is a compelling need for efficient compression of light field images.
Deep learning-based bias transfer for overcoming laboratory differences of microscopic images
imsb-uke/bias-transfer-microscopy
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The automated analysis of medical images is currently limited by technical and biological noise and bias.
Spatiotemporal Entropy Model is All You Need for Learned Video Compression
The framework of dominant learned video compression methods is usually composed of motion prediction modules as well as motion vector and residual image compression modules, suffering from its complex structure and error propagation problem.
