44 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.
LibrariesUse these libraries to find MS-SSIM models and implementations
Most implemented papers
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.
Variational image compression with a scale hyperprior
We describe an end-to-end trainable model for image compression based on variational autoencoders.
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.
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.
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.
Joint Autoregressive and Hierarchical Priors for Learned Image Compression
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.
Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules
In this paper, we explore the remaining redundancy of recent learned compression algorithms.
Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement
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.
CompressAI: a PyTorch library and evaluation platform for end-to-end compression research
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.
DSSLIC: Deep Semantic Segmentation-based Layered Image Compression
A compact representation of the input image is also generated and encoded as the first enhancement layer.