In this paper, we propose a novel variable-rate learned image compression framework with a conditional autoencoder. Previous learning-based image compression methods mostly require training separate ...

We show that our patch-based learned image compression with transformers obtain 0.75dB improvement in PSNR at 0.15bpp than the prior variable-rate compression work on the Kodak dataset. When using the ...

In this paper, we study high fidelity variable rate compression framework. Both conventional and learned codecs in prior works are optimized for objective quality commonly measured by PSNR or SSIM, ...

This repository is the implementation of "Variable-Rate Deep Image Compression through Spatially-Adaptive Feature Transform" (ICCV 2021). Our code is based on CompressAI.. Abstract: We propose a ...

This repository contains the official implementation of our paper: Multi-Scale Invertible Neural Network for Wide-Range Variable-Rate Learned Image Compression. Autoencoder-based structures have ...

Mounds of data and layers of field maps are common elements of precision farming systems. A new web-based tool simplifies variable-rate technologies, combining historical field satellite imagery with ...