Contribute to ganeshiitj/Quantized-Auto-Encoder-Based-Image-Compression development by creating an account on GitHub. ... Load the pre-trained auto-encoder model; Compress each image in the images ...

In this article, we are going to see how we can remove noise from the image data using an encoder-decoder model. We will go through two approaches of denoising with encoder-decoder, one with dense ...

This repository contains the implementation of an Encoder-Decoder model for image denoising using the MNIST dataset. The model is trained with varying levels of Gaussian noise and bottleneck ...

Recently, neural image compression (NIC) has made remarkable progress. Two key parts of NIC are the encoder-decoder and the entropy model. For the encoder-decoder, a larger effective receptive field ...

Since both encoder and decoder models are learned ... Using this approach, we introduced a new concept called DANICE - Domain Adaptation in Neural Image Compression.

AE/AD represent arithmetic encoder and decoder, respectively. C m and EP stand for the context model and entropy parameters, respectively. Q represents ... the noisy image is first compressed using ...

Image compression, which aims to represent an image with less storage space, is a classical problem in image processing. Recently, by training an encoder-quantizer-decoder network, deep convolutional ...