Sep 26, 2024 · In this tutorial we cover a thorough introduction to autoencoders and how to use them for image compression in Keras.

Mar 1, 2017 · We propose a new approach to the problem of optimizing autoencoders for lossy image compression. New media formats, changing hardware technology, as well as diverse requirements and content types create a need for compression algorithms which are more flexible than existing codecs.

Apr 27, 2023 · Autoencoders are neural networks that can learn to compress and reconstruct data, including images. The primary advantage of autoencoders over other compression techniques is their ability to learn the data’s underlying structure, which allows for more efficient compression and reconstruction.

This is an autoencoder with cylic loss and coding parsing loss for image compression and reconstruction. Network backbone is simple 3-layer fully conv (encoder) and symmetrical for decoder. Finally it can achieve 21 mean PSNR on CLIC dataset (CVPR 2019 workshop).

Mar 14, 2025 · The growing volume of high-resolution Whole Slide Images in digital histopathology poses significant storage, transmission, and computational efficiency challenges. Standard compression methods, such as JPEG, reduce file sizes but often fail to preserve fine-grained phenotypic details critical for downstream tasks. In this work, we repurpose autoencoders (AEs) designed for Latent Diffusion ...

The proposed technique entails training an autoencoder on a substantial image dataset and subsequently employing it to compress new images by encoding them into a lower-dimensional representation, which can be later decoded to reconstruct the original image.

May 23, 2023 · Learn how to harness the power of a Deep CNN Autoencoder for image compression and denoising. Discover advanced techniques to enhance images, reduce noise, and optimize storage without compromising quality.

Using Auto Encoders for image compression. This project mainly follows the Tensorflow Autoencoders Tutorial (More info at: https://www.tensorflow.org/tutorials/generative/autoencoder) The notebook contains the steps taken to compress an 28x28 size image to a 7x7 size array which occupies roughly 0.5x space occupied by the original images.

May 31, 2023 · One of these methods is the Variational Autoencoder (VAE), a generative model that learns a lower-dimensional latent space representation of the image. This guide will explore using Variational Autoencoders for image compression, its working principles, and how to implement a VAE model using PyTorch.

Jan 25, 2024 · At their core, AutoEncoders are specialized neural networks designed to cleverly learn how to reconstruct their input data. They achieve this through a process of: Encoding: An AutoEncoder has an...