Official Code of "Deep Homography for Efficient Stereo Image Compression"[cvpr21oral]

Multi-Reference Entropy Models for Learned Image Compression

🚀✨ Fast Image Optimization Tool for Web and Social Media

Unofficial pytorch implementation of CVPR2022 papar "ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding"

ZJU 2021年春学期多媒体技术课程大作业，用MATLAB实现标准JPEG图像压缩流程

Image Compression using Huffman Coding

Compressy is a web application that allows users to compress and resize images easily. It helps in reducing the file size of images while maintaining reasonable quality.

Implementation & Learning of Compression of Image through use of K-means Clustering Algorithm

Unofficial pytorch implementation of CVPR2021 paper "Checkerboard Context Model for Efficient Learned Image Compression".

Image compression

Optimize images programmatically using C#

Image Compression GUI APP Python: PyQt5

Image compression using vector quantization

An Image Compression Project Using Huffman Coding Algorithm

Efficiently compress and decompress image files with the Image Compression Tool, a MATLAB-based utility designed for simplicity and effectiveness. This tool streamlines the compression process by providing a user-friendly interface for selecting and processing multiple image files in one go.

Code developed from scratch for image compression using k means compresses the image size by representing the image using less number of colors than original image.

Compression of an image is done using K-Means Clustering Algorithm. Image compression is a type of data compression applied to digital images without degrading the quality of the image to an unacceptable level. The reduction in file size allows more images to be stored in a given amount of disk or memory space.

Explore a novel approach to handling pixel data, featuring unique functions for reading, compressing, and decompressing PPM image files.

Autoencoder is a type of neural network where the output layer has the same dimensionality as the input layer. In simpler words, the number of output units in the output layer is equal to the number of input units in the input layer. An autoencoder replicates the data from the input to the output in an unsupervised manner and is therefore someti…