Introduction

This project mainly dedicated to show how Google Compression, in which they introduced context-based entropy model for image compression using deep learning, realize their work! And their websie lies in tensorflow.github.io/compression, and IN THE VERY BEGINING OF THE END, more descriptions will be shown in my blog💪🏿💪🏿💪🏿!!!

Installation

Note: Precompiled packages are currently only provided for Linux (Python 2.7, 3.3-3.6) and Darwin/Mac OS (Python 2.7, 3.7). To use these packages on Windows, consider using a TensorFlow Docker image and installing tensorflow-compression using pip inside the Docker container.

MacOs/Linux

Using Anaconda
It seems that Anaconda ships its own binary version of TensorFlow which is incompatible with our pip package. It also installs Python 3.7 by default, which we currently don't support on Linux. To solve this, make sure to use Python 3.6 on Linux, and always install TensorFlow via pip rather than conda. For example, this creates an Anaconda environment with Python 3.6 and CUDA libraries, and then installs TensorFlow and tensorflow-compression with GPU support:

conda create --name ENV_NAME python=3.6 cudatoolkit=10.0 cudnn
conda activate ENV_NAME
pip install tensorflow-gpu==1.15

Furthermore
you must first download changed pip package for tensorflow-compression in pipackages
For correctly using my precompiled binary pip package, you must first download the wheel file and then install it with

pip install tensorflow_compression-*.whl

Usage

We recommend importing the library from your Python code as follows:

import tensorflow as tf
import tensorflow_compression as tfc

Citation

Joint Autoregressive and Hierarchical Priors for Learned Image Compression
Variational image compression with a scale hyperprior
End-to-end Optimized Image Compression
Density Modeling of Images using a Generalized Normalization Transformation