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LearnedJPEG

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This repository contains the implementation associated with the paper "Learning to improve image compression without changing the standard decoder".

// repository documentation

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## Learned architectures (models)
### ljpeg.py
This file contains a learned JPEG variant based on a differentiable JPEG substitute in tensorflow. The quantization tables used in the JPEG pipeline are learned through backpropagation.
### ljpeg_attention.py
This file contains the code of out proposed solution: Additionally to the code in ljpeg.py, an attention network is used to employ spatially varying smoothing to the image.
### ljpeg_PIL.py
This is a version of our pipeline that is used for evaluation of the standard JPEG performance. Hence, we use the standard IJG quantization tables instead of learned ones.

### google_baseline.py
This file contains the implementation that follows Talebi et al., 2020 closely, however it only uses MSE and an estimated BPP in the training loss.

### Parameters
The following parameters can be specified for training:
- --checkpoint_dir: Directory where to store (and load) checkpoints. Training can be resumed from previous checkpoints.
- --train_glob: Glob pattern that refers to the list of training images.
- --batchsize: Batch size of training crops
- --patchsize: size of square patches used for training that are obtained by random cropping
- --lambda: Rate-distortion tradeoff parameter
- --last_step: Total number of training steps
- --lr: learning rate used for the Adam optimizer
- --lr_decay: Factor for reducing learning rate after 10000 steps, 1 if no decay intended

Only ljpeg and ljpeg_attention:
- --lpips_weight: Weight on LPIPS term in distortion loss, We use the LPIPS implementation from here: https://github.com/alexlee-gk/lpips-tensorflow
- --total_weight_regularizer: weight on L1 loss of quantization tables

### Run Training

Training can be launched by:

python3 ljpeg_attention.py train --train_glob \<my_train_set/\*.png\>

## Utils
### image_utils.py
Contains image operations such as the DCT and IDCT that are shared among different models.

### arg_parser.py
Contains the argument parser for the models.

### conv_blocks.py
Contains convolutional blocks for editing the image, namely the feedforward ResConvLayer used by Talebi et al. and our VGGAttention network.

## Batch Job Training
### batch_job_attention.sh
Example script that trains an ljpeg_attention architecture for several rate-distortion tradeoffs and stores the checkpoints in a folder structure that is compatible with the evaluation script below. It takes one argument to set the target directory of the training files, for example:

/bin/bash  batch_job_attention.sh \<my_experiments_folder/run_1\>

Inside the batch script, the hyperparameters can be set and are then applied to each training session.

## Evaluation
### evaluate.py
This evaluation script evaluates multiple experiments at once. In particular the expected folder structure is:

\<my_experiments_folder\>
* \<run_1\>
  * lambda_\<01\>
    * train
  * lambda_\<005\>
    * train
  * ...
* \<run_2\>
  * ...

where the train folders contain the checkpoints and the values in brackets \<> may change.
It is important to understand that each folder "lambda_\<...\>" will correspond to one dot on the final plots and each run will be drawn in the same color. The script ignores any run folder that starts with an underscore \_. The results for the JPEG Pillow pipeline will be putt into the "\_output\_PIL" folder. The results from the experiments will go into the "\_output" folder. The script creates pickle files that allows quick re-evaluation. These files store the metrics computed by the evaluation. For recomputing the metrics, the pickle files need to be deleted or moved to an archive folder.

## Parameters
The evaluation script takes the following parameters:
* --batch_dir: directory where all runs are stored, for example \<my_experiments_folder\> above
* --google_dir: optional parameter to compare against the google_baseline, path should point to directory that contains the checkpoints
* --jpeg_type: Choose the learned architecture type to be evaluated, possible values : [ljpeg, ljpeg_attention]
* --test_image_glob: test images, for example "KODAK/*.png"

A full command may look like this:

python3 evaluate.py --batch_dir attention_experiment --jpeg_type ljpeg_attention --test_image_glob "KODAK/*.png" --google_dir google_baseline_lr_1e-5/lambda_005/train

## Requirements

The required packages to run the models are:
* tensorflow 1.15
* tensorflow-compression
* lpips-tensorflow (from github source)
* Pillow
* numpy

LearnedJPEG

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