This is the source code to our compression model in "Learning-Based Practical Light Field Image Compression Using A Disparity-Aware Model", M. Singh, R. M. Rameshan, PCS'21.

https://arxiv.org/abs/2106.11558

• tensorflow-gpu 2.4
• tensorflow-probability 0.12.1
• tensorflow-compression 2.0b2
• tensorflow-addons 0.12

A Singularity container with all required packages is available, contact at [email protected].

Training data should be placed in a folder, defaults to './train' and can be changed via command line arguments. For training, we use 64x64 patches of the data. Each folder in './train' contains 8 views belonging to the same row, each having spatial dimension 64x64x3.

train
  - folder1
    - 1_1.png
    - 1_2.png
      .
      .
    - 1_7.png
    - 1_8.png
  - folder2
    - 2_1.png
    - 2_2.png
      .
      .
    - 2_7.png
    - 2_8.png

The test images can be full sized, default directory is './test'. Each folder in './test' contains 64 views, or the center 8x8 views of the entire 4D light field. The views should be named as i_j.png, where i is given by the row and j by the column of the view with respect to entire 4D light field.

For training:

python lfdaae.py train

Additional arguments can be added to change default batch size, checkpoint directory, etc. To see list of available commands:

python lfdaae.py -h
python lfdaae.py train -h

To compress a light field image:

python lfdaae.py compress './test/examplefolder' './output'

This will read all 64 views in 'examplefolder' and compress each row and save the bitstreams as a .tfci file in the './output' folder. Reconstructions from the bistream will also be saved.

To decompress a bitstream:

python lfdaae.py decompress './output/ex.tfci' './output'

• Update code for compatibility with TF2.5 and TFC2.2
• Add video links

Parts of the code here are borrowed from the example codes in the Tensorflow Compression repository.

If you find it useful in your research, kindly cite our PCS'21 paper.

@misc{singh2021learningbased,
      title={Learning-Based Practical Light Field Image Compression Using A Disparity-Aware Model}, 
      author={Mohana Singh and Renu M. Rameshan},
      year={2021},
      eprint={2106.11558},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}