guhuozhengling / learned_image_compression

Joint autoregressive and hierarchical priors for learned image compression

Joint autoregressive and hierarchical priors for learned image compression

PyTorch implementation of the paper

Minnen, David, Johannes Ballé, and George D. Toderici. "Joint autoregressive and hierarchical priors for learned image compression." Advances in Neural Information Processing Systems. 2018.

Required packages:

torch~=1.3.1
torchvision~=0.4.2
torchsummary~=1.5.1
Pillow~=5.1.0
scipy~=1.4.0

Implemented model summary

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1        [-1, 192, 381, 381]          14,592
               GDN-2        [-1, 192, 381, 381]               0
            Conv2d-3        [-1, 192, 189, 189]         921,792
               GDN-4        [-1, 192, 189, 189]               0
            Conv2d-5          [-1, 192, 93, 93]         921,792
               GDN-6          [-1, 192, 93, 93]               0
            Conv2d-7          [-1, 192, 45, 45]         921,792
           Encoder-8          [-1, 192, 45, 45]               0
            Conv2d-9          [-1, 192, 43, 43]         331,968
           Conv2d-10          [-1, 192, 21, 21]         921,792
           Conv2d-11            [-1, 192, 9, 9]         921,792
     HyperEncoder-12            [-1, 192, 9, 9]               0
     MaskedConv2d-13          [-1, 384, 45, 45]       1,843,584
ContextPrediction-14          [-1, 384, 45, 45]               0
  ConvTranspose2d-15          [-1, 192, 21, 21]         921,792
  ConvTranspose2d-16          [-1, 288, 43, 43]       1,382,688
  ConvTranspose2d-17          [-1, 384, 45, 45]         995,712
     HyperDecoder-18          [-1, 384, 45, 45]               0
           Conv2d-19          [-1, 640, 45, 45]         492,160
           Conv2d-20          [-1, 512, 45, 45]         328,192
           Conv2d-21          [-1, 384, 45, 45]         196,992
EntropyParameters-22          [-1, 384, 45, 45]               0
  ConvTranspose2d-23          [-1, 192, 93, 93]         921,792
              GDN-24          [-1, 192, 93, 93]               0
  ConvTranspose2d-25        [-1, 192, 189, 189]         921,792
              GDN-26        [-1, 192, 189, 189]               0
  ConvTranspose2d-27        [-1, 192, 381, 381]         921,792
              GDN-28        [-1, 192, 381, 381]               0
  ConvTranspose2d-29          [-1, 3, 765, 765]          14,403
          Decoder-30          [-1, 3, 765, 765]               0
================================================================
Total params: 13,896,419
Trainable params: 13,896,419

Preliminary results

Training of the model with a simplified hyperlatent rate show the potential for the model to train:

Example of original/reconstructed image at epoch 0 Example of original/reconstructed image at epoch 1 Example of original/reconstructed image at epoch 2

Future work

• add padding so the model can work with imaging of any resolution
• optimise updates of dynamic Lagrange multipliers vs constants
• add Compress function encodes an image into a bitstream
• add Decompress function that reconstructs

References:

Minnen, David, Johannes Ballé, and George D. Toderici. "Joint autoregressive and hierarchical priors for learned image compression." Advances in Neural Information Processing Systems. 2018.

Ballé, D. Minnen, S. Singh, S. J. Hwang, and N. Johnston, “Variational image compression with a scale hyperprior,” 6th Int. Conf. on Learning Representations, 2018.

Implementation of Masked convolution was taken from: https://www.codeproject.com/Articles/5061271/PixelCNN-in-Autoregressive-Models

Implementation of the GDN non-linearity was from https://github.com/jorge-pessoa/pytorch-gdn/blob/master/pytorch_gdn/__init__.py