Generative Compression

Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed. We describe the concept of generative compression, the compression of data using generative models, and suggest that it is a direction worth pursuing to produce more accurate and visually pleasing reconstructions at deeper compression levels for both image and video data. We also show that generative compression is orders- of-magnitude more robust to bit errors (e.g., from noisy channels) than traditional variable-length coding schemes.

[1]  Andrew Brock,et al.  Neural Photo Editing with Introspective Adversarial Networks , 2016, ICLR.

[2]  Daan Wierstra,et al.  Towards Conceptual Compression , 2016, NIPS.

[3]  Leon A. Gatys,et al.  Image Style Transfer Using Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Bolei Zhou,et al.  Learning Deep Features for Scene Recognition using Places Database , 2014, NIPS.

[5]  Leon A. Gatys,et al.  Texture Synthesis Using Convolutional Neural Networks , 2015, NIPS.

[6]  Aaron C. Courville,et al.  Discriminative Regularization for Generative Models , 2016, ArXiv.

[7]  Yann LeCun,et al.  Energy-based Generative Adversarial Network , 2016, ICLR.

[8]  Lucas Theis,et al.  Lossy Image Compression with Compressive Autoencoders , 2017, ICLR.

[9]  Matthias Bethge,et al.  A note on the evaluation of generative models , 2015, ICLR.

[10]  Nir Shavit,et al.  Deep Tensor Convolution on Multicores , 2016, ICML.

[11]  Hugo Larochelle,et al.  The Neural Autoregressive Distribution Estimator , 2011, AISTATS.

[12]  Joseph M. Kahn,et al.  Image transmission over noisy channels using multicarrier modulation , 1997, Signal Process. Image Commun..

[13]  Ali Farhadi,et al.  XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks , 2016, ECCV.

[14]  T. Munich,et al.  Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks , 2008, NIPS.

[15]  Jürgen Schmidhuber,et al.  Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks , 2008, NIPS.

[16]  David Pfau,et al.  Unrolled Generative Adversarial Networks , 2016, ICLR.

[17]  D. Huffman A Method for the Construction of Minimum-Redundancy Codes , 1952 .

[18]  Valero Laparra,et al.  End-to-end Optimized Image Compression , 2016, ICLR.

[19]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[20]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[21]  Lucas Theis,et al.  Amortised MAP Inference for Image Super-resolution , 2016, ICLR.

[22]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[23]  Pritish Narayanan,et al.  Deep Learning with Limited Numerical Precision , 2015, ICML.

[24]  Xiaogang Wang,et al.  Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[25]  Allen Gersho,et al.  Vector Quantization I:Structure and Performance , 1992 .

[26]  Trevor Darrell,et al.  Adversarial Feature Learning , 2016, ICLR.

[27]  Alexei A. Efros,et al.  Generative Visual Manipulation on the Natural Image Manifold , 2016, ECCV.

[28]  Matthias Bethge,et al.  Generative Image Modeling Using Spatial LSTMs , 2015, NIPS.

[29]  Pieter Abbeel,et al.  InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets , 2016, NIPS.

[30]  Valero Laparra,et al.  Perceptual image quality assessment using a normalized Laplacian pyramid , 2016, HVEI.

[31]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[32]  Christian Ledig,et al.  Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Barbara Caputo,et al.  Recognizing human actions: a local SVM approach , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[34]  Aaron C. Courville,et al.  Adversarially Learned Inference , 2016, ICLR.

[35]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[36]  Thomas Brox,et al.  Generating Images with Perceptual Similarity Metrics based on Deep Networks , 2016, NIPS.

[37]  Alex Krizhevsky,et al.  Learning Multiple Layers of Features from Tiny Images , 2009 .

[38]  David Minnen,et al.  Full Resolution Image Compression with Recurrent Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  Daniel Rueckert,et al.  Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[41]  Ran El-Yaniv,et al.  Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations , 2016, J. Mach. Learn. Res..

[42]  Touradj Ebrahimi,et al.  An analytical study of JPEG 2000 functionalities , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[43]  Kristen Grauman,et al.  Fine-Grained Visual Comparisons with Local Learning , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[44]  Ole Winther,et al.  Autoencoding beyond pixels using a learned similarity metric , 2015, ICML.

[45]  Geoffrey E. Hinton,et al.  Deep Boltzmann Machines , 2009, AISTATS.

[46]  Koray Kavukcuoglu,et al.  Pixel Recurrent Neural Networks , 2016, ICML.

[47]  Vijitha Weerackody,et al.  Transmission of JPEG-coded images over wireless channels , 1996, Bell Labs Tech. J..

[48]  David Minnen,et al.  Variable Rate Image Compression with Recurrent Neural Networks , 2015, ICLR.

[49]  Léon Bottou,et al.  Wasserstein GAN , 2017, ArXiv.

[50]  Lubomir D. Bourdev,et al.  Real-Time Adaptive Image Compression , 2017, ICML.

[51]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.