Deep Learning in Data Compression


End-to-end auto-encoder like schemes

J. Ballé, V. Laparra, E. P. Simoncelli: End-to-end optimization of nonlinear transform codes for perceptual quality. PCS 2016: 1-5
J. Ballé, V. Laparra, E. P. Simoncelli: End-to-end optimized image compression. ICLR 2017
T. Dumas, A. Roumy, C. Guillemot: Image compression with stochastic winner-take-all auto-encoder. ICASSP 2017: 1512-1516
K. Gregor, Y. LeCun: Learning representations by maximizing compression.
K. Gregor, F. Besse, D. J. Rezende, I. Danihelka, D. Wierstra: Towards conceptual compression. NIPS 2016: 3549-3557
N. Johnston, D. Vincent, D. Minnen, M. Covell, S. Singh, T. Chinen, S. J. Hwang, J. Shor, G. Toderici: Improved lossy image compression with priming and spatially adaptive bit rates for recurrent networks.
A. B. L. Larsen, S. K. Sønderby, H. Larochelle, O. Winther: Autoencoding beyond pixels using a learned similarity metric. ICML 2016: 1558-1566
O. Rippel, L. Bourdev: Real-time adaptive image compression. ICML 2017: 2922-2930
S. Santurkar, D. Budden, N. Shavit: Generative compression.
L. Theis, W. Shi, A. Cunningham, F. Huszár: Lossy image compression with compressive autoencoders. ICLR 2017
G. Toderici, S. M. O'Malley, S. J. Hwang, D. Vincent, D. Minnen, S. Baluja, M. Covell, R. Sukthankar: Variable rate image compression with recurrent neural networks. ICLR 2016
G. Toderici, D. Vincent, N. Johnston, S. J. Hwang, D. Minnen, J. Shor, M. Covell: Full resolution image compression with recurrent neural networks. CVPR 2017: 5306-5314
 

Generative models

A. Dosovitskiy, T. Brox: Generating images with perceptual similarity metrics based on deep networks. NIPS 2016: 658-666
J. Snell, K. Ridgeway, R. Liao, B. D. Roads, M. C. Mozer, R. S. Zemel: Learning to generate images with perceptual similarity metrics.
L. Theis, M. Bethge: Generative image modeling using spatial LSTMs. NIPS 2015: 1927-1935
A. van den Oord, N. Kalchbrenner, K. Kavukcuoglu: Pixel recurrent neural networks. ICML 2016: 1747-1756
A. van den Oord, N. Kalchbrenner, O. Vinyals, L. Espeholt, A. Graves, K. Kavukcuoglu: Conditional image generation with PixelCNN decoders. NIPS 2016: 4790-4798

 

Predictive coding

N. Yan, D. Liu, H. Li, F. Wu: A convolutional neural network approach for half-pel interpolation in video coding. ISCAS 2017: 822-825

 

Down/up-sampling-based coding

F. Jiang, W. Tao, S. Liu, J. Ren, X. Guo, D. Zhao: An end-to-end compression framework based on convolutional neural networks. IEEE Trans. Circuits and Systems for Video Technology
Y. Li, D. Liu, H. Li, L. Li, F. Wu, H. Zhang, H. Yang: Convolutional neural network-based block up-sampling for intra frame coding. IEEE Trans. Circuits and Systems for Video Technology

 

Saliency-based coding

A. Prakash, N. Moran, S. Garber, A. DiLillo, J. Storer: Semantic perceptual image compression using deep convolution networks. DCC 2017: 250-259

 

Colorization-based coding

M. H. Baig, L. Torresani: Multiple hypothesis colorization and its application to image compression. Computer Vision and Image Understanding

 

Post-processing in video coding

Y. Dai, D. Liu, F. Wu: A convolutional neural network approach for post-processing in HEVC intra coding. MMM 2017: 28-39
W.-S. Park, M. Kim: CNN-based in-loop filtering for coding efficiency improvement. IEEE Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) 2016: 1-5
T. Wang, M. Chen, H. Chao: A novel deep learning-based method of improving coding efficiency from the decoder-end for HEVC. DCC 2017: 410-419

 

Mode decision

Z. Liu, X. Yu, Y. Gao, S. Chen, X. Ji, D. Wang: CU partition mode decision for HEVC hardwired intra encoder using convolution neural network. IEEE Trans. Image Processing 25(11): 5088-5103 (2016)

 


 

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