Learning for Video Compression With Recurrent Auto-Encoder and Recurrent Probability Model
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Radu Timofte | Luc Van Gool | Ren Yang | Fabian Mentzer | R. Timofte | L. Van Gool | Ren Yang | Fabian Mentzer
[1] Marko Viitanen,et al. UVG dataset: 50/120fps 4K sequences for video codec analysis and development , 2020, MMSys.
[2] David Minnen,et al. Joint Autoregressive and Hierarchical Priors for Learned Image Compression , 2018, NeurIPS.
[3] Xiaoyun Zhang,et al. DVC: An End-To-End Deep Video Compression Framework , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[4] J. Glosup. Handbook of the Logistic Distribution , 1993 .
[5] Wenhan Yang,et al. Coarse-to-Fine Hyper-Prior Modeling for Learned Image Compression , 2020, AAAI.
[6] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[7] F. Bossen,et al. Common test conditions and software reference configurations , 2010 .
[8] Feng Wu,et al. Learning for Video Compression , 2018, IEEE Transactions on Circuits and Systems for Video Technology.
[9] Gary J. Sullivan,et al. Overview of the High Efficiency Video Coding (HEVC) Standard , 2012, IEEE Transactions on Circuits and Systems for Video Technology.
[10] Joonki Paik,et al. Dual Autoencoder Network for Retinex-Based Low-Light Image Enhancement , 2018, IEEE Access.
[11] Glen G. Langdon,et al. An Introduction to Arithmetic Coding , 1984, IBM J. Res. Dev..
[12] Trevor Darrell,et al. Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Michael J. Black,et al. Optical Flow Estimation Using a Spatial Pyramid Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Luc Van Gool,et al. Conditional Probability Models for Deep Image Compression , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[15] Jun Yu,et al. Coupled Deep Autoencoder for Single Image Super-Resolution , 2017, IEEE Transactions on Cybernetics.
[16] Luca Benini,et al. Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations , 2017, NIPS.
[17] Valero Laparra,et al. End-to-end Optimized Image Compression , 2016, ICLR.
[18] Zhan Ma,et al. Learned Video Compression via Joint Spatial-Temporal Correlation Exploration , 2019, AAAI.
[19] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[20] Lukás Burget,et al. Extensions of recurrent neural network language model , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[21] G. Bjontegaard,et al. Calculation of Average PSNR Differences between RD-curves , 2001 .
[22] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[23] Lovedeep Gondara,et al. Medical Image Denoising Using Convolutional Denoising Autoencoders , 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).
[24] Jiajun Wu,et al. Video Enhancement with Task-Oriented Flow , 2018, International Journal of Computer Vision.
[25] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[26] Luc Van Gool,et al. OpenDVC: An Open Source Implementation of the DVC Video Compression Method , 2020, ArXiv.
[27] Ajay Luthra,et al. Overview of the H.264/AVC video coding standard , 2003, IEEE Trans. Circuits Syst. Video Technol..
[28] Jooyoung Lee,et al. Context-adaptive Entropy Model for End-to-end Optimized Image Compression , 2018, ICLR.
[29] Jiro Katto,et al. Learning Image and Video Compression Through Spatial-Temporal Energy Compaction , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Yoshua Bengio,et al. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.
[31] Dit-Yan Yeung,et al. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.
[32] Dong Liu,et al. One-for-All: Grouped Variation Network-Based Fractional Interpolation in Video Coding , 2019, IEEE Transactions on Image Processing.
[33] Geoffrey E. Hinton,et al. Autoencoders, Minimum Description Length and Helmholtz Free Energy , 1993, NIPS.
[34] Xiaoming Tao,et al. A Deep Learning Approach for Multi-Frame In-Loop Filter of HEVC , 2019, IEEE Transactions on Image Processing.
[35] Chao-Yuan Wu,et al. Video Compression through Image Interpolation , 2018, ECCV.
[36] Soumik Sarkar,et al. LLNet: A deep autoencoder approach to natural low-light image enhancement , 2015, Pattern Recognit..
[37] Dacheng Tao,et al. Non-Local Auto-Encoder With Collaborative Stabilization for Image Restoration , 2016, IEEE Transactions on Image Processing.
[38] L. Gool,et al. Learning for Video Compression With Hierarchical Quality and Recurrent Enhancement , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Didier J. Le Gall,et al. The MPEG video compression algorithm , 1992, Signal Process. Image Commun..
[40] Nitish Srivastava,et al. Unsupervised Learning of Video Representations using LSTMs , 2015, ICML.
[41] Ivan V. Bajic,et al. Deep Frame Prediction for Video Coding , 2018, IEEE Transactions on Circuits and Systems for Video Technology.
[42] Abdelaziz Djelouah,et al. Neural Inter-Frame Compression for Video Coding , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[43] Lucas Theis,et al. Lossy Image Compression with Compressive Autoencoders , 2017, ICLR.
[44] Alex Graves,et al. Conditional Image Generation with PixelCNN Decoders , 2016, NIPS.
[45] David Minnen,et al. Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[46] Eirikur Agustsson,et al. Scale-Space Flow for End-to-End Optimized Video Compression , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Zulin Wang,et al. Reducing Complexity of HEVC: A Deep Learning Approach , 2017, IEEE Transactions on Image Processing.
[48] Samy Bengio,et al. Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[49] Zhan Ma,et al. DeepCoder: A deep neural network based video compression , 2017, 2017 IEEE Visual Communications and Image Processing (VCIP).
[50] Lukás Burget,et al. Recurrent neural network based language model , 2010, INTERSPEECH.
[51] Yonghui Wu,et al. Exploring the Limits of Language Modeling , 2016, ArXiv.
[52] Fei-Fei Li,et al. Visualizing and Understanding Recurrent Networks , 2015, ArXiv.
[53] Taco S. Cohen,et al. Video Compression With Rate-Distortion Autoencoders , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[54] David Minnen,et al. Variable Rate Image Compression with Recurrent Neural Networks , 2015, ICLR.
[55] Ping Wang,et al. MCL-JCV: A JND-based H.264/AVC video quality assessment dataset , 2016, 2016 IEEE International Conference on Image Processing (ICIP).
[56] David Minnen,et al. Full Resolution Image Compression with Recurrent Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[57] Kyunghyun Cho,et al. Simple Sparsification Improves Sparse Denoising Autoencoders in Denoising Highly Corrupted Images , 2013, ICML.
[58] Dong Liu,et al. A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding , 2016, MMM.
[59] David Zhang,et al. Learning Convolutional Networks for Content-Weighted Image Compression , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[60] David Minnen,et al. Variational image compression with a scale hyperprior , 2018, ICLR.
[61] Touradj Ebrahimi,et al. The JPEG 2000 still image compression standard , 2001, IEEE Signal Process. Mag..