Multiple Description Coding Based on Convolutional Auto-Encoder

Deep learning, such as convolutional neural networks, has been achieved great success in image processing, computer vision task, and image compression, and has achieved better performance. This paper designs a multiple description coding frameworks based on symmetric convolutional auto-encoder, which can achieve high-quality image reconstruction. First, the image is input into the convolutional auto-encoder, and the extracted features are obtained. Then, the extracted features are encoded by the multiple description coding and split into two descriptions for transmission to the decoder. We can get the side information by the side decoder and the central information by the central decoder. Finally, the side information and the central information are deconvolved by convolutional auto-encoder. The experimental results validate that the proposed scheme outperforms the state-of-the-art methods.

[1]  Michael K. Ng,et al.  Reducing Artifacts in JPEG Decompression Via a Learned Dictionary , 2014, IEEE Transactions on Signal Processing.

[2]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[3]  Yao Zhao,et al.  Multiple Description Video Coding Based on Human Visual System Characteristics , 2014, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Yunjin Chen,et al.  Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1992 .

[6]  Vivek K. Goyal,et al.  Multiple description coding: compression meets the network , 2001, IEEE Signal Process. Mag..

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

[8]  Yuwei Ren,et al.  Perceptual multiple description coding with randomly offset quantizers , 2016, 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA).

[9]  Yao Zhao,et al.  Multiple Description Convolutional Neural Networks for Image Compression , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

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

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

[12]  Karen O. Egiazarian,et al.  Pointwise Shape-Adaptive DCT for High-Quality Denoising and Deblocking of Grayscale and Color Images , 2007, IEEE Transactions on Image Processing.

[13]  Jiro Katto,et al.  Performance Comparison of Convolutional AutoEncoders, Generative Adversarial Networks and Super-Resolution for Image Compression , 2018, CVPR Workshops.

[14]  Majid Rabbani,et al.  An overview of the JPEG 2000 still image compression standard , 2002, Signal Process. Image Commun..

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

[16]  Wen Gao,et al.  CONCOLOR: Constrained Non-Convex Low-Rank Model for Image Deblocking , 2016, IEEE Transactions on Image Processing.

[17]  Michael Fleming,et al.  Generalized multiple description vector quantization , 1999, Proceedings DCC'99 Data Compression Conference (Cat. No. PR00096).

[18]  N. J. A. Sloane,et al.  Multiple-description vector quantization with lattice codebooks: Design and analysis , 2001, IEEE Trans. Inf. Theory.

[19]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[20]  Kannan Ramchandran,et al.  Multiple-description wavelet based image coding , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[21]  Yao Zhao,et al.  Optimized Multiple Description Lattice Vector Quantization for Wavelet Image Coding , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[22]  Kyoung Mu Lee,et al.  Accurate Image Super-Resolution Using Very Deep Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Jaroslaw Domaszewicz,et al.  Design of entropy-constrained multiple-description scalar quantizers , 1994, IEEE Trans. Inf. Theory.

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

[25]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

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

[27]  Vinay A. Vaishampayan,et al.  Design of multiple description scalar quantizers , 1993, IEEE Trans. Inf. Theory.

[28]  Yao Zhao,et al.  Multiple Description Coding With Randomly and Uniformly Offset Quantizers , 2014, IEEE Transactions on Image Processing.

[29]  Jiro Katto,et al.  Deep Convolutional AutoEncoder-based Lossy Image Compression , 2018, 2018 Picture Coding Symposium (PCS).

[30]  Wuzhen Shi,et al.  An End-to-End Compression Framework Based on Convolutional Neural Networks , 2017, 2017 Data Compression Conference (DCC).

[31]  Ce Zhu,et al.  Enhancing Two-Stage Multiple Description Scalar Quantization , 2009, IEEE Signal Processing Letters.

[32]  Hamid Jafarkhani,et al.  Multiple description trellis coded quantization , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[33]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[34]  Thomas Guionnet,et al.  Embedded multiple description coding for progressive image transmission over unreliable channels , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[35]  Volodymyr Turchenko,et al.  Creation of a deep convolutional auto-encoder in Caffe , 2015, 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS).

[36]  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).

[37]  Luca Benini,et al.  CAS-CNN: A deep convolutional neural network for image compression artifact suppression , 2016, 2017 International Joint Conference on Neural Networks (IJCNN).