Paper Info
Title
Mixed-Resolution Image Representation and Compression with Convolutional Neural Networks.
Abstract
In this paper, we propose a end-to-end mixed-resolution image compression framework with convolutional neural networks. Firstly, given one input image, feature description neural network (FDNN) is used to generate a new representation of this image, so that this representation can be more efficiently compressed by standard coder, as compared to the input image. Furthermore, we use post-processing neural network (PPNN) to remove the coding artifacts caused by quantization of codec. Secondly, low-resolution representation is considered under low bit-rate for high efficiency compression in terms of most of bit spent by imageu0027s structures. However, more bits should be assigned to image details in the high-resolution, when most of structures have been kept after compression at the high bit-rate. This comes from that the low-resolution representation canu0027t burden more information than high-resolution representation beyond a certain bit-rate. Finally, to resolve the problem of error back-propagation from the PPNN network to the FDNN network, we introduce a virtual codec neural network to intimate the procedure of standard compression and post-processing. The objective experimental results have demonstrated the proposed method has a large margin improvement, when comparing with several state-of-the-art approaches.
Year
Venue
Field
2018
arXiv: Computer Vision and Pattern Recognition
Compression (physics),Pattern recognition,Convolutional neural network,Computer science,Image representation,Artificial intelligence,Artificial neural network,Quantization (signal processing),Mixed resolution,Codec,Image compression
DocType
Volume
Citations 
Journal
abs/1802.01447
0
PageRank 
References 
Authors
0.34
9
5
Name
Order
Citations
PageRank
Lijun Zhao111717.89
Bai Huihui224341.01
Feng Li382.97
Wang Anhong416938.51
Yao Zhao51926219.11