995 research outputs found
Deep Multiple Description Coding by Learning Scalar Quantization
In this paper, we propose a deep multiple description coding framework, whose
quantizers are adaptively learned via the minimization of multiple description
compressive loss. Firstly, our framework is built upon auto-encoder networks,
which have multiple description multi-scale dilated encoder network and
multiple description decoder networks. Secondly, two entropy estimation
networks are learned to estimate the informative amounts of the quantized
tensors, which can further supervise the learning of multiple description
encoder network to represent the input image delicately. Thirdly, a pair of
scalar quantizers accompanied by two importance-indicator maps is automatically
learned in an end-to-end self-supervised way. Finally, multiple description
structural dissimilarity distance loss is imposed on multiple description
decoded images in pixel domain for diversified multiple description generations
rather than on feature tensors in feature domain, in addition to multiple
description reconstruction loss. Through testing on two commonly used datasets,
it is verified that our method is beyond several state-of-the-art multiple
description coding approaches in terms of coding efficiency.Comment: 8 pages, 4 figures. (DCC 2019: Data Compression Conference). Testing
datasets for "Deep Optimized Multiple Description Image Coding via Scalar
Quantization Learning" can be found in the website of
https://github.com/mdcnn/Deep-Multiple-Description-Codin
Cascaded Reconstruction Network for Compressive image sensing
The theory of compressed sensing (CS) has been successfully applied to image
compression in the past few years, whose traditional iterative reconstruction
algorithm is time-consuming. However, it has been reported deep learning-based
CS reconstruction algorithms could greatly reduce the computational complexity.
In this paper, we propose two efficient structures of cascaded reconstruction
networks corresponding to two different sampling methods in CS process. The
first reconstruction network is a compatibly sampling reconstruction network
(CSRNet), which recovers an image from its compressively sensed measurement
sampled by a traditional random matrix. In CSRNet, deep reconstruction network
module obtains an initial image with acceptable quality, which can be further
improved by residual network module based on convolutional neural network. The
second reconstruction network is adaptively sampling reconstruction network
(ASRNet), by matching automatically sampling module with corresponding residual
reconstruction module. The experimental results have shown that the proposed
two reconstruction networks outperform several state-of-the-art compressive
sensing reconstruction algorithms. Meanwhile, the proposed ASRNet can achieve
more than 1 dB gain, as compared with the CSRNet.Comment: 17 pages,16 figure
Multiple Description Video Coding Using Joint Frame Duplication/Interpolation
Multiple description coding (MDC) is a promising alternative to combatting information loss without any retransmission. In this paper, an effective MD video codec is designed based on temporal pre- and post-processing of video sequences without modifying the actual coding process itself, which makes it compatible with the current standard source or channel codec. For ease of post-processing, motion-compensated interpolation (MCI) based on piecewise uniform motion assumption is adopted to estimate the lost frame in side decoding. Accordingly, to match the post-processing, in the pre-processing joint frame duplication/interpolation is first applied to the original video data before performing odd/even frame splitting, which attempts to make the motion variety in the generated descriptions piecewise uniformly thus achieving better side reconstructed quality based on MCI. The experimental results exhibit better performance of the proposed scheme than some other tested schemes, in both the on-off channel environment and packet loss network
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