57 citations · 132 across the 25 of their papers we have counts for
5 papers · 1 filter
Deep Multiple Description Coding by Learning Scalar Quantization
Lijun Zhao, Huihui Bai, Anhong Wang +1
In this paper, we propose a deep multiple description coding framework, whose quantizers are adaptively learned via the minimization of multiple description compressive loss. First…
Virtual Codec Supervised Re-Sampling Network for Image Compression
Lijun Zhao, Huihui Bai, Anhong Wang +1
In this paper, we propose an image re-sampling compression method by learning virtual codec network (VCN) to resolve the non-differentiable problem of quantization function for ima…
Mixed-Resolution Image Representation and Compression with Convolutional Neural Networks
Lijun Zhao, Huihui Bai, Feng Li +2
In this paper, we propose an end-to-end mixed-resolution image compression framework with convolutional neural networks. Firstly, given one input image, feature description neural…
Learning a Virtual Codec Based on Deep Convolutional Neural Network to Compress Image
Lijun Zhao, Huihui Bai, Anhong Wang +1
Although deep convolutional neural network has been proved to efficiently eliminate coding artifacts caused by the coarse quantization of traditional codec, it's difficult to train…
Multiple Description Convolutional Neural Networks for Image Compression
Lijun Zhao, Huihui Bai, Anhong Wang +1
Multiple description coding (MDC) is able to stably transmit the signal in the un-reliable and non-prioritized networks, which has been broadly studied for several decades. However…