7 citations · 9 across the 4 of their papers we have counts for
7 papers
Deep Optimized Multiple Description Image Coding via Scalar Quantization Learning
Lijun Zhao, Huihui Bai, Anhong Wang +1
In this paper, we introduce a deep multiple description coding (MDC) framework optimized by minimizing multiple description (MD) compressive loss. First, MD multi-scale-dilated enc…
Concurrently Extrapolating and Interpolating Networks for Continuous Model Generation
Lijun Zhao, Jinjing Zhang, Fan Zhang +3
Most deep image smoothing operators are always trained repetitively when different explicit structure-texture pairs are employed as label images for each algorithm configured with…
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…