20 citations · 41 across the 9 of their papers we have counts for
6 papers · 1 filter
Learned Block-based Hybrid Image Compression
Yaojun Wu, Xin Li, Zhizheng Zhang +2
Recent works on learned image compression perform encoding and decoding processes in a full-resolution manner, resulting in two problems when deployed for practical applications. F…
FAN: Frequency Aggregation Network for Real Image Super-resolution
Yingxue Pang, Xin Li, Xin Jin +4
Single image super-resolution (SISR) aims to recover the high-resolution (HR) image from its low-resolution (LR) input image. With the development of deep learning, SISR has achiev…
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Pengxu Wei, Hannan Lu, Radu Timofte +68
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This ch…
Learning Disentangled Feature Representation for Hybrid-distorted Image Restoration
Xin Li, Xin Jin, Jianxin Lin +5
Hybrid-distorted image restoration (HD-IR) is dedicated to restore real distorted image that is degraded by multiple distortions. Existing HD-IR approaches usually ignore the inher…
Learned Video Compression with Feature-level Residuals
Runsen Feng, Yaojun Wu, Zongyu Guo +3
In this paper, we present an end-to-end video compression network for P-frame challenge on CLIC. We focus on deep neural network (DNN) based video compression, and improve the curr…
3-D Context Entropy Model for Improved Practical Image Compression
Zongyu Guo, Yaojun Wu, Runsen Feng +2
In this paper, we present our image compression framework designed for CLIC 2020 competition. Our method is based on Variational AutoEncoder (VAE) architecture which is strengthene…