mc-2-lab Goto Github PK
Name: MC-2 Lab
Type: Organization
Bio: Multimedia Computing Towards Communications (MC-2) Lab, Beihang University, China
Location: China
Blog: http://www.buaamc2.net/
Name: MC-2 Lab
Type: Organization
Bio: Multimedia Computing Towards Communications (MC-2) Lab, Beihang University, China
Location: China
Blog: http://www.buaamc2.net/
Official repository of "Deep Coupled Feedback Network for Joint Exposure Fusion and Image Super-Resolution"
A large-scale database for CU partition of HEVC (CPH), at both intra- and inter-modes.
A large-scale database for QTMT-based CU partition of VVC.
Official repository of "DeepMIH: Deep Invertible Network for Multiple Image Hiding", TPAMI 2022.
Predicting Head Movement in Panoramic Video: A Deep Reinforcement Learning Approach. TPAMI 2018.
Source codes for accelerate the ETH-CNN model. A related journal paper is published on IEEE TIP 2020.
Official Code of "Deep Homography for Efficient Stereo Image Compression"[cvpr21oral]
Source programs to test the deep-learning-based complexity reduction approach for HEVC, at both intra- and inter-modes.
A large-scale database for HEVC in-loop filter (HIF).
Official PyTorch implementation of "HiNet: Deep Image Hiding by Invertible Network" (ICCV 2021)
www.buaamc2.net
Official repository of "MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video", TPAMI 2019.
Source codes for training and evaluating the multi-frame in-loop filter (MIF) approach.
Official PyTorch implements for Multi-level Wavelet-Based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video (ECCV, 2020) and MW-GAN+ for Perceptual Quality Enhancement on Compressed Video (IEEE TCSVT).
Official repository of "Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images", ECCV 2020.
ICME'19: Removing Rain in Videos: A Large-scale Database and A Two-stream ConvLSTM Approach
Code for "Learning to Predict Salient Faces: A Novel Visual-Audio Saliency Model", ECCV 2020
Our method and experience of wining the NTIRE22 challenge on video quality enhancement
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