Search Results for author:
Found 24 papers, 19 papers with code
Boosting Neural Representations for Videos with a Conditional Decoder
Implicit neural representations (INRs) have emerged as a promising approach for video storage and processing, showing remarkable versatility across various video tasks.
Detecting Reddit Users with Depression Using a Hybrid Neural Network SBERT-CNN
In this work, we propose a hybrid deep learning model which combines a pretrained sentence BERT (SBERT) and convolutional neural network (CNN) to detect individuals with depression with their Reddit posts.
Advancing Learned Video Compression with In-loop Frame Prediction
In this paper, we propose an Advanced Learned Video Compression (ALVC) approach with the in-loop frame prediction module, which is able to effectively predict the target frame from the previously compressed frames, without consuming any bit-rate.
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
The homepage of this challenge is at https://github. com/RenYang-home/AIM22_CompressSR.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
Implicit Neural Representations for Image Compression
Recently Implicit Neural Representations (INRs) gained attention as a novel and effective representation for various data types.
Perceptual Learned Video Compression with Recurrent Conditional GAN
This paper proposes a Perceptual Learned Video Compression (PLVC) approach with recurrent conditional GAN.
R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network
Solving a series of degradation of low-light images can effectively improve the visual quality of images and the performance of high-level visual tasks.
Deep Homography for Efficient Stereo Image Compression
To fully explore the mutual information across two stereo images, we use a deep regression model to estimate the homography matrix, i. e., H matrix.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Study
In our study, we analyze the proposed methods of the challenge and several methods in previous works on the proposed LDV dataset.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
Learning to Improve Image Compression without Changing the Standard Decoder
Therefore, we propose learning to improve the encoding performance with the standard decoder.
Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement
In our HLVC approach, the hierarchical quality benefits the coding efficiency, since the high quality information facilitates the compression and enhancement of low quality frames at encoder and decoder sides, respectively.
Wavelet Domain Style Transfer for an Effective Perception-distortion Tradeoff in Single Image Super-Resolution
In this paper, we propose a novel method based on wavelet domain style transfer (WDST), which achieves a better PD tradeoff than the GAN based methods.
Quality-Gated Convolutional LSTM for Enhancing Compressed Video
The past decade has witnessed great success in applying deep learning to enhance the quality of compressed video.
A DenseNet Based Approach for Multi-Frame In-Loop Filter in HEVC
High efficiency video coding (HEVC) has brought outperforming efficiency for video compression.
MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video
Finally, experiments validate the effectiveness and generalization ability of our MFQE approach in advancing the state-of-the-art quality enhancement of compressed video.
Ranked #5 on
Video Enhancement
on MFQE v2
Understanding and Predicting the Memorability of Outdoor Natural Scenes
In particular, we find that the high-level feature of scene category is rather correlated with outdoor natural scene memorability, and the deep features learnt by deep neural network (DNN) are also effective in predicting the memorability scores.
What Makes Natural Scene Memorable?
Recent studies on image memorability have shed light on the visual features that make generic images, object images or face photographs memorable.
Multi-Frame Quality Enhancement for Compressed Video
In this paper, we investigate that heavy quality fluctuation exists across compressed video frames, and thus low quality frames can be enhanced using the neighboring high quality frames, seen as Multi-Frame Quality Enhancement (MFQE).
Ranked #6 on
Video Enhancement
on MFQE v2
Enhancing Quality for HEVC Compressed Videos
Our experimental results validate that our QE-CNN method is effective in enhancing quality for both I and P frames of HEVC videos.
Multimedia
Reducing Complexity of HEVC: A Deep Learning Approach
Therefore, this paper proposes a deep learning approach to predict the CU partition for reducing the HEVC complexity at both intra- and inter-modes, which is based on convolutional neural network (CNN) and long- and short-term memory (LSTM) network.
