Search Results for author:
Found 15 papers, 9 papers with code
An AFDM-Based Integrated Sensing and Communications
This paper considers an affine frequency division multiplexing (AFDM)-based integrated sensing and communications (ISAC) system, where the AFDM waveform is used to simultaneously carry communications information and sense targets.
Blind VQA on 360° Video via Progressively Learning from Pixels, Frames and Video
When assessing the quality of 360{\textdegree} video, human tends to perceive its quality degradation from the viewport-based spatial distortion of each spherical frame to motion artifact across adjacent frames, ending with the video-level quality score, i. e., a progressive quality assessment paradigm.
Removing Rain in Videos: A Large-scale Database and A Two-stream ConvLSTM Approach
In this paper, we establish a large-scale video database for rain removal (LasVR), which consists of 316 rain videos.
Saliency Prediction on Omnidirectional Images with Generative Adversarial Imitation Learning
According to these findings, our SalGAIL approach applies deep reinforcement learning (DRL) to predict the head fixations of one subject, in which GAIL learns the reward of DRL, rather than the traditional human-designed reward.
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.
DeepVS: A Deep Learning Based Video Saliency Prediction Approach
Hence, an object-to-motion convolutional neural network (OM-CNN) is developed to predict the intra-frame saliency for DeepVS, which is composed of the objectness and motion subnets.
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.
Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model
To fill in the gap between subjective quality and human behavior, this paper proposes a large-scale visual quality assessment (VQA) dataset of omnidirectional video, called VQA-OV, which collects 60 reference sequences and 540 impaired sequences.
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
Predicting Head Movement in Panoramic Video: A Deep Reinforcement Learning Approach
Finally, the experiments validate that our approach is effective in both offline and online prediction of HM positions for panoramic video, and that the learned offline-DHP model can improve the performance of online-DHP.
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.
Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM
We further find from our database that there exists a temporal correlation of human attention with a smooth saliency transition across video frames.
Learning to Predict Saliency on Face Images
For modeling attention on faces and facial features, the proposed method learns the Gaussian mixture model (GMM) distribution from the fixations of eye tracking data as the top-down features for saliency detection of face images.
