4,394 research outputs found
Unequal Error Protected JPEG 2000 Broadcast Scheme with Progressive Fountain Codes
This paper proposes a novel scheme, based on progressive fountain codes, for
broadcasting JPEG 2000 multimedia. In such a broadcast scheme, progressive
resolution levels of images/video have been unequally protected when
transmitted using the proposed progressive fountain codes. With progressive
fountain codes applied in the broadcast scheme, the resolutions of images (JPEG
2000) or videos (MJPEG 2000) received by different users can be automatically
adaptive to their channel qualities, i.e. the users with good channel qualities
are possible to receive the high resolution images/vedio while the users with
bad channel qualities may receive low resolution images/vedio. Finally, the
performance of the proposed scheme is evaluated with the MJPEG 2000 broadcast
prototype
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. However, the existing approaches aim
at quality enhancement on a single frame, or only using fixed neighboring
frames. Thus they fail to take full advantage of the inter-frame correlation in
the video. This paper proposes the Quality-Gated Convolutional Long Short-Term
Memory (QG-ConvLSTM) network with bi-directional recurrent structure to fully
exploit the advantageous information in a large range of frames. More
importantly, due to the obvious quality fluctuation among compressed frames,
higher quality frames can provide more useful information for other frames to
enhance quality. Therefore, we propose learning the "forget" and "input" gates
in the ConvLSTM cell from quality-related features. As such, the frames with
various quality contribute to the memory in ConvLSTM with different importance,
making the information of each frame reasonably and adequately used. Finally,
the experiments validate the effectiveness of our QG-ConvLSTM approach in
advancing the state-of-the-art quality enhancement of compressed video, and the
ablation study shows that our QG-ConvLSTM approach is learnt to make a
trade-off between quality and correlation when leveraging multi-frame
information. The project page: https://github.com/ryangchn/QG-ConvLSTM.git.Comment: Accepted to IEEE International Conference on Multimedia and Expo
(ICME) 201
Rateless Codes with Progressive Recovery for Layered Multimedia Delivery
This paper proposes a novel approach, based on unequal error protection, to
enhance rateless codes with progressive recovery for layered multimedia
delivery. With a parallel encoding structure, the proposed Progressive Rateless
codes (PRC) assign unequal redundancy to each layer in accordance with their
importance. Each output symbol contains information from all layers, and thus
the stream layers can be recovered progressively at the expected received
ratios of output symbols. Furthermore, the dependency between layers is
naturally considered. The performance of the PRC is evaluated and compared with
some related UEP approaches. Results show that our PRC approach provides better
recovery performance with lower overhead both theoretically and numerically
Multi-Frame Quality Enhancement for Compressed Video
The past few years have witnessed great success in applying deep learning to
enhance the quality of compressed image/video. The existing approaches mainly
focus on enhancing the quality of a single frame, ignoring the similarity
between consecutive frames. 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). Accordingly, this paper proposes an MFQE approach
for compressed video, as a first attempt in this direction. In our approach, we
firstly develop a Support Vector Machine (SVM) based detector to locate Peak
Quality Frames (PQFs) in compressed video. Then, a novel Multi-Frame
Convolutional Neural Network (MF-CNN) is designed to enhance the quality of
compressed video, in which the non-PQF and its nearest two PQFs are as the
input. The MF-CNN compensates motion between the non-PQF and PQFs through the
Motion Compensation subnet (MC-subnet). Subsequently, the Quality Enhancement
subnet (QE-subnet) reduces compression artifacts of the non-PQF with the help
of its nearest PQFs. Finally, the experiments validate the effectiveness and
generality of our MFQE approach in advancing the state-of-the-art quality
enhancement of compressed video. The code of our MFQE approach is available at
https://github.com/ryangBUAA/MFQE.gitComment: to appear in CVPR 201
Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model
Omnidirectional video enables spherical stimuli with the viewing range. Meanwhile, only the viewport region of omnidirectional
video can be seen by the observer through head movement (HM), and an even
smaller region within the viewport can be clearly perceived through eye
movement (EM). Thus, the subjective quality of omnidirectional video may be
correlated with HM and EM of human behavior. 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. Our VQA-OV dataset
provides not only the subjective quality scores of sequences but also the HM
and EM data of subjects. By mining our dataset, we find that the subjective
quality of omnidirectional video is indeed related to HM and EM. Hence, we
develop a deep learning model, which embeds HM and EM, for objective VQA on
omnidirectional video. Experimental results show that our model significantly
improves the state-of-the-art performance of VQA on omnidirectional video.Comment: Accepted by ACM MM 201
Mega-Reward: Achieving Human-Level Play without Extrinsic Rewards
Intrinsic rewards were introduced to simulate how human intelligence works;
they are usually evaluated by intrinsically-motivated play, i.e., playing games
without extrinsic rewards but evaluated with extrinsic rewards. However, none
of the existing intrinsic reward approaches can achieve human-level performance
under this very challenging setting of intrinsically-motivated play. In this
work, we propose a novel megalomania-driven intrinsic reward (called
mega-reward), which, to our knowledge, is the first approach that achieves
human-level performance in intrinsically-motivated play. Intuitively,
mega-reward comes from the observation that infants' intelligence develops when
they try to gain more control on entities in an environment; therefore,
mega-reward aims to maximize the control capabilities of agents on given
entities in a given environment. To formalize mega-reward, a relational
transition model is proposed to bridge the gaps between direct and latent
control. Experimental studies show that mega-reward (i) can greatly outperform
all state-of-the-art intrinsic reward approaches, (ii) generally achieves the
same level of performance as Ex-PPO and professional human-level scores, and
(iii) has also a superior performance when it is incorporated with extrinsic
rewards
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