Variational deep image restoration
This paper presents a new variational inference framework for image restoration and a
convolutional neural network (CNN) structure that can solve the restoration problems …
convolutional neural network (CNN) structure that can solve the restoration problems …
Efficient in-loop filtering based on enhanced deep convolutional neural networks for HEVC
The raw video data can be compressed much by the latest video coding standard, high
efficiency video coding (HEVC). However, the block-based hybrid coding used in HEVC will …
efficiency video coding (HEVC). However, the block-based hybrid coding used in HEVC will …
A stable variant of linex loss SVM for handling noise with reduced hyperparameters
Abstract Support Vector Machine (SVM) primarily uses the hinge loss function with maximum
margin. The boundary instances determine the separating hyperplanes. However, in real …
margin. The boundary instances determine the separating hyperplanes. However, in real …
Extended openmax approach for the classification of radar images with a rejection option
The closed-set assumption in conventional classifiers, such as the Softmax, constrains deep
networks to select an output from the given known classes. However, the classification in a …
networks to select an output from the given known classes. However, the classification in a …
Attention-based dual-scale CNN in-loop filter for versatile video coding
MZ Wang, S Wan, H Gong, MY Ma - IEEE Access, 2019 - ieeexplore.ieee.org
As the upcoming video coding standard, Versatile Video Coding (ie, VVC) achieves up to
30% Bjøntegaard delta bit-rate (BD-rate) reduction compared with High Efficiency Video …
30% Bjøntegaard delta bit-rate (BD-rate) reduction compared with High Efficiency Video …
AGARNet: Adaptively gated JPEG compression artifacts removal network for a wide range quality factor
Most of existing compression artifacts reduction methods focused on the application for low-
quality images and usually assumed a known compression quality factor. However, images …
quality images and usually assumed a known compression quality factor. However, images …
QA-Filter: A QP-adaptive convolutional neural network filter for video coding
Convolutional neural network (CNN)-based filters have achieved great success in video
coding. However, in most previous works, individual models were needed for each …
coding. However, in most previous works, individual models were needed for each …
A global appearance and local coding distortion based fusion framework for CNN based filtering in video coding
In-loop filtering is used in video coding to process the reconstructed frame in order to
remove blocking artifacts. With the development of convolutional neural networks (CNNs) …
remove blocking artifacts. With the development of convolutional neural networks (CNNs) …
Review and evaluation of end-to-end video compression with deep-learning
HM Yasin, SY Ameen - … of Modern Trends in Information and …, 2021 - ieeexplore.ieee.org
Recent years have shown exponential growth in video processing and transfer through the
Internet and other applications. With the restriction on bandwidth, processing, and storage …
Internet and other applications. With the restriction on bandwidth, processing, and storage …
MO-QoE: Video QoE using multi-feature fusion based optimized learning models
The escalating demand for video content and streaming services has made it a predominant
medium of exchanging information in the modern era. Videos are processed, compressed …
medium of exchanging information in the modern era. Videos are processed, compressed …