A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends
In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning
(DL), are widely used for various computer vision tasks such as image classification, object …
(DL), are widely used for various computer vision tasks such as image classification, object …
Leveraging Endo-and Exo-Temporal Regularization for Black-box Video Domain Adaptation
To enable video models to be applied seamlessly across video tasks in different
environments, various Video Unsupervised Domain Adaptation (VUDA) methods have been …
environments, various Video Unsupervised Domain Adaptation (VUDA) methods have been …
WCDANN: A Lightweight CNN Post-Processing Filter for VVC-Based Video Compression
H Zhang, C Jung, D Zou, M Li - IEEE Access, 2023 - ieeexplore.ieee.org
In this paper, we propose a weakly connected dense attention neural network for
compression artifact removal, called WCDANN. WCDANN is a convolutional neural network …
compression artifact removal, called WCDANN. WCDANN is a convolutional neural network …
DAQE: Enhancing the Quality of Compressed Images by Exploiting the Inherent Characteristic of Defocus
Image defocus is inherent in the physics of image formation caused by the optical aberration
of lenses, providing plentiful information on image quality. Unfortunately, existing quality …
of lenses, providing plentiful information on image quality. Unfortunately, existing quality …
λ-Domain Rate Control via Wavelet-Based Residual Neural Network for VVC HDR Intra Coding
High dynamic range (HDR) video offers a more realistic visual experience than standard
dynamic range (SDR) video, while introducing new challenges to both compression and …
dynamic range (SDR) video, while introducing new challenges to both compression and …
Temporal Wavelet Transform-Based Low-Complexity Perceptual Quality Enhancement of Compressed Video
The past few years have witnessed a great success in applying deep learning to enhance
the perceptual quality of compressed video. These methods usually perform frame-by-frame …
the perceptual quality of compressed video. These methods usually perform frame-by-frame …
Enlarged Motion-Aware and Frequency-Aware Network for Compressed Video Artifact Reduction
Making full use of spatial-temporal information is the key factor for removing compressed
video artifacts. Recently, many deep learning-based compression artifact reduction methods …
video artifacts. Recently, many deep learning-based compression artifact reduction methods …
CGVC-T: Contextual Generative Video Compression with Transformers
P Du, Y Liu, N Ling - IEEE Journal on Emerging and Selected …, 2024 - ieeexplore.ieee.org
With the high demands for video streaming, recent years have witnessed a growing interest
in utilizing deep learning for video compression. Most existing neural video compression …
in utilizing deep learning for video compression. Most existing neural video compression …
PixRevive: Latent Feature Diffusion Model for Compressed Video Quality Enhancement
W Wang, M Jing, Y Fan, W Weng - Sensors, 2024 - mdpi.com
In recent years, the rapid prevalence of high-definition video in Internet of Things (IoT)
systems has been directly facilitated by advances in imaging sensor technology. To adapt to …
systems has been directly facilitated by advances in imaging sensor technology. To adapt to …
Versatile Video Coding-Post Processing Feature Fusion: A Post-Processing Convolutional Neural Network with Progressive Feature Fusion for Efficient Video …
T Das, X Liang, K Choi - Applied Sciences (2076-3417), 2024 - search.ebscohost.com
Advanced video codecs such as High Efficiency Video Coding/H. 265 (HEVC) and Versatile
Video Coding/H. 266 (VVC) are vital for streaming high-quality online video content, as they …
Video Coding/H. 266 (VVC) are vital for streaming high-quality online video content, as they …