20 citations · 113 across the 20 of their papers we have counts for
25 papers
Efficient Convolution and Transformer-Based Network for Video Frame Interpolation
Issa Khalifeh, Luka Murn, Marta Mrak +1
Video frame interpolation is an increasingly important research task with several key industrial applications in the video coding, broadcast and production sectors. Recently, trans…
Query-based Video Summarization with Pseudo Label Supervision
Jia-Hong Huang, Luka Murn, Marta Mrak +1
Existing datasets for manually labelled query-based video summarization are costly and thus small, limiting the performance of supervised deep video summarization models. Self-supe…
PeQuENet: Perceptual Quality Enhancement of Compressed Video with Adaptation- and Attention-based Network
Saiping Zhang, Luis Herranz, Marta Mrak +3
In this paper we propose a generative adversarial network (GAN) framework to enhance the perceptual quality of compressed videos. Our framework includes attention and adaptation to…
Slimmable Video Codec
Zhaocheng Liu, Luis Herranz, Fei Yang +4
Neural video compression has emerged as a novel paradigm combining trainable multilayer neural networks and machine learning, achieving competitive rate-distortion (RD) performance…
Multi-encoder Network for Parameter Reduction of a Kernel-based Interpolation Architecture
Issa Khalifeh, Marc Gorriz Blanch, Ebroul Izquierdo +1
Video frame interpolation involves the synthesis of new frames from existing ones. Convolutional neural networks (CNNs) have been at the forefront of the recent advances in this fi…
Complexity Reduction of Learned In-Loop Filtering in Video Coding
Woody Bayliss, Luka Murn, Ebroul Izquierdo +2
In video coding, in-loop filters are applied on reconstructed video frames to enhance their perceptual quality, before storing the frames for output. Conventional in-loop filters a…
DCNGAN: A Deformable Convolutional-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video
Saiping Zhang, Luis Herranz, Marta Mrak +3
In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to…
Assisting News Media Editors with Cohesive Visual Storylines
Gonçalo Marcelino, David Semedo, André Mourão +3
Creating a cohesive, high-quality, relevant, media story is a challenge that news media editors face on a daily basis. This challenge is aggravated by the flood of highly relevant…
DVC-P: Deep Video Compression with Perceptual Optimizations
Saiping Zhang, Marta Mrak, Luis Herranz +3
Recent years have witnessed the significant development of learning-based video compression methods, which aim at optimizing objective or perceptual quality and bit rates. In this…
Spatial Information Refinement for Chroma Intra Prediction in Video Coding
Chengyi Zou, Shuai Wan, Tiannan Ji +3
Video compression benefits from advanced chroma intra prediction methods, such as the Cross-Component Linear Model (CCLM) which uses linear models to approximate the relationship b…
Improved CNN-based Learning of Interpolation Filters for Low-Complexity Inter Prediction in Video Coding
Luka Murn, Saverio Blasi, Alan F. Smeaton +1
The versatility of recent machine learning approaches makes them ideal for improvement of next generation video compression solutions. Unfortunately, these approaches typically bri…
Towards Transparent Application of Machine Learning in Video Processing
Luka Murn, Marc Gorriz Blanch, Maria Santamaria +2
Machine learning techniques for more efficient video compression and video enhancement have been developed thanks to breakthroughs in deep learning. The new techniques, considered…
Attention-based Stylisation for Exemplar Image Colourisation
Marc Gorriz Blanch, Issa Khalifeh, Alan Smeaton +2
Exemplar-based colourisation aims to add plausible colours to a grayscale image using the guidance of a colour reference image. Most of the existing methods tackle the task as a st…
GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization
Jia-Hong Huang, Luka Murn, Marta Mrak +1
Traditional video summarization methods generate fixed video representations regardless of user interest. Therefore such methods limit users' expectations in content search and exp…
DANICE: Domain adaptation without forgetting in neural image compression
Sudeep Katakol, Luis Herranz, Fei Yang +1
Neural image compression (NIC) is a new coding paradigm where coding capabilities are captured by deep models learned from data. This data-driven nature enables new potential funct…
Attention-Based Neural Networks for Chroma Intra Prediction in Video Coding
Marc Górriz, Saverio Blasi, Alan F. Smeaton +2
Neural networks can be successfully used to improve several modules of advanced video coding schemes. In particular, compression of colour components was shown to greatly benefit f…
Chroma Intra Prediction with attention-based CNN architectures
Marc Górriz, Saverio Blasi, Alan F. Smeaton +2
Neural networks can be used in video coding to improve chroma intra-prediction. In particular, usage of fully-connected networks has enabled better cross-component prediction with…
Interpreting CNN for Low Complexity Learned Sub-pixel Motion Compensation in Video Coding
Luka Murn, Saverio Blasi, Alan F. Smeaton +2
Deep learning has shown great potential in image and video compression tasks. However, it brings bit savings at the cost of significant increases in coding complexity, which limits…
Analytic Simplification of Neural Network based Intra-Prediction Modes for Video Compression
Maria Santamaria, Saverio Blasi, Ebroul Izquierdo +1
With the increasing demand for video content at higher resolutions, it is evermore critical to find ways to limit the complexity of video encoding tasks in order to reduce costs, p…
Estimation of Rate Control Parameters for Video Coding Using CNN
Maria Santamaria, Ebroul Izquierdo, Saverio Blasi +1
Rate-control is essential to ensure efficient video delivery. Typical rate-control algorithms rely on bit allocation strategies, to appropriately distribute bits among frames. As r…
End-to-End Conditional GAN-based Architectures for Image Colourisation
Marc Górriz, Marta Mrak, Alan F. Smeaton +1
In this work recent advances in conditional adversarial networks are investigated to develop an end-to-end architecture based on Convolutional Neural Networks (CNNs) to directly ma…
Fast Inter-Prediction based on Decision Trees for AV1 encoding
Jieon Kim, Saverio Blasi, Andre Seixas Dias +2
The AOMedia Video 1 (AV1) standard can achieve considerable compression efficiency thanks to the usage of many advanced tools and improvements, such as advanced inter-prediction mo…
Decision Trees for Complexity Reduction in Video Compression
Natasha Westland, André Seixas Dias, Marta Mrak
This paper proposes a method for complexity reduction in practical video encoders using multiple decision tree classifiers. The method is demonstrated for the fast implementation o…
A Benchmark of Visual Storytelling in Social Media
Gonçalo Marcelino, David Semedo, André Mourão +3
Media editors in the newsroom are constantly pressed to provide a "like-being there" coverage of live events. Social media provides a disorganised collection of images and videos t…