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pro vyhledávání: '"Izquierdo, Ebroul"'
This paper presents a novel joint neural networks approach to address the challenging one-shot object recognition and detection tasks. Inspired by Siamese neural networks and state-of-art multi-box detection approaches, the joint neural networks are
Externí odkaz:
http://arxiv.org/abs/2408.00701
Video frame interpolation is an increasingly important research task with several key industrial applications in the video coding, broadcast and production sectors. Recently, transformers have been introduced to the field resulting in substantial per
Externí odkaz:
http://arxiv.org/abs/2307.06443
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 field. One popular CNN-based approach involves the application of genera
Externí odkaz:
http://arxiv.org/abs/2205.06723
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 are obtained by hand-crafted methods. Recently, learned filters based o
Externí odkaz:
http://arxiv.org/abs/2203.08650
Publikováno v:
2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 6-10 July 2020, London, United Kingdom
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, power consumption and environmental impact of video services. In the la
Externí odkaz:
http://arxiv.org/abs/2004.11056
Publikováno v:
IEEE International Conference on Visual Communications and Image Processing (VCIP 2018), Taichung, Taiwan, 9 -12 December 2018
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 reference frames are essential for exploiting temporal redundancies, in
Externí odkaz:
http://arxiv.org/abs/2003.06315
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 modes. However, these come at the cost of high computational complexity
Externí odkaz:
http://arxiv.org/abs/1908.11166
Autor:
Toutounchi, Farzad, Izquierdo, Ebroul
In this paper, we present a novel deep learning-based approach for still image super-resolution, that unlike the mainstream models does not rely solely on the input low resolution image for high quality upsampling, and takes advantage of a set of art
Externí odkaz:
http://arxiv.org/abs/1812.06023
Akademický článek
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Autor:
Giannopoulos, Michalis, Tsagkatakis, Grigorios, Blasi, Saverio, Toutounchi, Farzad, Mouchtaris, Athanasios, Tsakalides, Panagiotis, Mrak, Marta, Izquierdo, Ebroul
Video Quality Assessment (VQA) is a very challenging task due to its highly subjective nature. Moreover, many factors influence VQA. Compression of video content, while necessary for minimising transmission and storage requirements, introduces distor
Externí odkaz:
http://arxiv.org/abs/1809.10117