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pro vyhledávání: '"Bull, David A."'
Recent advances in implicit neural representation (INR)-based video coding have demonstrated its potential to compete with both conventional and other learning-based approaches. With INR methods, a neural network is trained to overfit a video sequenc
Externí odkaz:
http://arxiv.org/abs/2409.07414
The influence of atmospheric turbulence on acquired imagery makes image interpretation and scene analysis extremely difficult and reduces the effectiveness of conventional approaches for classifying and tracking objects of interest in the scene. Rest
Externí odkaz:
http://arxiv.org/abs/2409.14587
Neural video compression has recently demonstrated significant potential to compete with conventional video codecs in terms of rate-quality performance. These learned video codecs are however associated with various issues related to decoding complex
Externí odkaz:
http://arxiv.org/abs/2409.00953
In recent years, user-generated content (UGC) has become one of the major video types consumed via streaming networks. Numerous research contributions have focused on assessing its visual quality through subjective tests and objective modeling. In mo
Externí odkaz:
http://arxiv.org/abs/2408.07171
Recent advances in video compression have seen significant coding performance improvements with the development of new standards and learning-based video codecs. However, most of these works focus on application scenarios that allow a certain amount
Externí odkaz:
http://arxiv.org/abs/2408.05042
Deep learning is now playing an important role in enhancing the performance of conventional hybrid video codecs. These learning-based methods typically require diverse and representative training material for optimization in order to achieve model ge
Externí odkaz:
http://arxiv.org/abs/2408.03265
With the rapid growth of User-Generated Content (UGC) exchanged between users and sharing platforms, the need for video quality assessment in the wild has emerged. UGC is mostly acquired using consumer devices and undergoes multiple rounds of compres
Externí odkaz:
http://arxiv.org/abs/2407.11496
Autor:
Lin, Ruirui, Anantrasirichai, Nantheera, Huang, Guoxi, Lin, Joanne, Sun, Qi, Malyugina, Alexandra, Bull, David R
Low-light videos often exhibit spatiotemporal incoherent noise, compromising visibility and performance in computer vision applications. One significant challenge in enhancing such content using deep learning is the scarcity of training data. This pa
Externí odkaz:
http://arxiv.org/abs/2407.03535
In many real-world scenarios, recorded videos suffer from accidental focus blur, and while video deblurring methods exist, most specifically target motion blur. This paper introduces a framework optimised for the joint task of focal deblurring (refoc
Externí odkaz:
http://arxiv.org/abs/2407.01230
Visual artifacts are often introduced into streamed video content, due to prevailing conditions during content production and/or delivery. Since these can degrade the quality of the user's experience, it is important to automatically and accurately d
Externí odkaz:
http://arxiv.org/abs/2406.00212