no code implementations • 19 Dec 2023 • Zihao Qi, Chen Feng, Duolikun Danier, Fan Zhang, Xiaozhong Xu, Shan Liu, David Bull
In this work, we observe that existing full-/no-reference quality metrics fail to accurately predict the perceptual quality difference between transcoded UGC content and the corresponding unpristine references.
no code implementations • 14 Dec 2023 • Chen Feng, Duolikun Danier, Haoran Wang, Fan Zhang, Benoit Vallade, Alex Mackin, David Bull
Deep learning-based video quality assessment (deep VQA) has demonstrated significant potential in surpassing conventional metrics, with promising improvements in terms of correlation with human perception.
no code implementations • 14 Dec 2023 • Chen Feng, Duolikun Danier, Fan Zhang, Alex Mackin, Andy Collins, David Bull
Professionally generated content (PGC) streamed online can contain visual artefacts that degrade the quality of user experience.
1 code implementation • 31 Oct 2023 • Guoxuan Xia, Duolikun Danier, Ayan Das, Stathi Fotiadis, Farhang Nabiei, Ushnish Sengupta, Alberto Bernacchia
As a simple fix, we propose to instead reparameterise the score (at inference) by dividing it by the average absolute value of previous score estimates at that time step collected from offline high NFE generations.
2 code implementations • 16 Mar 2023 • Duolikun Danier, Fan Zhang, David Bull
Existing works on video frame interpolation (VFI) mostly employ deep neural networks that are trained by minimizing the L1, L2, or deep feature space distance (e. g. VGG loss) between their outputs and ground-truth frames.
1 code implementation • 16 Feb 2023 • Crispian Morris, Duolikun Danier, Fan Zhang, Nantheera Anantrasirichai, David R. Bull
Currently, one of the major challenges in deep learning-based video frame interpolation (VFI) is the large model sizes and high computational complexity associated with many high performance VFI approaches.
2 code implementations • 3 Oct 2022 • Duolikun Danier, Fan Zhang, David Bull
In order to narrow this research gap, we have developed a new video quality database named BVI-VFI, which contains 540 distorted sequences generated by applying five commonly used VFI algorithms to 36 diverse source videos with various spatial resolutions and frame rates.
1 code implementation • 18 Jul 2022 • Chen Feng, Zihao Qi, Duolikun Danier, Fan Zhang, Xiaozhong Xu, Shan Liu, David Bull
In this work, we modify the MFRNet network architecture to enable multiple frame processing, and the new network, multi-frame MFRNet, has been integrated into the EBDA framework using two Versatile Video Coding (VVC) host codecs: VTM 16. 2 and the Fraunhofer Versatile Video Encoder (VVenC 1. 4. 0).
no code implementations • 17 Jul 2022 • Duolikun Danier, Fan Zhang, David Bull
Video frame interpolation (VFI) serves as a useful tool for many video processing applications.
no code implementations • 19 May 2022 • Duolikun Danier, Chen Feng, Fan Zhang, David Bull
This paper describes a CNN-based multi-frame post-processing approach based on a perceptually-inspired Generative Adversarial Network architecture, CVEGAN.
no code implementations • 17 Feb 2022 • Chen Feng, Duolikun Danier, Fan Zhang, David Bull
In recent years, deep learning techniques have shown significant potential for improving video quality assessment (VQA), achieving higher correlation with subjective opinions compared to conventional approaches.
no code implementations • 15 Feb 2022 • Duolikun Danier, Fan Zhang, David Bull
This paper presents a new deformable convolution-based video frame interpolation (VFI) method, using a coarse to fine 3D CNN to enhance the multi-flow prediction.
no code implementations • 15 Feb 2022 • Duolikun Danier, Fan Zhang, David Bull
Video frame interpolation (VFI) is one of the fundamental research areas in video processing and there has been extensive research on novel and enhanced interpolation algorithms.
3 code implementations • CVPR 2022 • Duolikun Danier, Fan Zhang, David Bull
Video frame interpolation (VFI) is currently a very active research topic, with applications spanning computer vision, post production and video encoding.
Ranked #1 on Video Frame Interpolation on SNU-FILM (easy)
no code implementations • 30 Nov 2021 • Chen Feng, Duolikun Danier, Charlie Tan, Fan Zhang, David Bull
This paper presents a deep learning-based video compression framework (ViSTRA3).
no code implementations • 26 Feb 2021 • Duolikun Danier, David Bull
Our study shows that video texture has significant impact on the performance of frame interpolation models and it is beneficial to have separate models specifically adapted to these texture classes, instead of training a single model that tries to learn generic motion.