no code implementations • 29 Apr 2023 • Mohsen Jenadeleh, Johannes Zagermann, Harald Reiterer, Ulf-Dietrich Reips, Raouf Hamzaoui, Dietmar Saupe
The experimental results show that the inclusion of the ``not sure'' response option in the forced choice method reduced mental load and led to models with better data fit and correspondence to ground truth.
1 code implementation • 24 Mar 2023 • Jinrui Xing, Hui Yuan, Raouf Hamzaoui, Hao liu, Junhui Hou
To reduce color distortion in point clouds, we propose a graph-based quality enhancement network (GQE-Net) that uses geometry information as an auxiliary input and graph convolution blocks to extract local features efficiently.
no code implementations • 30 Nov 2022 • Qi Liu, Yiyun Liu, Honglei Su, Hui Yuan, Raouf Hamzaoui
In this paper, a progressive knowledge transfer based on human visual perception mechanism for perceptual quality assessment of point clouds (PKT-PCQA) is proposed.
no code implementations • 2 Mar 2022 • Hao liu, Hui Yuan, Junhui Hou, Raouf Hamzaoui, Wei Gao
We propose a generative adversarial network for point cloud upsampling, which can not only make the upsampled points evenly distributed on the underlying surface but also efficiently generate clean high frequency regions.
no code implementations • 25 Nov 2020 • Qi Liu, Hui Yuan, Raouf Hamzaoui, Honglei Su, Junhui Hou, Huan Yang
In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bit rate.
1 code implementation • 7 Jan 2020 • Hanhe Lin, Vlad Hosu, Chunling Fan, Yun Zhang, Yuchen Mu, Raouf Hamzaoui, Dietmar Saupe
We then use deep feature learning to predict samples of the SUR curve and apply the method of least squares to fit the parametric model to the predicted samples.
no code implementations • 24 Oct 2018 • Erinc Merdivan, Anastasios Vafeiadis, Dimitrios Kalatzis, Sten Hanke, Johannes Kropf, Konstantinos Votis, Dimitrios Giakoumis, Dimitrios Tzovaras, Liming Chen, Raouf Hamzaoui, Matthieu Geist
We propose a new approach to natural language understanding in which we consider the input text as an image and apply 2D Convolutional Neural Networks to learn the local and global semantics of the sentences from the variations ofthe visual patterns of words.