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Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
Bosse, Sebastian,Maniry, Dominique,Muller, Klaus-Robert,Wiegand, Thomas,Samek, Wojciech IEEE 2018 IEEE TRANSACTIONS ON IMAGE PROCESSING - Vol.27 No.1
<P>We present a deep neural network-based approach to image quality assessment (IQA). The network is trained end-to-end and comprises ten convolutional layers and five pooling layers for feature extraction, and two fully connected layers for regression, which makes it significantly deeper than related IQA models. Unique features of the proposed architecture are that: 1) with slight adaptations it can be used in a no-reference (NR) as well as in a full-reference (FR) IQA setting and 2) it allows for joint learning of local quality and local weights, i.e., relative importance of local quality to the global quality estimate, in an unified framework. Our approach is purely data-driven and does not rely on hand-crafted features or other types of prior domain knowledge about the human visual system or image statistics. We evaluate the proposed approach on the LIVE, CISQ, and TID2013 databases as well as the LIVE In the wild image quality challenge database and show superior performance to state-of-the-art NR and FR IQA methods. Finally, cross-database evaluation shows a high ability to generalize between different databases, indicating a high robustness of the learned features.</P>
Toward a Direct Measure of Video Quality Perception Using EEG
Scholler, S.,Bosse, S.,Treder, M. S.,Blankertz, B.,Curio, G.,Muller, Klaus-Robert,Wiegand, T. IEEE 2012 IEEE TRANSACTIONS ON IMAGE PROCESSING - Vol.21 No.5
<P>An approach to the direct measurement of perception of video quality change using electroencephalography (EEG) is presented. Subjects viewed 8-s video clips while their brain activity was registered using EEG. The video signal was either uncompressed at full length or changed from uncompressed to a lower quality level at a random time point. The distortions were introduced by a hybrid video codec. Subjects had to indicate whether they had perceived a quality change. In response to a quality change, a positive voltage change in EEG (the so-called P3 component) was observed at latency of about 400-600 ms for all subjects. The voltage change positively correlated with the magnitude of the video quality change, substantiating the P3 component as a graded neural index of the perception of video quality change within the presented paradigm. By applying machine learning techniques, we could classify on a single-trial basis whether a subject perceived a quality change. Interestingly, some video clips wherein changes were missed (i.e., not reported) by the subject were classified as quality changes, suggesting that the brain detected a change, although the subject did not press a button. In conclusion, abrupt changes of video quality give rise to specific components in the EEG that can be detected on a single-trial basis. Potentially, a neurotechnological approach to video assessment could lead to a more objective quantification of quality change detection, overcoming the limitations of subjective approaches (such as subjective bias and the requirement of an overt response). Furthermore, it allows for real-time applications wherein the brain response to a video clip is monitored while it is being viewed.</P>
Potential of Argo Drifters for Estimating Biological Production within the Water Column
SeungHyun Son,노재훈,Emmanuel Boss 한국해양과학기술원 2006 Ocean science journal Vol.41 No.2
Argo drifters provide information of the vertical structure in the water column and have a potential for the improvement of understanding phytoplankton primary production and biogeochemical cycles in combination with ocean color satellite data, which can obtain the horizontal distribution of phytoplankton biomass in the surface layer. Our examples show that using Argo drifters with satellite-measured horizontal distribution of phytoplankton biomass at the sea surface allow an improved understanding of the development of the spring bloom. The other possible uses of Argo drifter are discussed.
Objective quality assessment of stereoscopic images with vertical disparity using EEG
Avarvand, Forooz Shahbazi,Bosse, Sebastian,Mü,ller, Klaus-Robert,Schä,fer, Ralf,Nolte, Guido,Wiegand, Thomas,Curio, Gabriel,Samek, Wojciech IOP 2017 Journal of neural engineering Vol.14 No.4
<P> <I>Objective</I>. Neurophysiological correlates of vertical disparity in 3D images are studied in an objective approach using EEG technique. These disparities are known to negatively affect the quality of experience and to cause visual discomfort in stereoscopic visualizations. <I>Approach</I>. We have presented four conditions to subjects: one in 2D and three conditions in 3D, one without vertical disparity and two with different vertical disparity levels. Event related potentials (ERPs) are measured for each condition and the differences between ERP components are studied. Analysis is also performed on the induced potentials in the time frequency domain. <I>Main results</I>. Results show that there is a significant increase in the amplitude of P1 components in 3D conditions in comparison to 2D. These results are consistent with previous studies which have shown that P1 amplitude increases due to the depth perception in 3D compared to 2D. However the amplitude is significantly smaller for maximum vertical disparity (3D-3) in comparison to 3D with no vertical disparity. Our results therefore suggest that the vertical disparity in 3D-3 condition decreases the perception of depth compared to other 3D conditions and the amplitude of P1 component can be used as a discriminative feature. <I>Significance</I>. The results show that the P1 component increases in amplitude due to the depth perception in the 3D stimuli compared to the 2D stimulus. On the other hand the vertical disparity in the stereoscopic images is studied here. We suggest that the amplitude of P1 component is modulated with this parameter and decreases due to the decrease in the perception of depth.</P>