Two-Level Approach for No-Reference Consumer Video Quality Assessment.

Smartphones and other consumer devices capable of capturing video content and sharing it on social media in nearly real time are widely available at a reasonable cost. Thus, there is a growing need for no-reference video quality assessment (NR-VQA) of consumer produced video content, typically characterized by capture impairments that are qualitatively different from those observed in professionally produced video content. To date, most of the NR-VQA models in prior art have been developed for assessing coding and transmission distortions, rather than capture impairments. In addition, the most accurate NR-VQA methods known in prior art are often computationally complex, and therefore impractical for many real life applications. In this paper, we propose a new approach for learning-based video quality assessment, based on the idea of computing features in two levels so that low complexity features are computed for the full sequence first, and then high complexity features are extracted from a subset of representative video frames, selected by using the low complexity features. We have compared the proposed method against several relevant benchmark methods using three recently published annotated public video quality databases, and our results show that the proposed method can predict subjective video quality more accurately than the benchmark methods. The best performing prior method achieves nearly similar accuracy, but at substantially higher computational cost.

Modeling the Quality of Videos Displayed With Local Dimming Backlight at Different Peak White and Ambient Light Levels.

This paper investigates the impact of ambient light and peak white (maximum brightness of a display) on the perceived quality of videos displayed using local backlight dimming. Two subjective tests providing quality evaluations are presented and analyzed. The analyses of variance show significant interactions of the factors peak white and ambient light with the perceived quality. Therefore, we proceed to predict the subjective quality grades with objective measures. The rendering of the frames on liquid crystal displays with light emitting diodes backlight at various ambient light and peak white levels is computed using a model of the display. Widely used objective quality metrics are applied based on the rendering models of the videos to predict the subjective evaluations. As these predictions are not satisfying, three machine learning methods are applied: partial least square regression, elastic net, and support vector regression. The elastic net method obtains the best prediction accuracy with a spearman rank order correlation coefficient of 0.71, and two features are identified as having a major influence on the visual quality.

Modeling the subjective quality of highly contrasted videos displayed on LCD with local backlight dimming.

Local backlight dimming is a technology aiming at both saving energy and improving visual quality on television sets. As the rendition of the image is specified locally, the numerical signal corresponding to the displayed image needs to be computed through a model of the display. This simulated signal can then be used as input to objective quality metrics. The focus of this paper is on determining which characteristics of locally backlit displays influence quality assessment. A subjective experiment assessing the quality of highly contrasted videos displayed with various local backlight-dimming algorithms is set up. Subjective results are then compared with both objective measures and objective quality metrics using different display models. The first analysis indicates that the most significant objective features are temporal variations, power consumption (probably representing leakage), and a contrast measure. The second analysis shows that modeling of leakage is necessary for objective quality assessment of sequences displayed with local backlight dimming.

Interleukin-1ß is internalised by viable Aggregatibacter actinomycetemcomitans biofilm and locates to the outer edges of nucleoids.

The opportunistic pathogen Aggregatibacter actinomycetemcomitans causes periodontitis, which is a biofilm infection that destroys tooth-supportive tissues. Interleukin (IL)-1ß, a central proinflammatory cytokine of periodontitis, is an essential first line cytokine for local inflammation that modulates the cell proliferation and anti-pathogen response of human gingival keratinocytes. Previously, we demonstrated that A. actinomycetemcomitans biofilms bind IL-1ß; however, whether this binding is an active process is not known. In this study, we showed for the first time with immuno-electron microscopy that viable bacterial biofilm cells internalised IL-1ß when co-cultured with an organotypic mucosa. Decreased biofilm viability hindered the ability of biofilm to sequester IL-1ß and caused IL-1ß leakage into the culture medium. In some A. actinomycetemcomitans cells, intracellular IL-1ß localized to the outer edges of the nucleoids. We identified the DNA-binding protein HU as an IL-1ß interacting protein with mass spectroscopy and showed the interaction of recombinant HU and IL-1ßin vitro using enzyme-linked immunosorbent assay (ELISA). Close contact with a viable A. actinomycetemcomitans biofilm decreased the proliferation and apoptosis of human gingival keratinocytes as demonstrated using Ki-67 and the terminal deoxynucleotidyl transferase dUTP nick-end labelling (TUNEL) staining, respectively. Our results suggest that viable A. actinomycetemcomitans biofilms may disturb the critical first steps of local inflammation in periodontitis by binding and internalising IL-1ß. The interaction of IL-1ß with conserved HU provides a potential mechanism for shaping bacterial gene expression.