NTIRE 2023 Quality Assessment of Video Enhancement Challenge

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance.

Validating CircaCP: a Generic Sleep-Wake Cycle Detection Algorithm

Sleep-wake cycle detection is a key step when extrapolating sleep patterns from actigraphy data. Numerous supervised detection algorithms have been developed with parameters estimated from and optimized for a particular dataset, yet their generalizability from sensor to sensor or study to study is unknown. In this paper, we propose and validate an unsupervised algorithm -- CircaCP -- to detect sleep-wake cycles from minute-by-minute actigraphy data. It first uses a robust cosinor model to estimate circadian rhythm, then searches for a single change point (CP) within each cycle. We used CircaCP to estimate sleep/wake onset times (S/WOTs) from 2125 indviduals' data in the MESA Sleep study and compared the estimated S/WOTs against self-reported S/WOT event markers. Lastly, we quantified the biases between estimated and self-reported S/WOTs, as well as variation in S/WOTs contributed by the two methods, using linear mixed-effects models and variance component analysis. On average, SOTs estimated by CircaCP were five minutes behind those reported by event markers, and WOTs estimated by CircaCP were less than one minute behind those reported by markers. These differences accounted for less than 0.2% variability in SOTs and in WOTs, taking into account other sources of between-subject variations. By focusing on the commonality in human circadian rhythms captured by actigraphy, our algorithm transferred seamlessly from hip-worn ActiGraph data collected from children in our previous study to wrist-worn Actiwatch data collected from adults. The large between- and within-subject variability highlights the need for estimating individual-level S/WOTs when conducting actigraphy research. The generalizability of our algorithm also suggests that it could be widely applied to actigraphy data collected by other wearable sensors.