Abstract

No abstract

No abstract

No abstract

No abstract

Understanding the characteristics of high-quality professional videos is important for video classification, video quality measurement, and video enhancement. A professional video is good not only for its interesting story but also for its high visual quality. In this paper, we study what makes a professional video from the perspective of aesthetics. We discuss how a professional video is created and correspondingly design a variety of features that distinguish professional videos from amateur ones. We study general aesthetics features that are applied to still photos and extend them to videos. We design a variety of features that are particularly relevant to videos. We examined the performance of these features in the problem of professional and amateur video classification. Our experiments show that with these features, 97.3% professional and amateur shot classification accuracy rate is achieved on our own data set and 91.2% professional video detection rate is achieved on a public professional video set. Our experiments also show that the features that are particularly for videos are shown most effective for this task.

The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative. Results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2021 challenge was composed of four sub-challenges focusing on different tracking domains: (i) VOT-ST2021 challenge focused on short-term tracking in RGB, (ii) VOT-RT2021 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2021 focused on long-term tracking, namely coping with target disappearance and reappearance and (iv) VOT-RGBD2021 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2021 dataset was refreshed, while VOT-RGBD2021 introduces a training dataset and sequestered dataset for winner identification. The source code for most of the trackers, the datasets, the evaluation kit and the results along with the source code for most trackers are publicly available at the challenge website 1 .

Image quality is important not only for the viewing experience, but also for the performance of image processing algorithms. Image quality assessment (IQA) has been a topic of intense research in the fields of image processing and computer vision. In this paper, we first analyze the factors that affect twodimensional (2D) and three-dimensional (3D) image quality, and then provide an up-to-date overview on IQA for each main factor. The main factors that affect 2D image quality are fidelity and aesthetics. Another main factor that affects stereoscopic 3D image quality is visual comfort. We also describe the IQA databases and give the experimental results on representative IQA metrics. Finally, we discuss the challenges for IQA, including the influence of different factors on each other, the performance of IQA metrics in real applications, and the combination of quality assessment, restoration, and enhancement. INDEX TERMS Image quality assessment, image aesthetics assessment, visual comfort, and image quality enhancement.