Content-aware objective video quality assessment

Abstract

Abstract. Since the end-user of video-based systems is often a human observer, prediction of user-perceived video quality (PVQ) is an important task for increasing the user satisfaction. Despite the large variety of objective video quality measures (VQMs), their lack of generalizability remains a problem. This is mainly due to the strong dependency between PVQ and video content. Although this problem is well known, few existing VQMs directly account for the influence of video content on PVQ. Recently, we proposed a method to predict PVQ by introducing relevant video content features in the computation of video distortion measures. The method is based on analyzing the level of spatiotemporal activity in the video and using those as parameters of the anthropomorphic video distortion models. We focus on the experimental evaluation of the proposed methodology based on a total of five public databases, four different objective VQMs, and 105 content related indexes. Additionally, relying on the proposed method, we introduce an approach for selecting the levels of video distortions for the purpose of subjective quality assessment studies. Our results suggest that when adequately combined with content related indexes, even very simple distortion measures (e.g., peak signal to noise ratio) are able to achieve high performance, i.e., high correlation between the VQM and the PVQ. In particular, we have found that by incorporating video content features, it is possible to increase the performance of the VQM by up to 20% relative to its noncontent-aware baseline.

Quality Assessment of In-the-Wild Videos

“…Content-aware features can help addressing content-dependency on the predicted image/video quality, so as to improve the performance of objective models [13,17,41,49]. Jaramillo et al [13] Figure 2: The overall framework of the proposed method. It mainly consists of two modules.…”