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.
Highlights
Quality control of video-based systems is a very important task for increasing the user satisfaction
We found that among the tested content related indexes, those based on statistics of images filtered with SI13 filter, temporal gradients and spatial dependencies of pixel values are simple and effective in estimating content information as it has already been suggested in other related works.[7,14,16,17,26,33]
Sec. 3.2), we can still achieve higher performance than peak signal to noise ratio (PSNR) with a percentage of increase of 7.5% (21% in linear Fisher’s Z) in Pearson correlation coefficient (PCC) as well as in Spearman rank-order correlation coefficient (SROCC) and a percentage of decrease of 20% in root mean-squared error (RMSE) as well as in mean absolute error (MAE)
Summary
Quality control of video-based systems is a very important task for increasing the user satisfaction. VQMs use computer algorithms for computing numerical scores on corrupted video sequences that should agree with the subjective assessment provided by human evaluators. 1(a) and 1(b) can be further classified into traditional point-based (TPB) methods, natural visual characteristic (NVC) methods, or perceptual oriented (POM) methods, depending on the set of techniques used to compute the quality measure.[25] TPB full-reference quality measure use pixel-wise operations for computing differences between images and/or video sequences, for instance, PSNR is the most simple but still widely used TPB full-reference quality measure.[2] NVC methods use statistical measures (mean, variance, histograms) in local neighborhoods and/or visual features (blurring, blocking, texture, visual impairments) for computing numerical scores. The extracted features from reference and corrupted sequences are thresholded, compared, and pooled to obtain a unique numerical quality measure. Another example from this category is the wellknown SSIM27 which uses statistics (mean and standard (a)
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