A reduced-reference perceptual image and video quality metric based on edge preservation

In image and video compression and transmission, it is important to rely on an objective image/video quality metric which accurately represents the subjective quality of processed images and video sequences. In some scenarios, it is also important to evaluate the quality of the received video sequence with minimal reference to the transmitted one. For instance, for quality improvement of video transmission through closed-loop optimisation, the video quality measure can be evaluated at the receiver and provided as feedback information to the system controller. The original image/video sequence-prior to compression and transmission-is not usually available at the receiver side, and it is important to rely at the receiver side on an objective video quality metric that does not need reference or needs minimal reference to the original video sequence. The observation that the human eye is very sensitive to edge and contour information of an image underpins the proposal of our reduced reference (RR) quality metric, which compares edge information between the distorted and the original image. Results highlight that the metric correlates well with subjective observations, also in comparison with commonly used full-reference metrics and with a state-of-the-art RR metric. © 2012 Martini et al

Comparing apples and oranges: assessment of the relative video quality in the presence of different types of distortions

<p>Abstract</p> <p>Video quality assessment is essential for the performance analysis of visual communication applications. Objective metrics can be used for estimating the relative quality differences, but they typically give reliable results only if the compared videos contain similar types of quality distortion. However, video compression typically produces different kinds of visual artifacts than transmission errors. In this article, we focus on a novel subjective quality assessment method that is suitable for comparing different types of quality distortions. The proposed method has been used to evaluate how well different objective quality metrics estimate the relative subjective quality levels for content with different types of quality distortions. Our conclusion is that none of the studied objective metrics works reliably for assessing the co-impact of compression artifacts and transmission errors on the subjective quality. Nevertheless, we have observed that the objective metrics' tendency to either over- or underestimate the perceived impact of transmission errors has a high correlation with the spatial and temporal activity levels of the content. Therefore, our results can be useful for improving the performance of objective metrics in the presence of both source and channel distortions.</p