Existing Video Quality Metrics Research Articles

A Metric for Video Blending Quality Assessment.

We propose an objective approach to assess the quality of video blending. Blending is a fundamental operation in video editing, which can smooth the intensity changes of relevant regions. However blending also generates artefacts such as bleeding and ghosting. To assess the quality of the blended videos, our approach considers the illuminance consistency as a positive aspect while regard the artefacts as a negative aspect. Temporal coherence between frames is also considered. We evaluate our metric on a video blending dataset where the results of subjective evaluation are available. Experimental results validate the effectiveness of our proposed metric, and shows that this metric gives superior performance over existing video quality metrics.

Quality assessment of perceptual color video based on a top-down framework and quaternion

Objective video quality assessment is of great importance in a variety of video processing applications. Most existing video quality metrics either focus primarily on capturing spatial artifacts in the video signal, or are designed to assess only grayscale video thereby ignoring important chrominance information. In this paper, on the basis of the top-down visual analysis of cognitive understanding and video features, we propose and develop a novel full-reference perceptual video assessment technique that accepts visual information inputs in the form of a quaternion consisting of contour, color and temporal information. Because of the more important role of chrominance information in the "border-to-surface" mechanism at early stages of cognitive visual processing, our new metric takes into account the chrominance information rather than the luminance information utilized in conventional video quality assessment. Our perceptual quaternion model employs singular value decomposition (SVD) and utilizes the human visual psychological features for SVD block weighting to better reflect perceptual focus and interest. Our major contributions include: a new perceptual quaternion that takes chrominance as one spatial feature, and temporal information to model motion or changes across adjacent frames; a three-level video quality measure to reflect visual psychology; and the two weighting methods based on entropy and frame correlation. Our experimental validation on the video quality experts' group (VQEG) Phase I FR-TV test dataset demonstrated that our new assessment metric outperforms PSNR, SSIM, PVQM (P8) and has high correlation with perceived video quality.

Video quality for face detection, recognition, and tracking

Many distributed multimedia applications rely on video analysis algorithms for automated video and image processing. Little is known, however, about the minimum video quality required to ensure an accurate performance of these algorithms. In an attempt to understand these requirements, we focus on a set of commonly used face analysis algorithms. Using standard datasets and live videos, we conducted experiments demonstrating that the algorithms show almost no decrease in accuracy until the input video is reduced to a certain critical quality, which amounts to significantly lower bitrate compared to the quality commonly acceptable for human vision. Since computer vision percepts video differently than human vision, existing video quality metrics, designed for human perception, cannot be used to reason about the effects of video quality reduction on accuracy of video analysis algorithms. We therefore investigate two alternate video quality metrics, blockiness and mutual information, and show how they can be used to estimate the critical video qualities for face analysis algorithms.