Perceptual Quality Measure using a Spatio-Temporal Model of the Human Visual System

This paper addresses the problem of quality estimation of digitally coded video sequences. The topic is of great interest since many products in digital video are about to be released and it is thus important to have robust methodologies for testing and performance evaluation of such devices. The inherent problem is that human vision has to be taken into account in order to assess the quality of a sequence with a good correlation with human judgment. It is well known that the commonly used metric, the signal-to-noise ratio is not correlated with human vision. A metric for the assessment of video coding quality is presented. It is based on a multi- channel model of human spatio-temporal vision that has been parameterized for video coding applications by psychophysical experiments. The visual mechanisms of vision are simulated by a spatio-temporal filter bank. The decomposition is then used to account for phenomena as contrast sensitivity and masking. Once the amount of distortions actually perceived is known, quality estimation can be assessed at various levels. The described metric is able to rate the overall quality of the decoded video sequence as well as the rendition of important features of the sequence such as contours or textures

No-reference bitstream-based visual quality impairment detection for high definition H.264/AVC encoded video sequences

Ensuring and maintaining adequate Quality of Experience towards end-users are key objectives for video service providers, not only for increasing customer satisfaction but also as service differentiator. However, in the case of High Definition video streaming over IP-based networks, network impairments such as packet loss can severely degrade the perceived visual quality. Several standard organizations have established a minimum set of performance objectives which should be achieved for obtaining satisfactory quality. Therefore, video service providers should continuously monitor the network and the quality of the received video streams in order to detect visual degradations. Objective video quality metrics enable automatic measurement of perceived quality. Unfortunately, the most reliable metrics require access to both the original and the received video streams which makes them inappropriate for real-time monitoring. In this article, we present a novel no-reference bitstream-based visual quality impairment detector which enables real-time detection of visual degradations caused by network impairments. By only incorporating information extracted from the encoded bitstream, network impairments are classified as visible or invisible to the end-user. Our results show that impairment visibility can be classified with a high accuracy which enables real-time validation of the existing performance objectives

An automatic technique for visual quality classification for MPEG-1 video

The Centre for Digital Video Processing at Dublin City University developed Fischlar [1], a web-based system for recording, analysis, browsing and playback of digitally captured television programs. One major issue for Fischlar is the automatic evaluation of video quality in order to avoid processing and storage of corrupted data. In this paper we propose an automatic classification technique that detects the video content quality in order to provide a decision criterion for the processing and storage stages