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The growth of digital video has given rise to a need for computational methods for evaluating the visual quality of digital video. We have developed a new digital video quality metric, which we call DVQ (Digital Video Quality) . Here we provide a brief description of the metric, and give a preliminary report on its performance. DVQ accepts a pair of digital video sequences, and computes a measure of the magnitude of the visible difference between them. The metric is based on the Discrete Cosine Transform. It incorporates aspects of early visual processing, including light adaptation, luminance and chromatic channels, spatial and temporal filtering, spatial frequency channels, contrast masking, and probability summation. It also includes primitive dynamics of light adaptation and contrast masking. We have applied the metric to digital video sequences corrupted by various typical compression artifacts, and compared the results to quality ratings made by human observers.

We propose a new model for the prediction of distortion visibility in digital image sequences, which is aimed at use in digital video compression algorithms. The model is an extension of our spatial vision model with a spatio-temporal contrast sensitivity function and an eye movement estimation algorithm. Due to the importance of smooth pursuit eye movements when viewing image sequences, eye movements cannot be neglected in a spatio-temporal vision model. Although eye movements can be incorporated by motion compensation of the contrast sensitivity function, the requirements for this motion compensation are different than those for motion compensated prediction in video coding. We propose an algorithm for the estimation of smooth pursuit eye movements, under the worst-case assumption that the observer is capable of tracking all objects in the image. In image and image sequence compression, models which predict the visibility of coding distortions can be used to improve the visual quality of the total compression system. Many models have been proposed for the processing of spatial information in the human visual system to predict distortion visibility in coded images [1-5]. However, the number

The growth of digital video has given rise to a need for computational methods for evaluating the visual quality of digital video. We have developed a new digital video quality metric, which we call DVQ (Digital Video Quality) 1 .Here we provide a brief description of the metric, and give a preliminary report on its performance. DVQ accepts a pair of digital video sequences, and computes a measure of the magnitude of the visible difference between them. The metric is based on the Discrete Cosine Transform. It incorporates aspects of early visual processing, including light adaptation, luminance and chromatic channels, spatial and temporal filtering, spatial frequency channels, contrast masking, and probability summation. It also includes primitive dynamics of light adaptation and contrast masking. We have applied the metric to digital video sequences corrupted by various typical compression artifacts, and compared the results to quality ratings made by human observers. 1. INTRODUCTI...

In our study, we explore the human side of the multimedia experience. The authors propose a model that assesses quality variation from three distinct levels: the network-, the media- and the content-levels; and from two views: the technical- and the user-perspective. By facilitating parameter variation at each of the quality levels and from each of the perspectives, we were able to examine their impact on user quality perception. Results show that: a significant reduction in frame rate does not proportionally reduce the user's understanding of the presentation, independent of technical parameters; the type of video clip significantly impacts user information assimilation, user level of enjoyment and user perception of quality; the display type impacts user information assimilation and user perception of quality. Finally, to ensure transfer of informational content, network parameter variation should be adapted; to maintain user enjoyment, video content variation should be adapted.

Home networks are becoming ubiquitous, especially since the advent of wireless technologies such as IEEE 802.11. Coupled with this, there is an increase in the number of broadband-connected homes, and many new services are being deployed by broadband providers, such as TV and VoIP. The home network is thus becoming the ‘media hub ’ of the house. This trend is expected to continue, and to expand into the Consumer Electronics (CE) market as well. This means new devices that can tap into the network in order to get their data, such as wireless TV sets, gaming consoles, tablet PCs etc. In this paper, we address the issue of evaluating the QoS provided for those media services, from the end-user’s point of view. We present a performance analysis of the home network in terms of perceived quality, and show how our real-time quality assessment technique can be used to dynamically control existing QoS mechanisms. This participates to minimizing resource consumption by tuning the appropriate QoS affecting parameters in order to keep the perceived quality (the ultimate target) within acceptable bounds.

We propose a vision model for predicting the significance of coding errors in digital image sequences. Eye movements have a significant effect on distortion visibility, and hence they form an important part of the model. An algorithm is proposed to make a prediction of the eye movements a viewer will make when watching the image sequence. Furthermore, the spatio-temporal frequency sensitivity and masking effects are included in the model. The model is based on a decomposition of the signal into frequency and orientation bands, in order to allow accurate modeling of spatial masking, which occurs mainly between signal components which are similar in position, orientation, and frequency. 1. Introduction The Mean Squared Error (MSE) is widely used as a distortion measure for optimization of image compression algorithms, probably because its calculation is very simple. However, it is not a very good measure of quality as experienced by human viewers. Therefore, many alternative distortion ...

There is an increasing demand for streaming video applications over both the fixed Internet and wireless IP networks. The fluctuating bandwidth and time-varying delays of best-effort networks makes providing good quality streaming a challenge. Many adaptive video delivery mechanisms have been proposed over recent years; however, most do not explicitly consider user-perceived quality when making adaptations, nor do they define what quality is. This chapter describes research that proposes that an optimal adaptation trajectory through the set of possible encodings exists, and indicates how to adapt transmission in response to changes in network conditions in order to maximize user-perceived quality.