Quantifying subjective quality evaluations for mobile video watching in a semi-living lab context

This paper discusses results from an exploratory study in which Quality of Experience aspects related to mobile video watching were investigated in a semi-living lab setting. More specifically, we zoom in on usage patterns in a natural research context and on the subjective evaluation of high and low-resolution movie trailers that are transferred to a mobile device using two transmission protocols for video (i.e., real-time transport protocol and progressive download using HTTP). User feedback was collected by means of short questionnaires on the mobile device, combined with traditional pen and paper diaries. The subjective evaluations regarding the general technical quality, perceived distortion, fluentness of the video, and loading speed are studied and the influence of the transmission protocol and video resolution on these evaluations is analyzed. Multinomial logistic regression results in a model to estimate the subjective evaluations regarding the perceived distortion and loading speed based on objectively-measured parameters of the video session

Subjective Quality Assessment of the Impact of Buffer Size in Fine-Grain Parallel Video Encoding

Fine-Grain parallelism is essential for real-time video encoding performance. This usually implies setting a fixed buffer size for each encoded block. The choice of this parameter is critical for both performance and hardware cost. In this paper we analyze the impact of buffer size on image subjective quality, and its relation with other encoding parameters. We explore the consequences on visual quality, when minimizing buffer size to the point of causing the discard of quantized coefficients for highest frequencies. Finally, we propose some guidelines for the choice of buffer size, that has proven to be heavily dependent, in addition to other parameters, on the type of sequence being encoded. These guidelines are useful for the design of efficient realtime encoders, both hardware and software

Testing QoE in Different 3D HDTV Technologies

The three dimensional (3D) display technology has started flooding the consumer television market. There is a number of different systems available with different marketing strategies and different advertised advantages. The main goal of the experiment described in this paper is to compare the systems in terms of achievable Quality of Experience (QoE) in different situations. The display systems considered are the liquid crystal display using polarized light and passive lightweight glasses for the separation of the left- and right-eye images, a plasma display with time multiplexed images and active shutter glasses and a projection system with time multiplexed images and active shutter glasses. As no standardized test methodology has been defined for testing of stereoscopic systems, we develop our own approach to testing different aspects of QoE on different systems without reference using semantic differential scales. We present an analysis of scores with respect to different phenomena under study and define which of the tested aspects can really express a difference in the performance of the considered display technologies

Social Validity Assessment in Social Competence Interventions for Preschool Children: A Review

Social competence intervention studies published from 1970 to 2008 for preschoolers were reviewed for reports of social validity assessment. Analysis of 90 studies indicated that nearly 27% (n=24) of studies reported at least one measure of social validity assessment for: the goals (n=7), procedures (n=8), or effects (n=19). The methods used for these assessments were described including information about the participants, time of assessment and results. Trends in the rate of social validity assessment, methodological concerns and recommendations for further social validity assessments are discussed

Constructing a no-reference H.264/AVC bitstream-based video quality metric using genetic programming-based symbolic regression

In order to ensure optimal quality of experience toward end users during video streaming, automatic video quality assessment becomes an important field-of-interest to video service providers. Objective video quality metrics try to estimate perceived quality with high accuracy and in an automated manner. In traditional approaches, these metrics model the complex properties of the human visual system. More recently, however, it has been shown that machine learning approaches can also yield competitive results. In this paper, we present a novel no-reference bitstream-based objective video quality metric that is constructed by genetic programming-based symbolic regression. A key benefit of this approach is that it calculates reliable white-box models that allow us to determine the importance of the parameters. Additionally, these models can provide human insight into the underlying principles of subjective video quality assessment. Numerical results show that perceived quality can be modeled with high accuracy using only parameters extracted from the received video bitstream