Abstract: The quality of video transmitted by mobile ad hoc networks (MANETs) can be influenced by several factors, including protocol layers; parameter settings of each protocol. In this paper, we are concerned with understanding the functional relationship between these influential factors and objective video quality in MANETs. We illustrate a systematic statistical design of experiments (DOE) strategy can be used to analyze MANET parameters and performance. Using a 2k factorial design, we quantify the main and interactive effects of 7 factors on a response metric (i.e., mean opinion score (MOS) calculated by PSNR with Evalvid package) we then develop a first-order linear regression model between the influential factors and the performance metric.
Abstract: Visual information is very important in human perception
of surrounding world. Video is one of the most common ways to
capture visual information. The video capability has many benefits
and can be used in various applications. For the most part, the
video information is used to bring entertainment and help to relax,
moreover, it can improve the quality of life of deaf people. Visual
information is crucial for hearing impaired people, it allows them to
communicate personally, using the sign language; some parts of the
person being spoken to, are more important than others (e.g. hands,
face). Therefore, the information about visually relevant parts of the
image, allows us to design objective metric for this specific case. In
this paper, we present an example of an objective metric based on
human visual attention and detection of salient object in the observed
scene.
Abstract: In this paper we proposed comparison of four content based objective metrics with results of subjective tests from 80 video sequences. We also include two objective metrics VQM and SSIM to our comparison to serve as “reference” objective metrics because their pros and cons have already been published. Each of the video sequence was preprocessed by the region recognition algorithm and then the particular objective video quality metric were calculated i.e. mutual information, angular distance, moment of angle and normalized cross-correlation measure. The Pearson coefficient was calculated to express metrics relationship to accuracy of the model and the Spearman rank order correlation coefficient to represent the metrics relationship to monotonicity. The results show that model with the mutual information as objective metric provides best result and it is suitable for evaluating quality of video sequences.