The analysis of the impact of video content and transmission impairments on Quality of Experience (QoE) is a relevant topic for the robust design and adaptation of multimedia infrastructures, services, and applications. The goal of this paper is to study the impact of video content on QoE for different levels of impairments. In more details, this contribution aims at i) the study of the impact of delay, jitter, packet loss, and bandwidth on QoE, ii) the analysis of the impact of video content on QoE, and iii) the evaluation of the relationship between content related parameters (spatial-temporal perceptual information, motion, and data rate) and the QoE for different levels of impairments.

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Evaluation of perceived quality of light field images, as well as testing new processing tools, or even assessing the effectiveness of objective quality metrics, relies on the availability of test dataset and corresponding quality ratings. This article presents SMART light field image quality dataset. The dataset consists of source images (raw data without optical corrections), compressed images, and annotated subjective quality scores. Furthermore, analysis of perceptual effects of compression on SMART dataset is presented. Next, the impact of image content on the perceived quality is studied with the help of image quality attributes. Finally, the performances of 2D image quality metrics when applied to light field images are analyzed.

In this paper a video database, ReTRiEVED, to be used in evaluating the performances of video quality metrics is presented. The database contains 184 distorted videos obtained from eight videos of different content. Packet loss rate, jitter, delay, and throughput have been considered as possible distortions resulting from video transmission. Video sequences, collected subjective scores, and results of the performed analysis are made publicly available for the research community, for designing, testing and comparing objective video quality metrics. The analysis of the results shows that packet loss rate, throughput/bandwidth, and jitter have significant effect on perceived quality, while an initial delay does not significantly affect the perceived quality

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Multimedia service providers have to deliver video content through bandwidth limited and error-prone networks with agreed level of perceived video quality to customers for specific applications. To this aim service providers must devise a strategy to monitor the perceived quality and automatically adapt it when necessary. Measuring perceived quality is challenging for service providers. So there is the need for a mapping model to predict perceived video quality from system related quality of service parameters. In this article performance comparison of widely used quality of service to quality of experience mapping models has been presented and optimal solution has been recommended for key quality of service parameters: jitter, delay, packet loss rate and throughput limitation. For this purpose the freely available video quality database ReTRiEVED has been used

Recent research has shown that reliable recognition of sign language words and phrases using user-friendly and noninvasive armbands is feasible and desirable. This work provides an analysis and implementation of including fingerspelling recognition (FR) in such systems, which is a much harder problem due to lack of distinctive hand movements. A novel algorithm called DyFAV (Dynamic Feature Selection and Voting) is proposed for this purpose that exploits the fact that fingerspelling has a finite corpus (26 alphabets for the American Sign Language (ASL)). Detailed analysis of the algorithm used as well as comparisons with other traditional machine-learning algorithms is provided. The system uses an independent multiple-agent voting approach to identify letters with high accuracy. The independent voting of the agents ensures that the algorithm is highly parallelizable and thus recognition times can be kept low to suit real-time mobile applications. A thorough explanation and analysis is presented on results obtained on the ASL alphabet corpus for nine people with limited training. An average recognition accuracy of 95.36% is reported and compared with recognition results from other machine-learning techniques. This result is extended by including six additional validation users with data collected under similar settings as the previous dataset. Furthermore, a feature selection schema using a subset of the sensors is proposed and the results are evaluated. The mobile, noninvasive, and real-time nature of the technology is demonstrated by evaluating performance on various types of Android phones and remote server configurations. A brief discussion of the user interface is provided along with guidelines for best practices.

Securing multimedia data from undesired manipulation is a widely investigated topic in the state of the art. A large number of techniques has been developed for protecting images, videos, and also audio from malicious attacks. Nowadays new imaging systems pose new challenges from the data protection point of view. In particular, plenoptic cameras allow to record multiple views of a scene by using a single camera in a single shot, thus avoiding the problems related to calibration and camera synchronization. In this contribution, a novel embedding scheme tuned to light fields is presented. The watermark is inserted in the horizontal and vertical detail subbands of the first level of the Haar wavelet transform. The aim of this work is twofold: the design of a water-marking system that does not affect the depth map estimation procedure and therefore the refocusing procedure and the 3D scene reconstruction. On the other hand, the impact of light field data modification with respect to the perceived quality has been investigated. Experimental tests, both objective and subjective, have been carried out for assessing the performances of the proposed algorithm