In this paper, a performance evaluation of the Quality-of-Experience (QoE) of YouTube video streaming in mobile broadband networks with active measurements is described. The measurements were collected from a field experiment campaign using the MONROE platform which provides probes in four European countries and enables the benchmarking of three mobile broadband operators. Firstly, we present a framework for the automated collection and processing of the measurements, and then, we analyze the results to identify the cache allocation policy per operator. Additionally, we examine whether the selected cache server has an effect on the delivered video quality and present the results using standardised objective methods for the estimation of the perceived quality.

HTTP adaptive streaming technology is now widely adopted in multimedia services because of its ability to provide adaptation to the streaming context, especially characteristics of end-user devices and dynamic network conditions. There are various studies targeting the evaluation of the Quality of Experience (QoE) in this framework. However, none has considered the scenario of the user changing the viewing device during the streaming session, which is the objective of this paper. It provides the following major contributions: definition of the multi-device streaming session scenario; the implementation of a realistic testing case; the execution of subjective tests involving 28 people; and the detailed analysis of the influence of the devices' switching events.

In this paper, a novel method to detect scene changes in encrypted video streams is presented. Typically, in IPTV systems, the media stream is transmitted in encrypted form, and therefore the only available information to determine the scene changes are the packet headers which transport the video signal. Thus, the proposed method estimates the size and the type of each picture of the video sequence by extracting information from the packet headers. Then, based on the GOP structure, a set of rules are determined to predict changes of frame sizes which are indicative of scene changes in the video sequences. Furthermore, the application of the proposed method in the recently standardized ITU-T Recommendation P.1201.2 for no-reference audio-visual quality assessment for IPTV-grade services is presented to highlight how such method could be deployed. Finally, the proposed method is evaluated on a large set of video databases to demonstrate the validity of the proposed method.

A parametric packet-based model has been created to estimate user perceived audiovisual quality of Internet Protocol Television (IPTV) services. It is divided into three modules, for audio, video and audiovisual quality. The model is applicable to the quality monitoring of encrypted and non-encrypted audiovisual streams. Typical audio and video degradations for IPTV are covered for Standard Definition (SD) and High Definition (HD) video formats. The model supports the H.264 video codec and the audio codecs MPEG-I Layer II, MPEG-2 AAC-LC, MPEG-4 HE-AACv2 and AC3. It handles various types of IP-network layer transmission errors. The model was developed and validated using a large database of subjective tests. The underlying concept is based on an impairment factor approach, which enables detection of how users build their individual judgment of quality of a given audiovisual signal. Each impairment factor captures the perceived quality impact of a possible degradation and therefore enables diagnostic analysis of quality problems. The model shows high performance results, both in terms of Pearson's Correlation coefficient (r) and Root-Mean-Square-Error (RMSE). The model is standardized as ITU-T Recommendation P.1201.2, the higher resolution (IPTV and Video on Demand (VoD)) algorithm of Recommendation P.1201.

Perception based coding with Texture Analysis and Synthesis (TAS) is a promising way to increase the compression efficiency of modern coding schemes, such as High Efficiency Video Coding (HEVC). TAS approaches typically employ an analysis step which specifies which blocks could be reconstructed by a texture synthesizer. Even though synthesized blocks are perceptually similar to their original versions, they may produce relatively high Mean Squared Error with regard to the original signal. Hence, the contribution of this paper will be a novel image quality assessment method for image completion based on visual attention variations. A subjective experiment has been designed to provide insight into the perceived distortions which are induced by TAS techniques and it is shown that saliency changes are a promising predictor for perceptual distortion. Free access to the image database is provided to encourage further research on this topic.

In this paper, a no reference bit stream model for quality assessment of SD and HD H.264/AVC video sequences based on packet loss visibility is proposed. The method considers the impact of network impairments on human perception and uses the visibility of packet losses to predict objective scores. Also, a new subjective experiment has been designed to provide insight into the perceptual effect of degradations caused by transmission errors. The proposed algorithm extracts a set of features from the received bit stream. Then, the visibility of each packet loss event is determined by classifying the extracted features using a Support Vector Machines classifier. Finally, analytical expressions are developed to account for visual degradation due to compression and channel induced distortion based on the outcome of the visibility classifier. The evaluation demonstrates the validity of the proposed method.

The widespread deployment of biometric systems has raised public concern about security and privacy of personal data. In this paper, we present a novel framework for biometric template security in multimodal biometric authentication systems based on error correcting codes. Biometric recognition is formulated as a channel coding problem with noisy side information at the decoder based on distributed source coding principles. It is shown that the proposed method binds the biometric template in a cryptographic key which does not reveal any information about the original biometric data even if it is compromised by an attacker. Furthermore, the advantages of the proposed method in terms of security and impact on matching accuracy are discussed. We assess the performance of the proposed method in the context of HUMABIO, an EU Specific Targeted Research Project, where face and gait biometrics are employed in an unobtrusive application scenario for human authentication. Experimental evaluation on a multimodal biometric database demonstrates the validity of the proposed method.

Human authentication using biometric traits has become an increasingly important issue in a large range of applications. In this paper, a novel channel coding approach for biometric authentication based on distributed source coding principles is proposed. Biometric recognition is formulated as a channel coding problem with noisy side information at the decoder and error correcting codes are employed for user verification. It is shown that the effective exploitation of the noise channel distribution in the decoding process improves performance. Moreover, the proposed method increases the security of the stored biometric templates. As a case study, the proposed framework is employed for the development of a novel gait recognition system based on the extraction of depth data from human silhouettes and a set of discriminative features. Specifically, gait sequences are represented using the radial and the circular integration transforms and features based on weighted Krawtchouk moments. Analytical models are derived for the effective modeling of the correlation channel statistics based on these features and integrated in the soft decoding process of the channel decoder. The experimental results demonstrate the validity of the proposed method over state-of-the-art techniques for gait recognition.

A novel gait authentication scheme based on distributed source coding principles is proposed. Biometric recognition is formulated as a coding problem with noisy side information at the decoder and error correcting codes are employed for user authentication. The effective exploitation of the noise channel statistics in the decoding process improves performance. It is also shown that the proposed method increases the security of the stored biometric templates. Gait recognition is based on the extraction of depth data from human silhouettes and a set of discriminative features. The experimental results demonstrate the validity of the proposed method.

This paper addresses the problem of frame interpolation for Wyner-Ziv video coding. A novel frame interpolation method based on block-adaptive matching algorithm for motion estimation is presented. This scheme enables block size adaptation to local activity within frames using block merging and splitting techniques. The efficiency of the proposed method is evaluated in transform domain Wyner-Ziv video coding. The experimental results demonstrate the superiority of the proposed method over existing frame interpolation techniques.

This paper proposes an innovative gait identification and authentication method based on the use of novel 2-D and 3-D features. Depth-related data are assigned to the binary image silhouette sequences using two new transforms: the 3-D radial silhouette distribution transform and the 3-D geodesic silhouette distribution transform. Furthermore, the use of a genetic algorithm is presented for fusing information from different feature extractors. Specifically, three new feature extraction techniques are proposed: the two of them are based on the generalized radon transform, namely the radial integration transform and the circular integration transform, and the third is based on the weighted Krawtchouk moments. Extensive experiments carried out on USF ldquoGait Challengerdquo and proprietary HUMABIO gait database demonstrate the validity of the proposed scheme.

We present a novel scheme for the transmission of H.264/AVC video streams over lossy packet networks. The proposed scheme exploits the error resilient features of H.264/AVC codec and employs Reed-Solomon codes to protect effectively the streams. The optimal classification of macroblocks into slice groups and the optimal channel rate allocation are achieved by iterating two interdependent steps. Simulations clearly demonstrate the superiority of the proposed method over other recent algorithms for transmission of H.264/AVC streams