Savvas Argyropoulos

Gait Recognition Using Compact Feature Extraction Transforms and Depth Information

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.

Robust Transmission of H.264/AVC Video using Adaptive Slice Grouping and Unequal Error Protection

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

IP-Based Mobile and Fixed Network Audiovisual Media Services

This article provides a tutorial overview of current approaches for monitoring the quality perceived by users of IP-based audiovisual media services. The article addresses both mobile and fixed network services such as mobile TV or Internet Protocol TV (IPTV). It reviews the different quality models that exploit packet- header-, bit stream-, or signal-information for providing audio, video, and audiovisual quality estimates, respectively. It describes how these models can be applied for real-life monitoring, and how they can be adapted to reflect the information available at the given measurement point. An outlook gives insight into emerging trends for near- and mid-term future requirements and solutions.

A Channel Coding Approach for Human Authentication From Gait Sequences

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.

No-reference video quality assessment for SD and HD H.264/AVC sequences based on continuous estimates of packet loss visibility

In this paper, a novel method for predicting the visibility of packet losses in SD and HD H.264/AVC video sequences and modeling their impact on perceived quality is proposed. Based on the findings of a new subjective experiment it is initially shown that the classification of packet loss visibility in a binary fashion is not sufficient to model the perceptual degradations caused by the transmission errors. The proposed no-reference algorithm extracts a set of features from the video bit-stream to account for the spatial and temporal characteristics of the video content and the induced distortion due to the network impairments. Subsequently, the visibility of packet losses is predicted in a continuous fashion using Support Vector Regression. Finally, a no-reference bit-stream based video quality assessment model that explicitly employs the predicted packet loss visibility estimates is presented. The evaluation of the proposed model demonstrates that the use of continuous estimates for the visibility of packet losses improves the performance of the video quality assessment model.

Robust Transmission of Multi-View Video Streams using Flexible Macroblock Ordering and Systematic LT Codes

The transmission of fully compatible H.264/AVC multi-view video coded streams over packet erasure networks is examined. Macroblock classification into unequally important slice groups is considered using the flexible macroblock ordering (FMO) tool of H.264/AVC. Systematic LT codes are used for error protection due to their low complexity and advanced performance. The optimal slice grouping and channel rate allocation are jointly determined by an iterative optimization algorithm based on dynamic programming. The experimental evaluation clearly demonstrates the validity of the proposed method.

No-reference bit stream model for video quality assessment of h.264/AVC video based on packet loss visibility

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.

Optimal Adaptation Trajectories for Block-Request Adaptive Video Streaming

Block-Request Adaptive Streaming (BRAS), in form of its most prominent representative HTTP-Based Adaptive Streaming (HAS), is about to become the dominating technology for video delivery over the Internet. One of the challenges in the development of BRAS clients is the design of mechanisms that dynamically adapt the streamed video quality to network conditions, in order to maximize users Quality of Experience (QoE). The main contribution of this paper is an approach to calculating optimal adaptation trajectories. This approach not only allows to benchmark the performance of any streaming client, it also provides the possibility to study the impact of the networking environment, and of configuration parameters such as the start-up delay, number of available video representations, etc., on the achievable streaming performance. Since, to the best of our knowledge, there exist no widely accepted or standard approach to measure QoE for BRAS, we alternatively maximize the average video bit-rate, minimize the number of quality switches, and impose a hard constraint on the absence of rebuffering events. Further, we evaluate two HAS clients, Microsoft SmoothStreaming and our own streaming client that supports the recently adopted HAS standard Dynamic Adaptive Streaming over HTTP (DASH), in an indoor Wireless Local Area Network (WLAN) emulated with a high degree of precision. We compare their performance with the optimal client, and explore the configuration parameter space of the DASH client. Finally, we evaluate the impact of start-up delays and number of available video representations on achievable streaming performance.

Adaptive Frame Interpolation for Wyner-Ziv Video Coding

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.

Parametric model for audiovisual quality assessment in IPTV: ITU-T Rec. P.1201.2

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 Pearsons 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.