Professor Raouf Hamzaoui

Job: Professor in Media Technology

Faculty: Technology

School/department: School of Engineering and Sustainable Development

Research group(s): Centre for Electronic and Communications Engineering (CECE)

Address: De Montfort University, The Gateway, Leicester, LE1 9BH

T: +44 (0)116 207 8096

E: rhamzaoui@dmu.ac.uk

W: www.tech.dmu.ac.uk/~hamzaoui/

 

Personal profile

Raouf Hamzaoui received the MSc degree in mathematics from the University of Montreal, Canada, in 1993, the Dr.rer.nat. degree from the University of Freiburg, Germany, in 1997 and the Habilitation degree in computer science from the University of Konstanz, Germany, in 2004. He was an Assistant Professor with the Department of Computer Science of the University of Leipzig, Germany and with the Department of Computer and Information Science of the University of Konstanz. In September 2006, he joined DMU where he is a Professor in Media Technology and Head of the Signal Processing and Communications Systems Group in the Institute of Engineering Sciences. Raouf Hamzaoui is an IEEE Senior member. He is a member of the Editorial Board of the IEEE Transactions on Multimedia. He has published more than 80 research papers in books, journals, and conferences. His research has been funded by the EU, DFG, Royal Society, and industry and received best paper awards (ICME 2002, PV’07, CONTENT 2010, MESM’2012).

Research group affiliations

Institute of Engineering Sciences (IES)

Context, Intelligence and Interaction Research Group (CIIRG)

Publications and outputs 

  • SUR-Net: Predicting the Satisfied User Ratio Curve for Image Compression with Deep Learning
    SUR-Net: Predicting the Satisfied User Ratio Curve for Image Compression with Deep Learning Fan, Chunling; Lin, Hanhe; Hosu, Vlad; Zhang, Yun; Jiang, Qingshan; Hamzaoui, Raouf; Saupe, Dietmar The Satisfied User Ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the probability distribution of the Just Noticeable Difference (JND) level, the smallest distortion level that can be perceived by a subject. We propose the first deep learning approach to predict such SUR curves. Instead of the direct approach of regressing the SUR curve itself for a given reference image, our model is trained on pairs of images, original and compressed. Relying on a Siamese Convolutional Neural Network (CNN), feature pooling, a fully connected regression-head, and transfer learning, we achieved a good prediction performance. Experiments on the MCL-JCI dataset showed a mean Bhattacharyya distance between the predicted and the original JND distributions of only 0.072. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Interactive subjective study on picture-level just noticeable difference of compressed stereoscopic images
    Interactive subjective study on picture-level just noticeable difference of compressed stereoscopic images Fan, Chunling; Zhang, Yun; Hamzaoui, Raouf; Jiang, Qingshan The Just Noticeable Difference (JND) reveals the minimum distortion that the Human Visual System (HVS) can perceive. Traditional studies on JND mainly focus on background luminance adaptation and contrast masking. However, the HVS does not perceive visual content based on individual pixels or blocks, but on the entire image. In this work, we conduct an interactive subjective visual quality study on the Picturelevel JND (PJND) of compressed stereo images. The study, which involves 48 subjects and 10 stereoscopic images compressed with H.265 intra coding and JPEG2000, includes two parts. In the first part, we determine the minimum distortion that the HVS can perceive against a pristine stereo image. In the second part, we explore the minimum distortion that each subject perceives against a distorted stereo image. Modeling the distribution of the PJND samples as Gaussian, we obtain their complementary cumulative distribution functions, which are known as Satisfied User Ratio (SUR) functions. Statistical analysis results demonstrate that the SUR is highly dependent on the image contents. The HVS is more sensitive to distortion in images with more texture details. The compressed stereoscopic images and the PJND samples are collected in a data set called SIAT-JSSI, which we release to the public.
  • Model-based encoding parameter optimization for 3D point cloud compression
    Model-based encoding parameter optimization for 3D point cloud compression Liu, Qi; Yuan, Hui; Hou, Junhui; Liu, Hao; Hamzaoui, Raouf Rate-distortion optimal 3D point cloud compression is very challenging due to the irregular structure of 3D point clouds. For a popular 3D point cloud codec that uses octrees for geometry compression and JPEG for color compression, we first find analytical models that describe the relationship between the encoding parameters and the bitrate and distortion, respectively. We then use our models to formulate the rate-distortion optimization problem as a constrained convex optimization problem and apply an interior point method to solve it. Experimental results for six 3D point clouds show that our technique gives similar results to exhaustive search at only about 1.57% of its computational cost.
  • Energy-based decision engine for household human activity recognition
    Energy-based decision engine for household human activity recognition Vafeiadis, Anastasios; Vafeiadis, Thanasis; Zikos, Stelios; Krinidis, Stelios; Votis, Konstantinos; Giakoumis, Dimitrios; Ioannidis, Dimosthenis; Tzovaras, Dimitrios; Chen, Liming; Hamzaoui, Raouf We propose a framework for energy-based human activity recognition in a household environment. We apply machine learning techniques to infer the state of household appliances from their energy consumption data and use rulebased scenarios that exploit these states to detect human activity. Our decision engine achieved a 99.1% accuracy for real-world data collected in the kitchens of two smart homes.
  • Acoustic scene classification: from a hybrid classifier to deep learning
    Acoustic scene classification: from a hybrid classifier to deep learning Vafeiadis, Anastasios; Kalatzis, Dimitrios; Votis, Konstantinos; Giakoumis, Dimitrios; Tzovaras, Dimitrios; Chen, Liming; Hamzaoui, Raouf This report describes our contribution to the 2017 Detection and Classification of Acoustic Scenes and Events (DCASE) challenge. We investigated two approaches for the acoustic scene classification task. Firstly, we used a combination of features in the time and frequency domain and a hybrid Support Vector Machines - Hidden Markov Model (SVM-HMM) classifier to achieve an average accuracy over 4-folds of 80.9% on the development dataset and 61.0% on the evaluation dataset. Secondly, by exploiting dataaugmentation techniques and using the whole segment (as opposed to splitting into sub-sequences) as an input, the accuracy of our CNN system was boosted to 95.9%. However, due to the small number of kernels used for the CNN and a failure of capturing the global information of the audio signals, it achieved an accuracy of 49.5% on the evaluation dataset. Our two approaches outperformed the DCASE baseline method, which uses log-mel band energies for feature extraction and a Multi-Layer Perceptron (MLP) to achieve an average accuracy over 4-folds of 74.8%.
  • Standalone closed-form formula for the throughput rate of asynchronous normally distributed serial flow lines
    Standalone closed-form formula for the throughput rate of asynchronous normally distributed serial flow lines Aboutaleb, Adam; Kang, Parminder Singh; Hamzaoui, Raouf; Duffy, A. P. Flexible flow lines use flexible entities to generate multiple product variants using the same serial routing. Evaluative analytical models for the throughput rate of asynchronous serial flow lines were mainly developed for the Markovian case where processing times, arrival rates, failure rates and setup times follow deterministic, exponential or phase-type distributions. Models for non-Markovian processes are non-standalone and were obtained by extending the exponential case. This limits the suitability of existing models for real-world human-dependent flow lines, which are typically represented by a normal distribution. We exploit data mining and simulation modelling to derive a standalone closed-form formula for the throughput rate of normally distributed asynchronous human-dependent serial flow lines. Our formula gave steady results that are more accurate than those obtained with existing models across a wide range of discrete data sets. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Audio-based Event Recognition System for Smart Homes
    Audio-based Event Recognition System for Smart Homes Vafeiadis, Anastasios; Votis, Konstantinos; Giakoumis, Dimitrios; Tzovaras, Dimitrios; Chen, Liming; Hamzaoui, Raouf Building an acoustic-based event recognition system for smart homes is a challenging task due to the lack of high-level structures in environmental sounds. In particular, the selection of effective features is still an open problem. We make an important step toward this goal by showing that the combination of Mel-Frequency Cepstral Coefficients, Zero- Crossing Rate, and Discrete Wavelet Transform features can achieve an F1 score of 96.5% and a recognition accuracy of 97.8% with a gradient boosting classifier for ambient sounds recorded in a kitchen environment.
  • Peer-to-Peer Live Video Streaming with Rateless Codes for Massively Multiplayer Online Games
    Peer-to-Peer Live Video Streaming with Rateless Codes for Massively Multiplayer Online Games Ahmad, Shakeel; Bouras, Christos; Buyukkaya, Eliya; Dawood, Muneeb; Hamzaoui, Raouf; Kapoulas, Vaggelis; Papazois, Andreas; Simon, Gwendal We present a multi-level multi-overlay hybrid peer-to-peer live video system that enables players of Massively Multiplayer Online Games to simultaneously stream the video of their game and watch the game videos of other players. Each live video bitstream is encoded with rateless codes and multiple trees are used to transmit the encoded symbols. Trees are constructed dynamically with the aim to minimize the transmission rate at the source while maximizing the number of served peers and guaranteeing on-time delivery and reliability. ns-2 simulations and real measurements on the Internet show competitive performance in terms of start-up delay, playback lag, rejection rate, used bandwidth, continuity index, and video quality. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Error-resilient packet-switched mobile video telephony with channel-adaptive rateless coding and early reference picture selection
    Error-resilient packet-switched mobile video telephony with channel-adaptive rateless coding and early reference picture selection Dawood, Muneeb; Hamzaoui, Raouf; Ahmad, Shakeel; Al-Akaidi, Marwan, 1959- Providing high-quality video for packet-switched wireless video telephony on handheld devices is a challenging task due to packet loss, varying bandwidth, and end-to-end delay constraints. While many error resilience techniques have been proposed for video transmission over wireless channels, only a few were specifically designed for mobile video telephony. We propose a low-complexity channel-adaptive error resilience technique for packet-switched mobile video telephony, which combines rateless coding, feedback, and reference picture selection. In contrast to previous approaches, our technique uses cumulative feedback at every transmission opportunity and predicts when decoding is likely to fail so that reference picture selection can be triggered at an early stage. Experimental results for H.264 video sequences show that the proposed technique can achieve improvements of 1.64 dB in peak signal-to-noise ratio over benchmark techniques in simulated Long-Term Evolution networks.
  • Temporal and Inter-view Consistent Error Concealment Technique for Multiview plus Depth Video
    Temporal and Inter-view Consistent Error Concealment Technique for Multiview plus Depth Video Khattak, Shadan; Maugey, Thomas; Hamzaoui, Raouf; Ahmad, Shakeel; Frossard, Pascal Multiview plus depth (MVD) is an emerging video format with many applications, including 3D television and free viewpoint television. During broadcast of compressed MVD video, transmission errors may cause the loss of whole frames, resulting in significant degradation of video quality. Error concealment techniques have been widely used to deal with transmission errors in video communication. However, the existing solutions do not address the requirement that the reconstructed frames be consistent with neighbouring frames, i.e., corresponding pixels have consistent color information. We propose a new consistency model for error concealment of MVD video that allows to maintain a high level of consistency between frames of the same view (temporal consistency) and those of neighbouring views (inter-view consistency). We then propose an algorithm that uses our model to implement concealment in a consistent way. Simulations with the reference software for the Multiview Video Coding project of the Joint Video Team (JVT) of the ISO/IEC MPEG and ITU-T VCEG show that our method outperforms benchmark techniques, including a baseline approach based on the Boundary Matching Algorithm, with respect to both reconstruction quality and view consistency.

Click here for a full listing of Raouf Hamzaoui's publications and outputs.

Key research outputs

  • Ahmad, S., Hamzaoui, R., Al-Akaidi, M., Adaptive unicast video streaming with rateless codes and feedback, IEEE Transactions on Circuits and Systems for Video Technology, vol. 20, pp. 275-285, Feb. 2010.
  • Röder, M., Cardinal, J., Hamzaoui, R., Efficient rate-distortion optimized media streaming for tree-structured packet dependencies, IEEE Transactions on Multimedia, vol. 9, pp. 1259-1272, Oct. 2007.  
  • Röder, M., Hamzaoui, R., Fast tree-trellis list Viterbi decoding, IEEE Transactions on Communications, vol. 54, pp. 453-461, March 2006.
  • Röder, M., Cardinal, J., Hamzaoui, R., Branch and bound algorithms for rate-distortion optimized media streaming, IEEE Transactions on Multimedia, vol. 8, pp. 170-178, Feb. 2006.
  • Stankovic, V., Hamzaoui, R., Xiong, Z., Real-time error protection of embedded codes for packet erasure and fading channels, IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, pp. 1064-1072, Aug. 2004.
  • Stankovic, V., Hamzaoui, R., Saupe, D., Fast algorithm for rate-based optimal error protection of embedded codes, IEEE Transactions on Communications, vol. 51, pp. 1788-1795, Nov. 2003.
  • Hamzaoui, R., Saupe, D., Combining fractal image compression and vector quantization, IEEE Transactions on Image Processing, vol. 9, no. 2, pp. 197-208, 2000.
  • Hamzaoui, R., Fast iterative methods for fractal image compression, Journal of Mathematical Imaging and Vision 11,2 (1999) 147-159.

 

Research interests/expertise

  • Image and Video Compression
  • Multimedia Communication
  • Error Control Systems
  • Image and Signal Processing
  • Pattern Recognition
  • Algorithms

Areas of teaching

Signal Processing

Image Processing

Data Communication

Media Technology

Qualifications

Master’s in Mathematics (Faculty of Sciences of Tunis), 1986

MSc in Mathematics (University of Montreal), 1993

Dr.rer.nat (University of Freiburg), 1997

Habilitation in Computer Science (University of Konstanz), 2004

Courses taught

Digital Signal Processing

Mobile Communication

Communication Networks

Signal Processing

Multimedia Communication

Digital Image Processing

Mobile Wireless Communication

Research Methods

Pattern Recognition

Error Correcting Codes

Membership of professional associations and societies

IEEE Senior Member

IEEE Signal Processing Society

IEEE Multimedia Communications Technical Committee 

Current research students

Mohamed Al-Ibaisi, PT, PhD student since January 2017

Thaeer Kobbaey, FT, PhD student since April 2014

Professional esteem indicators

Editorial Board Member IEEE Transactions on Multimedia (since 2017)

Technical Program Committee Co-Chair, IEEE MMSP 2017, London-Luton, Oct. 2017.

Editorial Board Member IEEE Transactions on Circuits and Systems for Video Technology (2010-2016)

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