Signal Processing, IEEE Transactions

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IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 51, NO. 4, APRIL 2003

Blind Quality Assessment System for Multimedia Communications Using Tracing Watermarking Patrizio Campisi, Member, IEEE, Marco Carli, Member, IEEE, Gaetano Giunta, Member, IEEE, and Alessandro Neri, Member, IEEE

Abstract—This paper presents a novel method to blindly estimate the quality of a multimedia communication link by means of an unconventional use of digital fragile watermarking. Data hiding by digital watermarking is usually employed for multimedia copyright protection, authenticity verification, or similar purposes. However, watermarking is here adopted as a technique to provide a blind measure of the quality of service in multimedia communications. Specifically, a fragile watermark is hidden in an MPEG-like host data video transport stream using a spread-spectrum approach. Like a tracing signal, the watermark tracks the data, where it is embedded, since both the watermark and the host data follow the same communication link. The estimation of the tracing watermark allows dynamically evaluating the effective quality of the provided video services. This depends on the whole physical layer, including the employed video co/decoder. The performed method is based on the evaluation of the mean-square-error between the estimated and the actual watermarks. The proposed technique has been designed for application to wireless multimedia communication systems. According to the results obtained, the sensitivity of the detected tracing watermark on the quality of service (QoS) indices provides for some useful capabilities for analyzing future mobile Universal Mobile Telecommunications System (UMTS) services. Index Terms—Multimedia communications, quality of service, UMTS services, video streaming, watermarking.

I. INTRODUCTION

I

N THE past few years, there has been an explosion in the use and distribution of digital multimedia data, essentially driven by the diffusion of the Internet. Currently, the use of electronic commerce applications and online services such as banking or booking and, more in general, services involving multimedia data are rapidly increasing. In the near future, third-generation mobile communication systems [IMT2000/Universal Mobile Telecommunications System (UMTS)] are expected to offer multimedia applications and services with negotiation end-to-end quality of service (QoS) [1], [2]. Therefore, it is necessary that service providers develop simple and effective billing systems related to the quality of the services supplied. It is then crucial to devise quality assessment systems that do not increase the bit rate transmission. Manuscript received February 4, 2002; revised November 19, 2002. This work was partially presented at the IEEE International Conference on Communications (ICC 2002), April 28-May 2, 2002, New York, NY. The associate editor coordinating the review of this paper and approving it for publication was Ton A. C. M. Kalker. The authors are with the Dipartimento Elettronica Applicata, Università di Roma TRE, I-00146 Roma, Italy (e-mail: [email protected]; [email protected]; [email protected]; [email protected]). Digital Object Identifier 10.1109/TSP.2003.809381

Fig. 1.

Watermark embedding process.

For this purpose, a novel quality assessment system has been developed. It is embedded into the data themselves and allows blind evaluation of the quality of the data after a coding/transmission process. To achieve this goal, an unconventional use of fragile watermarking is here proposed. Watermarking techniques have been devised to answer the ever-growing need to protect the intellectual property (copyright) of digital still images, video sequences, or audio from piracy attacks in a networked environment like the World Wide Web. This will allow a controlled distribution of multimedia data. Although copyright protection was the very first application of watermarking, different uses have been recently proposed in the literature. Fingerprinting, broadcast monitoring, data authentication, multimedia indexing, content-based retrieval applications, medical imaging applications [3], [4], etc., are only a few of the new applications where watermarking can be usefully employed. The general watermark embedding procedure is sketched in Fig. 1. It consists of embedding a watermark sequence, which is usually binary, into host data by means of a key. In the detection phase, the key is used to verify the presence of the embedded sequence. With regard to the domain where the watermark embedding occurs, we can distinguish methods operating in the spatial domain [5], in the DCT domain [6]–[9], in the Fourier transform domain [10], and in the wavelet transform domain [11]–[15]. When considering a watermarking scheme, depending on its specific application, different requirements need to be achieved. One of them is the perceptual invisibility of the superimposed “mark” onto the host data. This implies that the alterations caused by the watermark embedding into the data should not degrade their perceptual quality. Moreover, when these techniques are used to preserve the copyright ownership with the purpose of avoiding unauthorized data duplications, the embedded watermark should be detectable. This is required even if malicious attacks or nondeliberate modifications (i.e., filtering, compression, etc.) affect the embedded watermark. This requirement is known as watermark robustness.

1053-587X/03$17.00 © 2003 IEEE

CAMPISI et al.: BLIND QUALITY ASSESSMENT SYSTEM FOR MULTIMEDIA COMMUNICATIONS

However, when unwanted modifications of the watermarked data affect even the extracted watermark, the embedding scheme is known as fragile. Fragile watermarking [16]–[18] can be used to obtain information about the tampering process. In fact, it indicates whether or not the data has been altered and supplies localization information as to where the data was altered. In the proposed approach, an unconventional use of a fragile watermark to evaluate the QoS in multimedia mobile communications is presented. Specifically, a known watermark is superimposed onto the host data. The rational behind this approach is that by transmitting the watermarked data onto a channel, the mark undergoes the same alterations suffered by the data. At the receiving side, the watermark is estimated and compared with the original. Since the alterations endured by the watermark are likely to also be suffered by the entire data, as they follow the same communication link, the watermark degradation can be used to estimate the overall alterations endured by the data. The obtained experimental results show the capability of this unconventional use of watermarking techniques to trace the alterations suffered by the data through the communication channel. The quality assessment method presented here can be applied both to uncompressed and compressed video sequences. In the first case, the watermark embedding is performed on the data before coding, whereas in the second case, watermark embedding is performed on the coded bit stream. Therefore, the method proposed here can be used to estimate both the degradations introduced by the cascade coder-channel, as when the data are first coded and then transmitted through a nonideal channel, and the ones introduced only by the channel. In this paper, video sequences encoded by means of the MPEG-2 compression standard are considered as host data. In fact, MPEG-2 coded video sequences can be potentially distributed to mobile terminals in UMTS applications with the constantly growing number of multimedia applications and services required [19]. An object-oriented coder, such as MPEG-4, has been considered as well, thus providing an object-oriented QoS assessment evaluation system. The paper is organized as follows. In Section II, a brief description of the MPEG-2 [20] and MPEG-4 [21] video compression standards are provided. The quality assessment procedure is summarized in Section III. In Section IV, the embedding method is described. In Section V, the watermark estimation is detailed, and the employed metric for the quality-assessment procedure is introduced. Experimental results are provided in Section VI. Finally, conclusions are drawn in Section VII.

II. MPEG-2/4 VIDEO STANDARDS The MPEG-2 video bit stream has a layered syntax. In a top-bottom hierarchical structure, the video sequence is partitioned into multiple groups of pictures (GOPs), representing sets of video frames that are contiguous in display order. The next layer is constituted by a single frame, composed by one or more slices. Then, each slice contains one or more macro blocks, consisting of four luminance (Y) and two chrominance

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(U,V) blocks. Finally, the block is the basic coding unit of dimension 8 by 8 pixels. In order to obtain a high compression ratio, both spatial and temporal redundancies are exploited. The spatial redundancy is reduced by using subsampling of the chrominance components (U, V) in accordance with the sensitivity of the human visual system. Next, the discrete cosine transform (DCT) is performed on the blocks of the Y and U, V components. The DCT coefficients are quantized and finally encoded by using variable length coding. The temporal redundancy is reduced by temporally predicting some frames from other motion-compensated frames. The prediction error is then encoded. The MPEG-4 standard offers an object-based representation of a video. Specifically, MPEG-4 considers a scene to be composed by video objects (VOs), each described by motion, texture, and shape. Texture coding as well as motion estimation are based on the same principles used for MPEG-2 adapted to objects of arbitrary shape. Temporal instances of video objects are referred to as video object planes (VOPs). Three types of frames are used in the MPEG-2(4) standard: intra coded (I), temporally predicted (P), and bi-directionally predicted (B), frames or VOPs for MPEG-2 and MPEG-4 coding, respectively. • I-frames (I-VOPs), which are coded without any reference to other frames; • P-frames (P-VOPs), which are coded with reference to previous I or P; • B-frames (B-VOPs), which are coded with reference to both previous and next frames (VOPs). III. TRACING WATERMARKING PROCEDURE The principle scheme of the tracing watermarking procedure for coder-channel quality assessment is reported in Fig. 2. The watermark embedding is performed by resorting to the spreadspectrum technique proposed in [7] for still images and applied to video sequences in [8]. The watermark is a narrowband lowenergy signal. It is then spread over very many frequency bins of the image, which has a larger bandwidth. Consequently, the watermark energy contribution for each host frequency bin is negligible, which makes the watermark imperceptible. In the novel watermark application addressed here, a system embedded into the data stream that is able to trace the degradations introduced by the transmission system composed by the coder-channel cascade and not perceptually affecting the data themselves is described. Examining Fig. 2 more closely, a set of uncorrelated pseudorandom noise (PN) matrices is multiplied by the reference watermark. Both the PN matrices and the embedded watermark is known at the receiving side. It is worth noting that the watermark is the same for each video sequence frame, whereas the PN matrices are different for each frame. This assures that the spatial localization of the mark is different frame by frame so that the watermark visual persistency is negligible. After the randomization of the watermark by the PN matrices, the embedding of the tracing marks is performed in the DCT domain. Specifically, the watermark is embedded in the DCT middle-band frequencies of the whole image. After the inverse

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embedded, in the DCT domain, in the middle-high frequency region of . The embedding is performed in the DCT domain according to the following rule:

(2) represents the DCT of the th watermarked where , and is a scaling factor that determines the frame watermark strength. By increasing its value, the mark becomes more evident, and a visual degradation of the video occurs. On the contrary, by diminishing its value, the mark can be easily removed by the coder and/or channel’s errors. Therefore, in the application scenario proposed in this paper, must be chosen in such a way to compromise between the two aforementioned has requirements. In all our experiments, the value been used. The th watermarked frame is then obtained by performing the IDCT transform Fig. 2. Principle scheme of tracing watermarking for coder-channel quality assessment in multimedia communications.

IDCT

(3)

DCT (IDCT) has been performed on each frame, the whole sequence is coded by a video coder and, finally, transmitted. At the receiving side, the video is first decoded; then, from the DCT of each received frame of the sequence, a matched filter extracts the spread watermark, which is finally despread using the known PN matrices. After having extracted the received watermark, it is matched to the reference one, which is known at the receiving side, and the mean-square error (MSE), between the original mark and the received one, is used as an index of the degradation affecting the received watermark. Our approach takes into account the evaluation of the quality of the received video sequence since the mark and the overall video follows the same communication link.

Finally, the whole sequence is coded and then transmitted through a noisy channel.

IV. QoS SYSTEM EMBEDDING

The middle-high frequency region of embedding is selected. is multiplied by Then, the corresponding portion of , which is known at the receiving side, the watermark of the spread version thus obtaining an estimation of the watermark embedded in the th frame

Let us consider a generic video sequence composed by the frames of pixels. Moreover, let us indicate with the emand with ployed watermark, having dimensions the PN matrices of dimensions . The watermark that has been employed is a visual pattern, like a logo, consisting of binary pixels. are binary The PN matrices sequences generated according to methods that are well known in literature [22]. As in spread spectrum communications, they are employed to spread a narrowband signal (watermark) over a much larger bandwidth signal (host frame) in such a way that the watermark energy is undetectable in any single frequency of the host image. This can be expressed as follows: (1) the spread version of the wahaving indicated with termark to be embedded in the th frame. DCT the DCT Let us indicate with . The spread watermark is transform of the th frame

V. QoS EVALUATION In this Section, the watermarking extraction procedure is described and the metric employed for the QoS assessment is introduced. the reLet us indicate with undergoes the ceived video sequence. Each frame DCT transform, thus obtaining DCT

(4)

(5) The dispreading operation, for the generic th frame, is then performed as follows: (6) Finally, the watermark is estimated as follows by averaging the transmitted frames dispread watermark given by (6) over the (7) The QoS is evaluated by comparing the extracted watermark with respect to the original one. Although sophisticated video quality metrics could be used for video quality assessment purposes (see, for instance, [23] and [24]), in this paper, as a proof


of concept of the method proposed here, the MSE between the estimated watermark and the original one is used. Specifically, the MSE is first evaluated for the th frame as follows: (8)

MSE

Then, it is averaged over the frames under analysis, thus obtaining the employed metric MSE

MSE

(9)

It is worth noting that the metric (9), which is evaluated using the estimated watermarks over the transmitted frames, is employed to provide a quality assessment of the received video after a coding/transmission process. VI. EXPERIMENTAL RESULTS In this Section, some experimental results characterizing the effectiveness of the proposed method are presented. The dimensions of the video sequences employed in our experimentations have been properly chosen in order to simulate a multimedia service in a UMTS scenario. Therefore, QCIF (144 176) video sequences, which well match the limited dimensions of a mobile terminal’s display, have been employed. At the transmission side, the QoS system addressed in this paper is embedded into the video sequence, as detailed in Section IV. The video is then compressed, thus obtaining a coded bit stream. The marked video is then transmitted over a noisy channel that is simulated by a Poisson’s generator of random transmission errors. The presented QoS assessment procedure has been tested for both the MPEG-2 and the MPEG-4 coders. Different bit rates (1000, 600, 200, 100, and 50 Kb/s) have been considered in our experiments. In order to characterize the performances of the proposed method to provide a quality measure of the video received after the coding/transmission process, in Figs. 3 and 4, the MSE of the estimated watermark with respect to the original one versus the bit error rate (BER) for MPEG-2 coded video sequences at different compression ratio have been considered. It is worth noting that the MSE of the extracted watermark increases when the BER increases and the bit rate decreases. This is in accordance with the perceptual degradation that the video suffers at increasing BER and decreasing bit rate. As shown in Figs. 4 and 5, the quality degradation of the watermark embedded into the host video has the same behavior of the one affecting the video (see also Fig. 6). The procedure has been tested for the MPEG-4 coder as well [26]. Specifically, at the transmitting side, the video sequence is first segmented into VOs; then, known watermarks, one for each object, are superimposed onto each VO by means of the procedure described in Section IV. Then, the video sequence is MPEG-4 coded. Here, we report the results pertaining the video sequence “Mother and daughter.” Each frame is composed from two different objects: the “background” and the foreground “mother

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and daughter.” As shown in Figs. 7 and 8, the MSE of the extracted watermark for each VO increases when the BER increases and the bit rate decreases. This is in accordance with the results presented for MPEG-2 coded video sequences. The experimental results that have been presented validate the initial hypothesis that the watermark alterations can be used to evaluate the video degradation amount and the transfer quality. In a real-world system, the estimated quality of the received video can also be used by the service provider as a feedback information for billing purposes. This raises questions on the security of the system. Although this issue is beyond the purpose of this paper, it is worth pointing out that in order to prevent a fraudulent user to obtain any benefit from any false declaration about the QoS of the supplied service, a possible approach is specified as follows. In real-time interactive video communications, let us consider the situation when the mobile station (MS) declares a received quality lower than the provided one. This would imply that the channel is not suited for the current bit rate for the given BER, and therefore, the bit rate emitted by the base station is lowered in a few seconds. Whether the MS declares a null quality, the operator interrupts the call. Moreover, frequent declarations of poor or null quality is a valid reason for the admission call manager to refuse the access to further calls of the same user, at least until the MS has moved to a region with less noise or interference. As a consequence, the result of possibly false declarations on the QoS is to lower the bit rate or to cut the communication link in a few seconds, thus preventing any fraudulent action. It is worth pointing out that the MS (that is the end-user of the communication process) needs to implement the evaluation of the provided QoS. As a consequence, the MS must perform real-time processing. Although actual MS does not perform hardware and/or software processing, the authors believe that in a few years, MS will host large processing capabilities because of the monotonically decreasing cost of very large scale integration. Furthermore, different tasks could be accomplished by the MS such as on-board adaptive beamforming (spatio-temporal array processing), near-optimum multiuser RAKE reception, which is suggested to be employed even in downlink, and so on (e.g., see [27]). Moreover, in a wireless video communication scenario like UMTS, the coded video sequence has to be decoded at the receiving side by the MS. Therefore, the complexity of the QoS evaluation procedure, which is presented in this paper, appears negligible in comparison with MPEG-2/4 decoding and adaptive on-board array processing. VII. CONCLUDING REMARKS In this paper, an unconventional method of tracing watermarking as a hidden technique suited for estimating the QoS in multimedia mobile communications has been presented. In the proposed approach, a fragile (known) watermark is hidden into MPEG-like host data video transport stream. Spread-spectrum embedding techniques have been employed. Experimentations have shown that the error affecting the watermark is very sensitive to the channel bit error rate and to the compression ratio. The proposed method allows one to blindly estimate the QoS provided by a coder/channel system without affecting the

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Fig. 3. Watermark MSE (normalized to 1) versus the BER for the sequence “Akiyo” MPEG-2 coded at different compression ratio.

Fig. 6. Watermark MSE and video sequence MSE (normalized to 1) versus the BER for the sequence “Akiyo” MPEG-2 coded at 200 Kb/s.

Fig. 4. Watermark MSE (normalized to 1) versus the BER for the sequence “Suzie” MPEG-2 coded at different compression ratio.

Fig. 7. Watermark MSE (normalized to 1) versus the BER of the object “Mother and daughter” in the video sequence “mother and daughter,” MPEG-4 coded at different compression ratio.

Fig. 5. Watermark MSE and video sequence MSE (normalized to 1) versus the BER for the sequence “Akiyo” MPEG-2 coded at 600 Kb/s.

quality of the video-communications. In addition, we do not increase the bit rate for data transmission. In summary, the described method can be usefully employed in a multimedia communication scenario for providing 1) control feedback to the sending user on the effective quality of the link; 2) detailed information to the operator for billing purposes;

Fig. 8. Watermark MSE (normalized to 1) versus the BER of the object “background” in the video sequence “Mother and daughter,” MPEG-4 coded at different compression ratio.

3) diagnostic information to the operator on the effective status of the link. ACKNOWLEDGMENT The authors wish to thank the anonymous reviewers for the valuable comments, which helped to improve the paper.


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REFERENCES [1] F. Yong Li, N. Stol, T. T. Pham, and S. Andresen, “A priority-oriented QoS management framework for multimedia services in UMTS,” in Proc. Fourth Int. IEEE Symp. Wireless Pers. Multimedia Commun., Aalborg, Denmark, Sept. 9–12, 2001. [2] L. Hanzo, P. J. Cherriman, and J. Streit, Wireless Video Mommunication: Second to Third Generation Systems and Beyond, ser. IEEE Series on Digital and Mobile Communication. New York: IEEE, 2001. [3] Hanjalic, G. C. Langelaar, P. M. B. van Roosmalen, J. Biemond, and R. L. Lagendijk, Image and Video Databases: Restoration, Watermarking and Retrieval. New York: Elsevier, 2000. [4] I. J. Cox, M. L. Miller, and J. A. Bloom, Digital Watermarking. San Francisco, CA: Morgan Kaufmann, 2002. [5] N. Nikolaidis and I. Pitas, “Robust image watermarking in the spatial domain,” Signal Process., vol. 66, no. 3, pp. 385–403, May 1998. [6] M. Barni, F. Bartolini, V. Cappellini, and A. Piva, “A DCT-domain system for robust image watermarking,” Signal Process., vol. 66, no. 3, pp. 357–372, May 1998. [7] I. Cox, J. Kilian, F. Leighton, and T. Shamoon, “Secure spread spectrum watermarking for multimedia,” IEEE Trans. Image Processing, vol. 6, pp. 1673–1687, Dec. 1997. [8] H. Hartung and B. Girod, “Watermarking of uncompressed and compressed video,” Signal Process., vol. 66, pp. 283–301, 1998. [9] M. D. Swanson, B. Zhu, and A. H. Tewfik, “Transparent robust image watermarking transform,” in Proc. IEEE Int. Conf. Image Process., Lausanne, Switzerland, Sept. 16–19, 1996, pp. 211–214. [10] R. M. Wolfgang and E. J. Delp, “A watermark for digital images,” in Proc. IEEE Int. Conf. Image Process., Lausanne, Switzerland, Sept. 16–19, 1996, pp. 219–222. [11] R. Dugad, K. Ratakonda, and N. Ahuja, “A new wavelet-based scheme for watermarking images,” in Proc. IEEE Int. Conf. Image Process., Chicago, IL, Oct. 4–7, 1998, pp. 419–423. [12] D. Kundur and D. Hatzinakos, “Digital watermarking using multiresolution wavelet decomposition,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., vol. 5, 1998, pp. 2969–2972. [13] H. Inoue, A. Miyazaki, and T. Katsura, “An image watermarking method based on the wavelet transform,” in Proc. IEEE Int. Conf. Image Process., Kobe, Japan, Oct. 25–28, 1999, pp. 296–300. [14] H. J. M. Wang, P. C. Su, and C. C. J. Kuo, “Wavelet-based blind watermark retrieval technique,” in Proc. SPIE, Conf. Multimedia Syst. Applicat., vol. 3528, Boston, MA, Nov. 1998. [15] P. Campisi, A. Neri, and M. Visconti, “A wavelet based method for high frequency subbands watermark embedding,” in Proc. SPIE Multimedia Syst. Applicat. III, Boston, MA, Nov. 2000. [16] M. M. Yeung and F. Mintzer, “An invisible watermarking technique for image verification,” in Proc. IEEE Int. Conf. Image Process., Santa Barbara, CA, 1997, pp. 680–683. [17] D. Kundur and D. Hatzinakos, “Toward a telltale watermarking technique for tamper-proofing,” in Proc. IEEE Int. Conf. Image Process., Chicago, IL, Oct. 4–7, 1998, pp. 409–413. [18] R. H. Wolfgang and E. J. Delp, “Fragile watermarking using the VW2D watermark,” in Proc. SPIE, Security Watermarking Multimedia Contents, vol. 3657, San Jose, CA, Jan. 1999. [19] P. D. F. Correira, S. M. M. Faria, and P. A. A. Assunção, “Matching MPEG-1/2 coded video to mobile applications,” in Proc. Fourth Int. IEEE Symp. Wireless Pers. Multimedia Commun., Aalborg, Denmark, Sept. 9–12, 2001. [20] “Information technology—Coding of moving pictures and associated audio for digital storage media at up to about 1.5 Mbit/s—Part 2: Video,” ISO, ISO/IEC 11 172-2, 1993. [21] T. Ebrahimi, “MPEG-4 video verification model: A video encoding/decoding algorithm based on content representation,” Signal Process.: Image Commun., pp. 367–384, 1997. [22] R. Gold, “Optimal binary sequences for spread spectrum multiplexing,” IEEE Trans. Inform. Theory, vol. IT-13, pp. 619–621, Oct. 1967. [23] K. T. Tan, M. Ghanbari, and D. E. Pearson, “An objective measurement tool for MPEG video quality,” Signal Process., vol. 70, no. 3, pp. 279–294, 1998. [24] S. Winkler, “Visual fidelity and perceived quality: Toward comprehensive metrics,” Proc. SPIE, vol. 4299, 2001. [25] P. Campisi, M. Carli, G. Giunta, and A. Neri, “Tracing watermarking for multimedia communication quality assessment,” in Proc. IEEE Int. Conf. Commun., New York, Apr.–May 28–2, 2002. [26] P. Campisi, G. Giunta, and A. Neri, “Object based quality of service assessment for MPEG-4 videos using tracing watermarking,” in Proc. IEEE Int. Conf. Image Process., Rochester, NY, Sept. 22–25, 2002. [27] H. Liu, Signal Processing Applications in CDMA Communications. Norwell, MA: Artech House, 2000.

Patrizio Campisi (M’99) received the “Laurea” degree in electrical engineering, summa cum laude from the University of Roma “La Sapienza,” Roma, Italy, and the Ph.D. degree in electrical engineering from the University of Roma “Roma Tre,” Roma, in 1995 and 1999, respectively. He is an Assistant Professor with the Department of Applied Electronics, University of Roma “Roma Tre,” where he has been also lecturer for the graduate course “Signal Theory” since 1998. From September 1997 until April 1998, he was a visiting research associate with the Communication Laboratory, University of Toronto, Toronto, ON, Canada, and from July 2000 until December 2000, he was a post doctoral fellow with the same laboratory. From October 1999 to October 2001, he held a post doctoral position at the University of Roma “Roma Tre.” His research interests are in the area of digital signal and image processing with applications to multimedia communications. He is a reviewer for the Journal of the Optical Society of America and the EURASIP Journal on Applied Signal Processing. Dr. Campisi was a member of the Technical Committee of the IEEE International Conference on Multimedia and Expo 2002 (ICME 2002), and he is member of the Technical Committee of the IEEE International Conference on Multimedia and Expo 2003 (ICME 2003). He is a Member of the IEEE Communications and Signal Processing Society and the Italian Professional Engineers association. He is a reviewer for the IEEE TRANSACTIONS ON SIGNAL PROCESSING and the IEEE SIGNAL PROCESSING LETTERS.

Marco Carli (M’97) received the “Laurea” degree in telecommunication engineering from the University of Rome, “La Sapienza,” Rome, Italy, in 1996. He was with Datamat—Systems Engineering Company until 1997. Since 1997, he has been involved in European Union international programs in Distance Education. In 2000, he was a Visiting Researcher with the Image Processing Laboratory, directed by Prof. S. Mitra, at the University of California, Santa Barbara (UCSB). He is currently an associate researcher with the University of Rome, “Roma Tre,” and he is an Academic Visiting Researcher with the Image Processing Laboratory, UCSB. His research interests are in the area of digital signal and image processing and in multimedia communications. He is a reviewer for the the Journal of Visual Communication and Image Representation. Dr. Carli is a reviewer for many conferences and for the IEEE TRANSACTIONS ON IMAGE PROCESSING.

Gaetano Giunta (M’88) was born in Messina, Italy, in 1959. He received the Dr.Eng. degree in electronic engineering from the University of Pisa, Pisa, Italy, in 1985 and the Ph.D. degree in information and communication engineering from the University of Rome “La Sapienza,” Rome, Italy, in 1990. In 1986, he received a research grant from the Italian Research Council (CNR) of the Institute of Information Processing (IEI), Pisa. He was also (since 1989) a research fellow of the Signal Processing Laboratory (LTS), Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. In 1992, he became an Assistant Professor with the INFO-COM Department, University of Rome “La Sapienza.” Since 1998, he has taught Digital Signal Processing with the Third University of Rome. Since 2001, he has been with the Third University of Rome as an Associate Professor of telecommunications. In particular, he currently teaches digital signal processing, signal processing for telecommunications, and random signal theory. His research interests include signal processing for communications, time-delay and motion estimation, multimedia communications, and spread-spectrum systems. He has been a reviwer of the journals Signal Processing, Proceedings of the Institute of Electrical Engineers (Proc. IEE)—Vision, Image and Signal Processing, and Proc. IEE—Radar, Sonar, and Navigation. Prof. Giunta is a member of the IEEE Communications, Signal Processing, and Vehicular Technology Societies. He has also served as a reviewer the the IEEE TRANSACTIONS ON COMMUNICATIONS, the IEEE TRANSACTIONS ON MULTIMEDIA, the IEEE TRANSACTIONS ON SIGNAL PROCESSING, and the IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY.

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Alessandro Neri (M’82) was born in Viterbo, Italy, in 1954. He received the doctoral degree in electronic engineering from the University of Rome “La Sapienza,” Rome, Italy, in 1977. In 1978, he joined the Research and Development Department of Contraves Italiana S.p.A., where he gained a specific expertise in the field of radar signal processing and in applied detection and estimation theory, becoming the chief of the advanced systems group. In 1987, he joined the INFOCOM Department, University of Rome “La Sapienza,” as an Associate Professor of signal and information theory. In November 1992, he joined the Electronic Engineering Department of the University of Rome III as Associate Professor of electrical communications and became Full Professor of telecommunications in Semptember 2001. Since 1992, he has been responsible for coordination and management of research and teaching activities in the telecommunication field at the University of Rome III. Since 1998, he has also been responsible for planning and design activities related to university campus telecommunication systems and services. His research activity has mainly been focused on information theory, signal theory, and signal and image processing and their applications to both telecommunications systems and remote sensing. His current research is focused on third- and fourth-generation cellular systems and multimedia communications. Since 1997, he has also been involved in several reserach programs connected with the use of information technologies in distance learning.
