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INTERACTIVE 3D VIDEO STREAMING
Peer-to-Peer System Design for Adaptive 3D Video Streaming C. Göktug Gürler and Murat Tekalp, Koç University
ABSTRACT 3D video is destined to be available in homes and mobile devices. Stereoscopic TV broadcasts have already begun in frame-compatible format for stereoscopic 3D. The natural next step is to deliver 3D content in the form of multiview video (MVV) that enables a natural glasses-free 3D experience. Unfortunately, the number of views needed to drive multiview displays varies depending on the price vs. quality trade-off. Therefore, the bitrate requirement of MVV content changes according to users’ display technology, making transmission over fixed bit rate channels inefficient. IP provides a flexible transport mechanism for 3D content; however, wellknown problems such as fluctuations in available link capacity and varying transmission delays pose challenges to 3D video services over the Internet. In this study, we discuss quality-ofexperience-aware rate adaptation methods specific to 3D video and efficient encoding schemes. Then, we introduce a framework to design P2P overlays to deliver 3D video. P2P overlays offer a promising approach to alleviate the high bandwidth requirement of MVV. Furthermore, two use case scenarios are provided to show the discussed methods can help to make 3D video delivery practical over the Internet.
INTRODUCTION Stereoscopic 3D has had a significant impact on the movie industry, and public interest in 3D content has increased over the last decade. At present, broadcast standards exist for stereoscopic 3D in a frame compatible format, where frames are subsampled to keep the size the same as in conventional 2D video. Although frame compatible formats have created a seamless solution for transmission of stereoscopic 3D video, there are serious drawbacks with this approach. First, the perceived visual quality can be inferior compared to 2D video because the resolution of individual views is lower. Second, the necessity of wearing glasses is a burden on viewers. There are alternative solutions that use lenticular sheet technology; however, such autostereoscopic displays have sweet spots, which are narrow and difficult to keep aligned all the time. Finally, the viewing angle of stereoscopic 3D is fixed to a single point of view, and users do not
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have the free-view capability that presents a scene from different perspectives. Therefore, 3D is destined to be available in homes and mobile devices in the form of multiview video (MVV). Multiview displays allow users to experience natural depth perception without wearing special glasses and can present a scene from multiple angles. With each additional view, it is possible to cover a wider range until the display’s limitation is reached. Therefore, the required number to drive multiview auto-stereoscopic displays is not fixed but depends on the display technology (based on the price vs. quality trade-off). The requirement to transmit varying numbers of views according to users’ display capability is a key challenge in the transmission of MVV contents because the traditional broadcast standards such as digital video broadcasting (DVB) operate over fixed bit rate channels, assuming that there exists an upper bound for the bit rate of the content. This is not the case with multiview video. Fortunately, the Internet can serve as a flexible platform to deliver MVV as it naturally supports varying bit rates. With simple modifications, IPTV and WebTV applications can serve as many views as needed. Moreover, with the increasing availability of mobile platforms, the IP network can serve as a medium to deliver MVV to home and mobile users. However, due to the large bandwidth requirement of MVV contents, it may become difficult to achieve service scalability against increasing numbers of recipients. Therefore, the classical server-client model may not adequately address all the challenges of MVV delivery over IP. On the other hand, peer-to-peer (P2P) overlays can distribute the task of data forwarding over peers and alleviate the problem of high bandwidth requirements. IP does not guarantee quality of service and poses serious challenges to video streaming applications that must deliver time-sensitive multimedia content at a desired quality of experience (QoE). Adaptive streaming is a key technology to handle IP artifacts such as varying link capacity as it can match source video rate to available capacity. Rate adaptation is more essential in P2P video streaming because peers have limited upload capacities, making connections more prone to rate fluctuations. This study presents the steps to create an adaptive streaming platform for the transmission
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of 3D video over the Internet. We introduce QoE-aware rate adaptation methods that minimize the perceived quality degradation while decreasing the source video rate. We describe efficient encoding schemes that utilize the adaptation methods. These approaches are applicable to any streaming architecture; however, we focus on a P2P solution and provide an example architecture. Following that, we present two illustrative scenarios for 3D video services over the Internet. Finally, we draw conclusions.
QUALITY-OF-EXPERIENCE-AWARE RATE ADAPTATION METHODS FOR 3D VIDEO QoE-aware rate adaptation corresponds to adapting source video bit rate in a graceful manner and minimizing the effect of quality degradation on perceived quality. Table 1 provides the list of some possible rate adaptation methods for both 2D and 3D video. For monoscopic (2D) video, available rate adaptation methods are mostly determined by the adopted video codec. For instance, stream switching is possible if content is encoded multiple times at different qualities (e.g., using different quantization parameters), and the resultant elementary bitstreams contain switching frames that are introduced with the H.264/AVC standard. More advanced rate adaptation techniques such as layer switching are possible with the scalable video coding (SVC) extension of H.264/AVC [1]. When SVC is adopted, enhancement layers are discarded (switched to a lower quality) to match content bit rate to available link capacity without introducing significant artifacts. The behavior of the human visual system is another paradigm for QoE-aware rate adaptation. One common application is to use fewer bits for chrominance components than for luminance because humans are more sensitive to changes in brightness. It is possible to apply this approach to 3D content and achieve higher perceived quality using asymmetric rate scaling. It is also possible to perform view scaling, which corresponds to discarding some views in the case of MVV and interpolate them using depth-imagebased rendering (DIBR) techniques if possible. In this section, we explain these two methods specifically pertaining to 3D video.
QOE-AWARE ADAPTATION METHODS FOR STEREOSCOPIC 3D VIDEO: ASYMMETRIC QUALITY ALLOCATION Studies on perception of stereoscopic 3D video have shown that the human visual system can tolerate lack of high-frequency components as long as that information is present in one of the views. This finding suggests that there is perceptual redundancy among views and that the best perceived quality may be achieved by assigning asymmetric quality among view pairs to minimize redundant information. Stelmach et al. [2] have compared spatial and temporal asymmetry, and concluded that applying spatial asymmetry between right and left stereo-pairs using low-pass
IEEE Communications Magazine • May 2013
Content
Basic adaptation method
Advanced adaptation method
Monoscopic video
Stream switching
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Stereoscopic 3D
Symmetric rate scaling
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Multiview 3D Video
View scaling
Assisted view scaling
Table 1. Adaptation methods for video streaming.
filtering causes only minor artifacts. In further research, it is claimed that the best 3D stereo video quality may be achieved by using asymmetric video coding in which views are coded at unequal quality levels [3]. In the following, we provide the steps to obtain the best perceived quality at a given bit rate for stereoscopic 3D. There is a peak signal-to-noise ratio (PSNR) threshold for asymmetric coding such that the perceived 3D stereo video quality is dominated by the higher-quality view, and artifacts are perceptually unnoticeable. To justify this proposition and determine the so-called just-noticeable asymmetry level in terms of a PSNR threshold, Saygili et al. [4] has conducted subjective tests and recorded perceived quality scores of various stereo video pairs at different unequal quality levels. The test results indicate that if the PSNR value of the low-quality view is above ~32 dB, assessors will not notice the quality degradation in one view, and the overall perception of the content is at the quality of the high-quality pair. It has also been noted that the ~32 dB threshold is roughly where blockiness artifacts start to appear due to video coding, which suggests that such artifacts may not be concealed by the human visual system.
QOE-AWARE ADAPTATION METHODS FOR MULTIVIEW VIDEO: VIEW SCALING When performing rate adaptation while transmitting stereoscopic 3D video content, it is possible to exploit features of the human visual system and introduce asymmetry among the quality of views. However, discarding one view entirely and falling back to 2D video is not a good choice because switching from 3D to 2D results in significant viewing discomfort and decreases users’ QoE. With the MVV format, view scaling becomes a possible option because the missing view(s) may be outside of the user’s field of view or can be replayed by an artificial view generated at the client side. To this end, one possible method is to use DIBR techniques in which color images are transmitted with associated depthmaps. Depthmaps are single-channel frames that represent the distance of pixels in a color view from the camera position [5]. Since depth information has lower complexity, depthmaps can be compressed with higher efficiency tha can color views. Therefore, it is possible to discard a color view and introduce multiple depthmaps to decrease the overall bit rate of the content for rate adaptation. There are fewer studies regarding QoE-aware rate adaptation for multiview contents. In one of
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may have almost no effect over the perceived quality, while discarding some others may lead to significant artifacts.
these studies, Sedef et al. [6] evaluate symmetric and asymmetric rate scaling and compare those methods against view scaling with various number of reference frames (e.g., using two reference frames at high quality or using three reference frames at lower quality). For the evaluation, test sequences are scaled to match certain target bit rate values using all available methods, and then users assess the perceived video quality over the resultant sequences. The test results indicate that using asymmetric rate scaling provides the best QoE for moderate bit rate reduction, whereas using DIBR techniques with few but high-quality reference frames are suggested for more aggressive cuts in content bit rate. One important aspect of view scaling is to determine which view should be discarded for minimum degradation in perceived quality. Based on the user’s region of interest, some views may have almost no effect on the perceived quality, while discarding some others may lead to significant artifacts. Similarly, the enhancement layer of one view can be more important than the base layer of another view if it is outside the user’s field of view. Such decisions are only possible while streaming MVV. In both cases, using system information like head position would significantly increase the chances of minimizing the impact of view scaling over the perceived quality.
method, the base layer of each view is encoded with a quality of about ~32 dB, and the enhancement layers are encoded at the maximum quality according to channel capacity. If available link capacity requires rate scaling, the enhancement layer of one of the views can be discarded, resulting in asymmetric quality among views. According to the above theory, the perceived quality degradation would be very limited since both views are above the threshold value. In a second method, only one view (the first) is scalably encoded as depicted in Fig. 1b. The second view is encoded using non-scalable H.264/AVC that has higher rate distortion performance. The gain in bit rate is used to improve the quality of the first view. In this approach, asymmetry is always exploited. When the available link capacity is high, the scalable coded view (with the enhancement layer) becomes the high-quality view. In the other case, the enhancement layer is discarded, and the second view becomes the higher-quality view as depicted in Fig. 1c. A recent study [7] compares the two encoding schemes and concludes that the latter delivers superior perceived quality assuming that perceived quality is dominated by the high-quality view in the stereo pair as implied in [4].
ADAPTATION READY ENCODING SCHEMES FOR 3D VIDEO
We provide the following encoding scheme as an example encoding scheme for a five-view display, which is the minimum number of views for us to cover all cases. In order to maximize rate-distortion performance, we use both H.264/AVC and SVC codecs. Figure 2 depicts the choice of streams and codecs for three different target bitrates. For utilizing asymmetric coding, we need to introduce quality difference between adjacent views. The most efficient way to perform this is to use SVC for views 2 and 4. If the quality of base layer streams is higher than ~32 dB, it is possible to utilize human visual theory and minimize the effect of rate scaling when the enhancement layers are discarded. All the remaining views are encoded using the H.264/AVC because these streams are either received or discarded and do not need any enhancement layers. Therefore, view scaling simply refers to discarding a particular bitstream.
The encoding scheme for 3D video should deliver the best rate-distortion performance while allowing the adaptation methods introduced previously. For stereoscopic 3D video, the encoding scheme consists of taking advantage of asymmetric coding to achieve the best QoE for a given bit rate. For the MVV case, the scheme should allow both asymmetric coding and view scaling using DIBR techniques with high-quality reference views. In the following, we describe such encoding schemes.
ASYMMETRIC ENCODING FOR STEREOSCOPIC 3D VIDEO Asymmetric scalable coding can be done in two ways: the first method is to encode both views using SVC, as depicted in Fig. 1a. In this
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VIDEO-PLUS-DEPTH CODING FOR MULTIVIEW VIDEO
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Figure 2. Stream selection for QoE aware rate adaption (v: view stream d: depthmap stream): a) maximum rate; b) medium rate; c) minimum rate.. When the available link capacity is high, it is possible to use the streams depicted in Fig. 2a in which all views are delivered at maximum quality. In the case where rate reduction is needed, the enhancement layers of intermediate views (views 2 and 4) are discarded first (Fig. 2b). If further reduction is needed, only the edge views and their corresponding depthmaps are streamed; the missing views are interpolated using DIBR at the client side (Fig. 2c).
SYSTEM DESIGN FOR ADAPTIVE P2P 3D VIDEO STREAMING In the previous sections, we have introduced efficient rate scaling methods for adaptive 3D video streaming. In this section, we explain the steps of creating a P2P video streaming architecture to illustrate a system that takes advantage of such methods. We start with the original BitTorrent protocol, and then modify chunk generation and chunk scheduling procedures to build a P2P solution for QoE-aware 3D video streaming.
REVIEW OF THE BITTORRENT PROTOCOL The BitTorrent protocol is designed to distribute large files over the Internet using P2P overlays. It adopts a mesh-based topology and flat connections with no hierarchy among peers. Thanks to these features, the BitTorrent protocol is more robust and efficient than its predecessors. The protocol became so successful that it constituted the largest portion of global IP traffic until 2011, when is was overtaken by video streaming applications [8]. Inspired by its success, there have been many proposals to modify BitTorrent and perform P2P video streaming. BiToS [9] and Tribler [10] primarily focus on modifying the chunk scheduling policy to enable 2D video streaming. NextShare [11] extends the same method to enable adaptive streaming by creating discardable video chunks using SVC encoding. The BitTorrent protocol adopts a divide-andconquer approach and splits content into equally sized chunks, which form the basic transmission unit that peers exchange. Based on the number of chunks they have, peers are of two types: seeders have the whole content and upload chunks to other peers, while leechers have some missing chunks, which they try to receive via chunk exchanges. In the BitTorrent protocol, chunk exchange is managed by two governing policies. The first policy is rarest-first chunk scheduling, which determines the chunks to be
IEEE Communications Magazine • May 2013
requested. It favors chunks that are least distributed in the swarm, since rare chunks are more likely to be used when trading with neighbors. The second policy is tit-for-tat, which determines which chunk requests are to be accepted. Peers that adopt tit-for-tat policy sorts their neighbors based on their level of contribution and may deny requests from neighbors at lower ranks. One exception is the optimistic unchoking policy, in which peers accept requests from a “lucky” neighbor even if it has not provided any data yet in order to provide leverage to new peers [12]. In the following, we provide further modifications that both increase the performance of video streaming and introduce 3D support. We particularly focus on chunk download and chunk upload policies, and base the architecture on the BitTorrent protocol.
topology and flat connections with no hierarchy among peers. Thanks to these features, the BitTorrent protocol is more robust and efficient than its predecessors.
MODIFICATIONS TO BITTORRENT TO ENABLE ADAPTIVE 3D VIDEO STREAMING Chunk Mapping: Variable-Size Layered Chunks — Chunk generation is the procedure that maps an elementary video stream to P2P chunks. The BitTorrent protocol and its derivatives for video streaming use chunks that have a fixed bit length. However, the network abstraction layers (NALs), which are the basic units of an encoded elementary stream, do not have a fixed length and therefore cannot align with chunk boundaries. Consequently, the loss of a single chunk is more likely to affect some other chunks even if those have been received. Using variable-length chunks that contain multiple groups of pictures (independently decodable segments of an elementary stream) minimizes the effect of chunk loss events because all chunks are self-decodable [13]. Moreover, when chunks are composed of groups of pictures, the chunks have fixed duration. This permits the duration of the video buffer to be computed accurately and perform rate adaptation, which we cover in the following subsections. Adaptive 3D Video Streaming — In the streaming of monoscopic video, rate adaptation is simply achieved by discarding the enhancement layer. However, in 3D video streaming, rate adaptation is not straightforward and may depend on external information such as the user’s field of view, the encoding scheme, and the display properties. We define an external module (adaptation module) that is responsible for evaluating such information. As an extreme
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Figure 3. System design for adaptive 3D video streaming.
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case, an adaptation module may decide that a “discardable” enhancement layer of a particular view can be more important than the base layer of another view if it is not in the user’s field of view. The input of the adaptation module is all the available external information. The output is simply the prioritization list of the video streams as depicted in Fig. 3. Here, video stream refers to a particular layer of a particular view or depthmap. In this modular design, the role of the P2P engine is to determine when to perform rate adaptation (discard/add a stream), whereas it is the task of the adaptation module to determine which streams should be affected first by the prioritization of video streams. This approach strengthens modularity of the design; the P2P engine, which is responsible for handling network related issues, does not get involved in parsing external information. Chunk Downloading: Rate Adaptation Using Buffer Duration — The duration of the ready-to-play buffer represents the time that the player can decode without any interruption event if no additional chunks are received. The buffer duration is a variable that provides feedback on the overall content retrieval rate. It is the duration between the last consumed chunks until the first missing chunk. Figure 4 depicts a sample scheduling window of size 4 (chunks). The chunks at time t = 1, t = 2, t = 3, and t = 4 are downloaded,
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but the chunks at time t = 5 are missing (dashed border). The video player has consumed the first chunk (t = 1, lighter boxes). In such a case, the ready-to-play buffer is equal to the duration of three chunks. The vertical dimension contains all video streams; the one with lowest priority is on the top. Increasing buffer duration indicates that the download rate is higher than the content rate, whereas low buffer duration means that the link capacity is not sufficient to support the current quality. Then, according to buffer duration, the P2P engine can decide when to perform rate adaptation. For instance, if buffer duration is critically low, the P2P engine may discard the chunks of the active stream with the lowest priority. Chunk Uploading: Request Prioritization — A peer occasionally receives chunk upload requests from its neighbors. When requests come from peers with almost equal contributions, it is best to favor requests that belong to streams of high priority. For instance, in the case of scalable coded video, a peer should be more likely to accept requests for base layer chunks. This increases the robustness and rate adaptation performance of the system. In the 3D video streaming case, depthmap streams should have the highest priority because they are used to generate multiple views at the client side. Then it is possible to prioritize the base and enhancement layers similar to the case of 2D video streaming.
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Figure 5. Synchronized full resolution stereoscopic video delivery.
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USE CASES FOR P2P 3D VIDEO STREAMING FULL RESOLUTION STEREOSCOPIC 3D VIDEO DELIVERY For users with Internet access, it is possible to deliver stereoscopic 3D content at full resolution without modifying the digital TV (DTV) infrastructure that delivers 3D video only in a frame-compatible format. The main idea is to use a hybrid delivery mechanism in which the IP channel is synchronized with the fixed bit rate broadcast channel. First, the full resolution source video is encoded as an enhancement layer to the base stream in a frame-compatible format that is transmitted over the DTV channel. Then the enhancement layer is transmitted to enable full resolution 3D video for users with Internet access, as depicted in Fig. 5. In such architecture, the DTV receiver provides the timing information to allow the IP channel to get in sync with the frame-compatible broadcast. A European Union project, DIOMEDES [14], has established such a hybrid delivery mechanism to distribute multiview content [15]. The fixed bit rate channel provides the baseline in frame-compatible format, whereas variable numbers of views and depthmaps are streamed over the P2P overlay. The deliverables indicate that the amount of data transmission from the content originator has been significantly reduced using P2P technology, making it a promising method to develop scalable solutions in 3D video delivery.
HEAD TRACKING SYSTEM FOR MULTIVIEW VIDEO DELIVERY As long as the view pairs change according to a user’s viewing position, it is possible to provide a free-view service. Such a system can be built using a head tracking system coupled with a stereoscopic display. In such a scenario, the head tracking device informs the adaptation module about the selected views. If the bit rate is high enough, users are still encouraged to receive all the video streams to allow for rapid changes in viewpoint. However, if the available link capacity is low, only required video streams are received, based on the feedback from the head tracking device. It is possible to further increase the effi-
IEEE Communications Magazine • May 2013
ciency of rapid view selection by introducing larger distances among camera positions and transmitting corresponding depthmaps. In such a case, a pair of two adjacent views covers a wider range when intermediate views are rendered at the client side.
CONCLUSIONS
plays in which QoE-aware rate adaptation is significantly affected by the number of supported views.
Broadcast of stereoscopic 3D media over digital TV platforms has already started. However, such platforms do not have the flexibility to support multiview video due to fixed bit rate constraints. Hence, we foresee that, in terms of medium, multiview video services will be over the IP platform using various architectures, including server-client and peer-to-peer systems. If the adaptation methods specific to stereoscopic 3D and multiview video are adopted and combined with adaptive P2P video streaming, it will be feasible to establish very successful 3D video services in the near future. One important challenge is to design an adaptation module that would assess the features of the 3D display to make the most effective prioritization of the video streams. This is especially a challenge with multiview displays in which QoEaware rate adaptation is significantly affected by the number of supported views.
REFERENCES [1] H. Schwarz, D. Marpe, and T. Wiegand, “Overview of the Scalable Video Coding Extension of the H.264/AVC Standard,” IEEE Trans. Circuits and Systems for Video Tech., vol. 17, no. 9, Sept. 2007, pp. 1103–20. [2] L. B. Stelmach et al., “Human Perception of Mismatched Stereoscopic 3D Inputs,” Proc. IEEE Int’l. Conf. Image Processing, vol. 1, Vancouver, BC, Canada, Sept. 2000, pp. 5–8. [3] L. B. Stelmach et al., “Stereo Image Quality: Effects of Mixed Spatio-Temporal Resolution,” IEEE Trans. Circuits and Sys. for Video Tech., vol. 10, no. 2, 2000, pp. 188–93. [4] G. Saygili, G.Gurler, and A. M. Tekalp, “Evaluation of Asymmetric Stereo Video Coding and Rate Scaling for Adaptive 3D Video Streaming,” IEEE Trans. Broadcasting, vol. 57, issue 2, June 2011, pp. 593–601. [5] C. Fehn, “Depth-Image-based Rendering (DIBR), Compression and Transmission for a New Approach on 3D TV,” Proc. SPIE Stereoscopic Displays and Virtual Reality Sys., vol. 5291, 2004, pp. 93–104. [6] C. G. Gurler, S. S. Savas and A. M. Tekalp, “Quality of Experience Aware Adaptation Strategies for Multi-View Video over P2P Networks,” Proc. IEEE Int. Conf. Image Processing, Orlando, FL, Oct. 2012. [7] G. Gurler, K. Bagci, and A. M. Tekalp, “Adaptive Stereoscopic 3D Video Streaming,” Proc. IEEE Int. Conf. Image Processing, Hong Kong, China, Sept. 2010.
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[8] Cisco Visual Networking Index, Forecast and Methodology, 2010–2015, http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ ns827/ VNI_Hyperconnectivity_WP.html [9] A. Vlavianos, M. Iliofotou, and M. Faloutsos, “BiToS: Enhancing BitTorrent for supporting Streaming Applications,” Global Internet Wksp. in conjunction with IEEE INFOCOM ’06, Apr. 2006 [10] J. Pouwelse et al., “Tribler: A social-based Peer-to-Peer system,” 5th Int’l. Wksp. Peer-to-Peer Sys., 2006. [11] M. Eberhard et al., “Knapsack Problem-Based Piece Picking Algorithms for Layered Content in Peer-to-Peer Networks,” ACM Wksp. Advanced Video Streaming Techniques for P2P Networks and Social Networking, 2010, pp. 71–76. [12] B. Cohen, “Incentives Build Robustness in Bittorrent,” Wksp. Economics of Peer-to-Peer Systems, Berkeley, CA, May 2003. [13] C. G. Gurler, S. S. Savas, and A. M. Tekalp, “Variable Chunk Size and Adaptive Scheduling Window for P2P Streaming of Scalable Video,” Proc. IEEE Int’l. Conf. Image Processing, Orlando, FL, Oct. 2012. [14] K. Aydogdu et al., “DIOMEDES: Content Aware and Adaptive Delivery of 3D Media over P2P/IP and DVBT2,” NEM Summit 2011, Implementing Future Media Internet, Torino, Italy, Sept. 2011. [15] E. Kurutepe, M. R. Civanlar, and A. M. Tekalp, “ClientDriven Selective Streaming of Multi-View Video for Interactive 3DTV,” IEEE Trans. Circuits and Sys. for Video Tech., 2007.
BIOGRAPHIES C. GÖKTUĞ GÜRLER (
[email protected]) received his B.Sc. degree in electrical engineering in 2006 and his Ph.D. degree in electrical engineering in 2013. His research interests include parallel processing in video codecs, transmission of 3D video over the Internet, perception of 3D (stereo and multiview), and quality adjustment and scalable video transmission. He has been involved in three EC projects regarding scalable P2P 2D/3D video streaming. He has published seven journal and 13 conference papers.
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M URAT T EKALP [S’80, M’82, SM’91, F’03] received double major B.S. degrees in electrical engineering and mathematics from Boğaziçi University, Istanbul, Turkey, in 1980, and his M.S. and Ph.D. degrees in electrical, computer, and systems engineering from Rensselaer in 1982 and 1984, respectively. After working briefly at Eastman Kodak Research, he joined the University of Rochester, New York, as an assistant professor in 1987, where he was promoted to Distinguished University Professor. He joined Koç University, Istanbul, Turkey, in 2001, where he is currently the Dean of Engineering. He has been the Editor-in-Chief of the EURASIP journal Signal Processing: Image Communication published by Elsevier (1999–2010). Formerly, he served as an Associate Editor for IEEE Transactions on Signal Processing (1990–1992) and IEEE Transactions on Image Processing (1994–1996). He was also on the Editorial Board of IEEE Signal Processing Magazine (2006–2009) and the Academic Press journal Visual Communication and Image Representation (1995–2002). He was appointed Special Sessions Chair for the 1995 IEEE International Conference on Image Processing, Technical Program Co-Chair for IEEE ICASSP 2000 in Istanbul, Turkey, General Chair of the IEEE International Conference on Image Processing at Rochester, New Yorrk, in 2002, and Technical Program Co-Chair of EUSIPCO 2005 in Antalya, Turkey. He is the founder and First Chairman of the Rochester Chapter of the IEEE Signal Processing Society. He was elected as Chair of the Rochester Section of IEEE in 1994–1995. He authored the book Digital Video Processing (Prentice Hall, 1995). He holds eight U.S. patents. He is a member of the Advanced Grant panel for the European Research Council, and a project evaluator and referee for the European Commission. He is also appointed as a National Expert for the European Commission. He is a member of the Turkish Academy of Sciences (TUBA), and a member of Academia Europaea. He was elected a Distinguished Lecturer by the IEEE Signal Processing Society in 1998 and received the TUBITAK Science Award in 2004. He was a member of the IEEE Signal Processing Society Technical Committee on Image and Multidimensional Signal Processing from 1990 to 1999, and chaired it from January 1996 to December 1997.
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