An efficient scheme for motion estimation using multireference frames ...

IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 8, NO. 3, JUNE 2006

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An Efficient Scheme for Motion Estimation Using Multireference Frames in H.264/AVC Sung-Eun Kim, Jong-Ki Han, and Jae-Gon Kim, Member, IEEE

Abstract—The multiple reference frame motion compensation (MRMC) supported by H.264 makes use of the redundancy which is between multiple frames to enhance the coding efficiency over a scheme using the single reference frame motion compensation (SRMC) in which motion vectors are searched over a single reference frame. And, the technique using multiple reference frames can combat the channel errors efficiently. However, searching the motion vectors in multiple frames may require a huge computing time. This paper proposes a novel motion estimation procedure, which has a lower search complexity without sacrificing image quality. To reduce the complexity of motion estimation procedure, we use a temporary motion vector generated with little computation. The temporary motion vector is calculated from the motion vector map composed of motion vectors between successive frames, and used to predict the optimal motion vector for a reference frame. The proposed scheme requires the lower complexity than conventional schemes by using the temporary motion vector and refinement process over a narrow search range around the temporary predictive motion vector. Since the temporary predictive motion vector effectively chases the optimal motion vector for each reference frame, the encoded image quality by proposed scheme is very similar to that of full search algorithm. The proposed motion estimation process consists of three phases: 1) making a vector map between two consecutive frames, where the vector map is constructed by copying motion vectors which have been estimated in first reference frame, 2) composing a temporary motion vector with element vectors which are in the vector map, and 3) finally, the temporary predictive motion vector is refined over a narrow search range. We show experimental results which demonstrate the effectiveness of the proposed method. To compare the proposed motion estimation algorithm with the conventional schemes, we check the CPU times consumed by ME module in H.264 encoder using the proposed scheme. In the results, CPU time consumed by the proposed scheme has been reduced significantly without additional distortion of the encoded video quality. Index Terms—H.264, motion estimation.

I. INTRODUCTION HE advanced video coding (AVC) standard now known as H.264 has dominated the video coding standardization community for the past several years [1]. The H.264 standard is designed to provide a technical solution appropriate for a

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Manuscript received October 11, 2004; revised May 24, 2005. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Teepak S. Duraga. S.-E. Kim and J.-K. Han are with the Department of Information and Communications Engineering, Sejong University, Seoul, Korea (e-mail: [email protected]; [email protected]). J.-G. Kim is with the Broadcasting Media Research Group, Electronics and Telecommunications Research Institute (ETRI), Taejon, Korea (e-mail: [email protected]). Digital Object Identifier 10.1109/TMM.2006.870740

broad range of applications, e.g., broadcast, interactive or serial storage, video conference services over wireless networks, video-on-demand, and streaming services [2]. The range of bit rates and frame sizes supported by H.264 is broad, from very low bit rate, low frame rate, small resolution video for mobile communications, through to SDTV or HDTV [2]. In the H.264 standard, a number of new technical developments have been adapted to increase the coding efficiency and provides robustness to network environments [3]–[5]. The new techniques include 1) the variable block-size motion compensation; 2) quarter-sample accuracy for motion compensation; 3) multiple reference frame motion compensation; 4) decoupling of referencing order from display order; 5) weighted prediction; 6) direct spatial prediction for intracoding; 7) in-the-loop deblocking filter [6]–[11]. Among the above techniques, an efficient algorithm for the multiple reference frame motion compensation (MRMC) is very important in the H.264 encoder since the computational complexity of MRMC is the biggest in the H.264 encoding system, and the coding performance of the H.264 encoder is dominantly affected by the MRMC module. Therefore, several approaches have been investigated to enhance coding efficiency of the MRMC module adapted in H.264 [12]–[15]. In [12], Wiegand et al. proposed a scheme to control the bits for the side information required to use multiple reference frames. Also, a novel approach to select some frames as the reference frames from multiple frames was proposed in [13], where authors classified the shifted versions of objects into “integer-pixel location,” “half-pixel location,” and “quarter-pixel location,” and they selected some frames according to the shifted version of the current macroblock. For example, if shift versions of the collocated macroblocks in second and third reference frames are the same, they use only a reference frame which is closer to the current reference frame, i.e., the second reference frame. However, this method does not reduce the complexity of ME module significantly when many reference frames are selected. Al-Mualla et al. [14] extended the simplex minimization search (SMS) of SRMC to the MRMC, and showed that their algorithm requires a computational complexity comparable to that of single reference full search scheme. A novel center-biased frame selection method was proposed to speed up the multireference frame motion estimation (MRME) [15], where authors searched an optimal motion vector on a center-biased path of each reference frame, and they selected a single reference frame that has an optimal motion vector on the path. In the selected frame, a full search scheme is used to find a further optimal motion vector. They assumed that the selected reference frame

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Fig. 1. Motion vector estimation using multireference frames in H.264.

Fig. 2. Example of the process to make a motion vector map.

has a global optimal motion vector. But the motion vector estimated over the selected frame might not be a global optimal one but a local one, since cost function for motion estimation may not be convex and the search regions of the scheme [15] are limited. Even though this method can make the MRME module faster, it usually incur a significant loss in visual quality since this algorithm can give a local optimum. The purpose of this paper is to derive an efficient scheme for the MRME adapted in the H.264 encoder. The proposed motion estimation scheme has low search complexity without sacrificing image quality, i.e., the quality of the image encoded by the proposed MRME is equal to that by the conventional MRME. The method proposed in this paper consists of three phases. First, the vector map is made of the motion vectors which have been estimated through ME (motion vector estimation) procedure between a current frame and first reference frame. Second,

we compose a temporary predictive motion vector using the vector map which has been generated at the previous phase. Finally, the final motion vector can be obtained by refinement of the temporary predictive motion vector over a narrow search range which is much smaller than that required by the conventional MRME scheme. In [11], H.264 allows multiple-hypothesis prediction using up to two macrohypotheses through the use of two-list prediction which is a bidirectional ME procedure using forward and backward reference frames. In this paper, for the clear and simple description of the algorithm, we consider one-list prediction which uses forward reference frames. Since it is easy for our framework to be extended to the two-list case, the one-list treatment does not result in loss of the generality of the algorithm. This paper is organized as follows. Section II describes the MRME (multireference frame motion vector estimation)

KIM et al.: AN EFFICIENT SCHEME FOR MOTION ESTIMATION USING MULTIREFERENCE FRAMES IN H.264/AVC

Fig. 3. Proposed process to estimate

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scheme adapted in H.264. In Section III, we propose an efficient scheme for MRME. Computer simulation results for the proposed algorithm are presented in Section IV. A brief conclusion is given in Section V. II. MOTION VECTOR ESTIMATION IN H.264 A. Conventional MRME Scheme In H.264, more than one prior coded frame can be used as reference for motion vector estimation in H.264 [3], [9], [11]. The coding efficiency can be improved by using multiple reference frames instead of single frame [3]. The difference data between the current macroblock and the predicted reference block can be reduced by searching motion vectors in multiple frames. Thus, MRME scheme improves the coding efficiency of the H.264 encoder in the respect of image quality and compression ratio. The procedure to estimate the motion vector using multireference frames is shown in Fig. 1, where is a macroblock or submacroblock for which we are going to estimate an optimal motion vector in multiple frames. Note that the size of the block is , where is one of , and , , and are the th past frame and a current frame, respectively.

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using motion vector map.

, one of 7 types, When the size of a block is set to a the motion vector for the block is estimated as shown in (1) at the bottom of page, where is a candidate moand a reference frame tion vector between a current frame and is a reference block which is in the and is shifted by a motion vector frame from the location of the block in . Note that both sizes of and are . is a Lagrangian paramis a median vector of eter for motion vector estimation and motion vectors of spatially adjacent blocks at the left, top, and top-right from current position. is the number of bits to transmit all components of the motion is a search range in the frame . vector, and , After the process (1) is executed for are generated. Among them, an optimal motion vector, which minimizes the cost ), is selected function of (1) for a block (whose size is . as After this procedure is applied for all possible combinations of block sizes , the motion vectors are obtained for the modes supported in H.264. Based on those motion vectors, an optimal

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TABLE I PROPOSED ALGORITHM FOR MRME

mode function

is decided by minimizing following cost

(2) where the distortion is measured as the sum of the squared differences between the corresponding reconstructed blocks and the original blocks which are in a represents one of possible combinations macroblock. . of can be , , For example, , and so on. And the is the bit rate generated by encoding the header and motion vectors information for a macroblock. In moving frame encoding standards, such as MPEG-2 and MPEG-4, the motion vector estimation module requires a large amount of computation to decide the motion vector and block mode. Furthermore, utilizing multiple reference frames requires much more computation than using single reference frame. Thus, increasing the speed of searching the motion vector and mode information is one of the most effective ways of reducing the computation time consumed in H.264 encoder. B. Computational Complexity of a Conventional MRME Scheme In this section, we evaluate the computational complexity of a conventional MRME scheme. This paper reduces the number of block matching processes for a mode, not the number of the considered modes. Thus, we evaluate computational complexity in the view point of the number of block matching processes. Assume the number of the reference frames is N, and in the th reference frame is limited to the search range the maximum displacement of pixels, where and are the sizes

of the width and height of the search range, respectively. In this circumstance, the conventional MRME algorithm performs block matchings for a . Thus, if the number of macblock in the current frame roblocks in the current frame is , the total number of the block matching processes required to estimate motion vectors for all is macroblocks in the frame

(3) where is the number of total modes considered to minimize the cost function of (2) in a macroblock. This computational quantity occupies the biggest part of the computing power consumed by the H.264 encoder. This means that an efficient scheme to speed up the processes of (1) and (2) is important in implementation of a multimedia system which uses H.264 standard as a video codec and is operating with low delay, i.e., real time application. III. PROPOSED EFFICIENT MRME ALGORITHM FOR H.264 In this section, we propose an efficient method to estimate the motion vector using multireference frames. Assume the circumstance of the proposed encoding procedure is equal to that shown in Fig. 1, except for the size of a new search range , , is much smaller than that of the conventional MRME process described in Section II. A. Motion Vector Map The maps , , are which had made from the motion vectors and been estimated when the frames were a current frame and a single reference frame, respectively.

KIM et al.: AN EFFICIENT SCHEME FOR MOTION ESTIMATION USING MULTIREFERENCE FRAMES IN H.264/AVC

Fig. 4. PSNR for a QCIF “Car phone” sequence, (GOP IPPP . . . ; 64 kbps, Frame rate = 10 Hz ).

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Fig. 6. PSNR for a CIF “Tempete” sequence, (GOP = IPPP . . . ; 256 kbps, Frame rate = 30 Hz).

Fig. 2 shows an example of the process to make a motion vector and . The motion vectors map between frame are estimated by (1) with single reference frame , where the inter mode of each macroblock is one of . The element vectors in has 4 4 spatial resolution and they are generated by copying motion vectors located at the same spatial position. For example, if the mode of a macroblock decided as 16 16, a motion vector is copied to all 16 element vectors (4 4 resolution) located at the same spatial position in . B. An Efficient Motion Estimation

Fig. 5. PSNR for a CIF “Mobile & Calendar” sequence, (GOP , ).

=

IPPP . . . ; 128 kbps Frame rate = 15 Hz

Note that, when is a current frame, the vector maps beand , , are given tween ’s are estifrom the previous procedures. mated by applying (4), shown at the bottom of the page. The procedure of (4) is a part of the SRME where is a current frame. Since this is included in the previous phase were a current frame, estimating of MRME, where motion vectors by (4) does not need any additional computation.

The procedure of the proposed MRME algorithm is shown in Fig. 3. When the current frame is and is given from the previous phase and where (1) had been applied for a current frame , the optimal motion vector a single reference frame is searched in the reference frame as follows. At first, a motion vector is generated by , i.e., (5), shown at the bottom of the next using (1) with page. Then the element vectors of partially or fully overlapped by the are used to make the trace vector . In the example shown in Fig. 3: (6)

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Fig. 7. Motion vector estimation time consumed by the H.264 encoders for various sequences.

The median is particularly effective in the presence of both bipolar and unipolar impulse noise vectors. Thus median uncorrupted scheme yields excellent results for by impulse noise vector. And a temporary predictive motion vector is composed by (7) may be different from the Note that the which is estimated by the conventional MRME,

. In order to reduce the difference, a i.e., by (1) with new motion vector is searched over a search range whose center is indicated by , where the area of is much smaller than that of . Thus, the computational complexity can be reduced dramatically. The proposed scheme is summarized in Table I. Motion esis pertimation process using first reference frame formed in Step 1. Then, in Step 2, the estimated motion vectors, , are stored in , and they becomes a current frame are utilized when a frame

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Fig. 8. Rate-distortion curves for various video sequences.

in the next phase. In Step 4, temporary predictive motion vector is calculated from motion vector map, and the predictive motion vector is refined in Step 5, where the size of is much more narrow than that of consearch range ventional search range . Steps 4 and 5 are repeated for reference frames. In Step 7, an optimal motion vector , , estiamong motion vectors mated in Step 1 and Step 5 for a mode is decided by minimizing ’s are considered, cost function of (1). After all cases of a particular mode is decided in Step 9, and then motion estimation procedure stops. is a current frame, , When , and are given as those made in the , previous phases where were current frames, respectively. The ’s cover the entire regions of reference frames. Therefore, there is no case is where the region corresponding to current block and nonoccluded in some previous occluded in frame frame. If an object in a video sequence is appearing or disap-

peared, the proposed scheme selects a motion vector that has a minimum prediction error. Even if the prediction error is large, the proposed algorithm can be applied properly. In the proposed scheme, when reference frames are used for motion estimation, motion vector maps have to be stored. If the total number of macroblocks in a frame is , motion vectors for a motion memory is required to save vector map since the proposed scheme save a motion vector for a 4 4 size block. Although the proposed motion estimation algorithm requires memory space to store motion vector maps and has to utilize the memory to calculate the temporary predictive motion vector, it can be compensated sufficiently by the enhanced encoding speed. C. Computational Complexity of the Proposed MRME Scheme In the proposed MRME scheme, the computational complexity is dominated by Step 1 and Step 5. In Step 1, the block matching process is executed by (1). Because the size in the reference frame is of the search range pixels, the motion estimation

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TABLE II TOTAL MOTION VECTOR ESTIMATION TIME FOR VARIOUS SEQUENCES

process of Step 1 requires block matching processes. In Step 5, the motion vector is whose size is refined over a narrow search range . Thus, the estimation of Step 5 block matching processes needs . Since Step 5 is repeated for a block in the current frame for reference frames , , the total number of the required block matching processes becomes

(8) where Since

is the number of macroblocks in the current frame. and are much smaller than and of (3), respectively, and the procedure is repeated times, the proposed scheme provides a significant gain in the overall complexity.

IV. SIMULATION RESULTS Computer simulations using video sequences were performed to evaluate the performance of the proposed algorithm. The test images are two QCIF (Car phone, Foreman) and two CIF (Mobile and Calendar, Tempete) sequences. The number of the ref, and the results are genererence frames is fixed as ated by optimizing motion vectors over a combination of . In this simulation, the structure of GOP is “IPPPP .” i.e., all frames except the first frame are encoded as P frame. The size of new is set with , (for QCIF search ranges image), , (for CIF image) for all , and is set with , that of conventional search ranges (for QCIF image), , (for CIF image). When Step 4 in Table I is applied recursively for reference frames, a vector makes trace information for a reference frame. Due to these effective motion vector trace process, the predictive motion vector can be very closed to optimal motion vector

. Thus, the size of search range for refinement in Step 5 does not have to be increased with temporal distance. For determining the effectiveness of proposed scheme, various bit-rates and frame-rates are used to encode test sequences. The sequence Car phone is coded at 64 kbps and 10 fps, Mobile and Calendar is coded at 128 kbps and 15 fps, and Tempete is coded at 256 kbps and 30 fps. The video codec used in this simulation is JM74 version [16]. The peak-to-peak signal-to-noise rations (PSNRs) of the encoded images are shown in Figs. 4–6. The PSNRs of the encoded images are evaluated with respect to the original sequences. The test images are encoded by the conventional MRME scheme, the proposed MRME scheme, a center-biased MRME [15], and the SRME, respectively. We have chosen the conventional MRME scheme and the center-biased scheme to compare the performance of the proposed scheme. Conventional MRME scheme is the full search method, sometimes referred to as the brute force search. This method gives an optimal solution, in term of image quality, by searching motion vectors over all . possible locations within a search window, But the drawback of this method is its high computational complexity. In the center-biased MRME scheme [15], motion vector is searched over narrow search ranges around center locations of all possible reference frames. After this process, they selected a reference frame which has a local optimal motion vector, and a full search scheme is performed to find optimal motion vector in the selected reference frame. It is assumed that the reference frame having a local optimal motion vector in the first step has a global one, but since the local optimal motion vector might be mismatched to the global one, the quality of the encoded image is degraded. In this paper, we use a temporary motion vector which was made from the motion vectors searched in the previous phases. Since the correlation between temporary motion vectors and optimal motion vectors is very high, the proposed algorithm provides more optimal motion vector than the center-biased MRME scheme [15]. As shown in Figs. 4–6, the PSNRs of the images encoded by MRME schemes (the proposed MRME scheme, the center-biased MRME [15], and the conventional MRME scheme) are

KIM et al.: AN EFFICIENT SCHEME FOR MOTION ESTIMATION USING MULTIREFERENCE FRAMES IN H.264/AVC

TABLE III NUMBER OF ARITHMETIC OPERATIONS REQUIRED TO ESTIMATE A MOTION VECTOR OF AN A

much higher than those by the SRME scheme. It implies that using multiple reference frames increases the coding efficiency of H.264 encoding system. And, it is observed that the PSNRs of the proposed MRME scheme are equal to those of the conventional MRME scheme, and are higher than those of the centerbiased MRME scheme and SRME method. These results show that, in the view point of image quality, the proposed MRME scheme outperforms the center-biased MRME and the SRME scheme. To compare the computational complexities among the encoding methods, the time consumed by the motion estimation module is checked in Fig. 7, where the MVE (motion vector estimation) time is displayed in the resolution of ms/frame. The ratio between the times consumed by the proposed MRME scheme and the conventional MRME scheme is shown in (9) at the bottom of the page, where the numerator and the denominator are (8) and (3), respectively. When QCIF and CIF images are encoded, and are substituted to (9), respectively, and the ratio becomes about 21% which coincide with the results shown in Fig. 7. As shown in these figures, the proposed scheme requires much smaller computing time than the conventional MRME and the center-biased MRME [15], and the computational complexity of the proposed MRME scheme is comparable to that of the SRME scheme. This is due to the fact that the proposed scheme utilizes the vector maps and the temporary motion vectors, while the conventional schemes do not concern the past infor-

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mation, such as motion vector maps. These results imply that the proposed algorithm exhibits significant improvement in the reduction of computational complexity when compared with the conventional schemes, while the encoded image quality remains unchanged. In order to check the usefulness of the proposed scheme, the average PSNRs of the encoded images are evaluated at the different bit rates. Fig. 8 shows that the average PSNRs resulting from the proposed scheme is close to those from the conventional MRME scheme, and the schemes using multiple reference frames outperform the method using single reference frame over the entire bit rate range. We check the motion vector estimation times for various sequences in Table II, where the complexities of proposed MRME scheme and conventional MRME scheme are compared by

(10) These results show that the proposed MRME scheme reduces the encoding time dramatically. The simulation results shown in this section indicate that the PSNRs of the bitstreams generated by the proposed MRME are almost equal to that of the conventional MRME, while the encoding system using the proposed MRME scheme requires only 21% of the computation time consumed by the conventional MRME scheme. From these results, it can be assured that the proposed MRME scheme is very effective in the H.264 encoder system.

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Table III shows the number of arithmetic operations required size block. To calculate to estimate a motion vector for a size block, multiplicathe cost value by (1) for a tions and additions are required. Therefore, when we , use a search range whose size is the number of multiplications and additions required to estiand mate a MV are , respectively. The proposed MRME scheme requires not only block matching but also median operations to calculate the temporary predictive motion vector. If is the number of element vectors to be considered in median operation, the median vector is calculated by addition and a multiplication. Though the prousing posed MRME scheme requires some median operations, since the number of block matchings of the proposed scheme is much smaller than that of conventional MRME scheme, our scheme is much faster than the conventional schemes. V. CONCLUSION We have proposed an efficient scheme to estimate the motion vector in the H.264 standard. The proposed scheme consists of three phases: preparation of motion vector maps, making a temporary motion vector, and the refinement of the motion vector. The proposed scheme made a temporary motion vector which is made from the information calculated in the previous steps. Since the temporary vector closes to the motion vector estimated by the conventional MRME scheme, the proposed scheme can be used to reduce the computational complexity while the bit rate and the image qualities are maintained. The various computer simulations show that our technique can reduce the encoding time consumed by the H.264 encoder. The results imply that the proposed scheme outperforms the conventional methods. The proposed algorithm enables a faster encoding without any loss of image quality. REFERENCES [1] A. Luthra, G. J. Sullivan, and T. Wiegand, “Introduction to the special issue on the H.264/AVC video coding standard,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 557–559, July 2003. [2] I. E. G. Richardson, H.264 and MPEG-4. New York: Wiley, 2003. [3] T. Wiegand, G. J. Sullivan, G. Bjntegaard, and A. Luthra, “Overview of the H.264/AVC video coding standard,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 560–576, Jul. 2003. [4] T. Stockhammer, M. M. Hannuksela, and T. Wiegand, “H.264/AVC in wireless environments,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 657–673, Jul. 2003. [5] S. Wenger, “H.264/AVC over IP,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 645–656, Jul. 2003. [6] D. Marpe, H. Schwarz, and T. Wiegand, “Context-based adaptive binary arithmetic coding in the H.264/AVC video compression standard,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 620–636, Jul. 2003. [7] M. Wien, “Variable block-size transforms for H.264/AVC,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 604–613, Jul. 2003. [8] H. S. Malvar, A. Hallapuro, M. Karczewicz, and L. Kerofsky, “Lowcomplexity transform and quantization in H.264/AVC,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 598–603, Jul. 2003.

[9] T. Wiegand, H. Schwarz, A. Joch, F. Kossentini, and G. J. Sullivan, “Rate-constrained coder control and comparison of video coding standards,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 688–703, Jul. 2003. [10] P. List, A. Joch, J. Lainema, G. Bjntegaard, and M. Karczewicz, “Adaptive deblocking filter,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 614–619, Jul. 2003. [11] M. Flierl and B. Girod, “Generalized B pictures and the drift H.264/AVC video-compression standard,” IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 587–597, Jul. 2003. [12] T. Wiegand, X. Zang, and B. Girod, “Long-term memory motion-compensated prediction,” IEEE Trans. Circuits Syst. Video Technol., vol. 9, no. 1, pp. 70–84, Feb. 1999. [13] A. Chang, O. C. An, and Y. M. Yeung, “A novel approach to fast multi-frame selection for H.264 video coding,” in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing (ICASSP’03), Apr. 2003, vol. 3, pp. III-413–III-416. [14] M. E. Al-Mualla, N. Canagarajah, and D. R. Bull, “Simplex minimization for multiple-reference motion estimation,” in Proc. IEEE Int. Symp. ISCAS’00, Geneva, Switzerland, May 2000, vol. 4, pp. 733–736. [15] C. W. Ting, L. M. Po, and C. H. Cheung, “Center-biased frame selection algorithms for fast multi-frame motion estimation in H.264,” in Proc. 2003 Int. Conf. Neural Networks, Signal Processing, Dec. 2003, vol. 2, pp. 1258–1261. [16] JVT Codec Reference Software [Online]. Available: http://iphome. hhi.de/suehring/tml/download/old_jm/jm74.zip Sung-Eun Kim was born in Seoul, Korea, on April 24, 1979. He received the B.S degree in 2004 from the Department of Information and Communication Engineering, Sejong University, Seoul, Korea, where he is currently pursuing the M.S degree. His research interests include video coding, image processing, scalable video coding, and transcoding.

Jong-Ki Han was born in Seoul, Korea, on September 5, 1968. He received the B.S., M.S., and Ph. D degree in electrical engineering from Korea Advanced Institute of Science and Technology (KAIST), Taejon, in 1992, 1994, and 1999, respectively. From 1999 to 2001, he was Member of Technical Staff at the Corporate R&D Center, Samsung Electronics, Suwon, Korea. He is currently an Assistant Professor, Department of Information and Communications Engineering, Sejong University, Seoul, Korea. His research interests include image and audio signal compression, transcoding, and VLSI signal processing.

Jae-Gon Kim (M’90) received the B.S. degree in electronics engineering from Kyungpook National University, Daegu, Korea, in 1990, and the M.S. and Ph.D. degrees in electrical engineering from the Korea Advanced Institute of Science and Technology (KAIST), Daejeon, in 1992 and 2005, respectively. Since 1992, he has been a Senior Member of Research Staff, in the Broadcasting Media Research Group of Electronics and Telecommunications Research Institute (ETRI), Taejon, Korea. He is currently the Team Leader of the Convergence Media Research Team. From 2001 to 2002, he was a Staff Associate at Columbia University, New York. His research interests include video processing, networked video, multimedia applications, MPEG-7, and MPEG-21.