An adaptive rate control scheme for H. 264 scalable video coding

An Adaptive Rate Control Scheme for H.264 Scalable Video Coding Balaji L

Thyagharajan KK

Asst Prof, Dept of ECE, Velammal Institute of Technology, Research Scholar, Anna University, Chennai, India [email protected]

Dean (Academic), RMD Engineering College, Chennai, India [email protected] www.tansitresearch.com

segment at different levels to provide jitter-free video using a mathematical model to estimate a minimum value for the initial delay and try to reduce the bandwidth required for transmitting video and they also focus on reducing the buffer requirement. Besides a dynamic rate-changing algorithm and a variable-initial-delay algorithm are proposed in [31] for dividing the video into segments and were used in a hierarchical video summarization scheme for effective QoS.

Abstract—Rate Control plays an important role in video coding, which performs successful transmission of all encoded bits with available limited bandwidth. This paper aims to provide an adaptive rate controller for H.264 scalable video coding. The Quantization Parameter (QP) plays an important role in any rate controller for encoding demanded bits. The Quantization parameter estimation and the rate controller algorithm follows two pass 1) Initial Quantization Parameter value estimated by simplified Rate Distortion model using Cauchy Density based PDF and Buffer status 2) Rate Distortion Optimization method to determine minimum cost for the mode and motion vector using the estimated Quantization Parameter. The experimental result shows that our rate control algorithm is better than the FixedQPencoder of JSVM 9.19.15 in terms of PSNR and bit rate. Moreover our scheme performs in single iteration to encode the frame with the desired QP and Buffer status calculated to prevent from overflow and underflow.

Scalable Video Coding became the most emerging coding technology which is compatible for all forms of decoders such as mobile, PDA, SDTV, HDTV, etc., Because of its inherent nature of scalability in spatial, temporal and quality with respect to H.264/AVC it is standardized as H.264/SVC [17] and evaluated based on its performance [18] which provides high coding efficiency. A layered coding approach is followed in spatial scalability in which the picture with lowest spatial resolution are considered as Base layer which are encoded as H.264/AVC compatible bit stream, while the picture with large spatial resolution are considered as Enhancement layer. It is encoded by interlayer prediction mechanism which determines the redundancies between the consecutive spatial layers in order to improve coding efficiency. A hierarchical B picture approach is used in temporal scalability for a given spatial layer with zero structural delay. The GOP size determines the number of temporal layers in a spatial layer and the first picture in each GOP is termed as key picture. Quality or SNR scalability achieved based on different spatiotemporal reconstruction quality levels. H.264/SVC adopts two quality scalability levels. 1) Coarse Grain Scalability (CGS) employs a single temporal layer per spatial layer. 2) Medium Grain Scalability (MGS) employs a multiple temporal layers based on GOP size per spatial layer is obtained.

Keywords— H.264/SVC, Rate control, RDO, Scalable Video Coding, Cauchy – Density PDF

I.

INTODUCTION

Rate Control is that all the estimated bits are transmitted successfully over a network of limited bandwidth without being delayed. Being an intermediate between the encoder and the network bandwidth, rate controller adjusts the bit rate by deciding the step sizes of Quantization parameter at different levels while optimizing coding efficiency and buffer occupancy. Besides some recommended baseline rate controller such as Test Model 5 for MPEG 2 [13], Test Model Near Term 8 for H.263 [14], Verification Model 8 for MPEG 4 [15], JVT G012 for H.264/AVC [16], but these algorithms focused only on non scalable video coding. Many Rate control methods have appeared in recent years such as analytical R-D model for QP estimation assuming a Gaussian pdf for DCT Coefficient [1]. On the other hand assuming a Cauchy pdf a simple exponential R-D Model [2] is proposed. Alternatively, using Laplacian pdf, linear R-D model [3], quadratic model [4], ȡ-domain based [5] R-D models have been proposed. With the variable nature of video signal to the variable network bandwidth, VBR Rate control algorithms such as live streaming [6], broadcast [7], one pass digital storage [8] [9], two pass digital storage [10] [11] have proposed. The visual quality can be improved in a VBR channel [12] with reduced buffer delay. In [30] streaming parameters such as bandwidth, buffer requirements and initial delay are evaluated for each

c 978-1-4673-6126-2/13/$31.00 2013 IEEE

However the reference software JSVM 9.19.15 [19], only adopts a FixedQPencoder to encode the frames of a video sequence and it is lagging in providing better coding efficiency. To give better performance on coding efficiency, few research has been carried out such as predicting the MAD for multilayer [20], ȡ-domain based Rate controller [21], mode decision based MAD [22], RBF based QP estimation [23], logarithmic R-D model from Cauchy – density based model [24] have been proposed. The rest of the paper is organized as follows, in section 2, Initial QP estimation for Basic unit is given, in section 3, the

40

proposed Rate Control Algorithm for H.264/SVC is given, in section 4, the experimental results are presented to evaluate the performance of the proposed rate controller. II.

and the model parameters lie in the range as shown

INITIAL QP ESTIMATION FOR BASIC UNIT

For any Rate controller the value of QP decides the number of bits being generated while coding a Block, MB, Picture or a Frame. The Lagrangian Multiplier for mode decision and motion vector depends upon the QP, which needs to be estimated prior to determining the minimum cost for mode and motion vector. A similar approach which is followed in [25], to estimate the QP value for Basic Unit through Cauchy – Density based pdf for the first picture (I / P) of GOP is followed. A single BU comprised of a set of MB, which share the same QP value when it is scanned by interlaced scanning. Although a picture comprised of a set of BU which takes different QP value, so the BU QP value changes in a frame. So, the possible number of BU size is inversely proportional to the total number of MBs available in a frame. ‹‰Ž‡ƒ•‹…‹– ‫ ן‬

ଵ ୒୳୫ୠୣ୰୭୤୑୆

(1)

III.

ͳǤʹ ൏  ߙ௞ǡ௝ ൏ ͳǤ͸

for P picture

ͳǤ͸ ൏  ߙ௞ǡ௝ ൏ ʹǤͲ

for B picture

PROPOSED RATE CONTROL ALGORITHM FOR H.264 SVC

The proposed Rate Control Algorithm is two pass, first it selects the QP value for the first picture of a GOP by estimating the target bits for the BU of the picture by simplified Rate Distortion model obtained by Cauchy – Density pdf [10]. Second, with the estimated QP value, buffer and minimum cost for estimating mode decision and motion vector are updated. The proposed block diagram in Figure 1 shows the Base layer and Enhancement layer computes the Initial QP for the first frame of a GOP and then it updates the buffer occupancy level and lagrangian parameter optimization of minimum cost for mode decision, but motion vector depends on mode decision. So, an optimized lagrangian parameter ȜMode and ȜMotion needs to be adjusted to overcome the tradeoff problem.

Assuming, jth picture in the ith GOP is composed of k BUs, then the bit budget, ܶ௥ǡ௜ ሺ݆ሻ is given by ܶ௜ ሺ݆ሻǡ ൌ ͳ ܶ௥ǡ௜ ሺ݆ሻ ൌ ൜ ܶ௥ǡ௜ ሺ݆ሻ െ ‫ݐ‬௞ିଵǡ௜ ሺ݆ሻǡ ‘–Š‡”™‹•‡

(2)

here ‫ݐ‬௞ିଵǡ௜ ሺ݆ሻ is the number of bits used to encode previous BU, ܶ௜ ሺ݆ሻ is to satisfy hypothetical reference decoder constraints. For an lth BU, the bit budget, ܶ௥ǡ௜ ሺ݆ሻ having ܾ௟ǡ௜ ሺ݆ሻ as Texture bits and ݄௟ǡ௜ ሺ݆ሻ as Header & motion bits will be ௄

ܶ௥ǡ௜ ሺ݆ሻ ൌ ෌௟ୀ௞൫ܾ௟ǡ௜ ሺ݆ሻ ൅ ݄௟ǡ௜ ሺ݆ሻ൯

(3)

The Cauchy – Density based pdf method of deriving a simplified Rate Distortion model proposed by [25] is used for our estimation of QP value with c & ߛ are model parameters given by ‫ܦ‬ሺܴሻ ൎ ܴܿ ିఊ

(4)

So, the Texture bits, bl,i(j) can be found by the relation described in (4) భ ം

షభ ം

ംೖǡೕ

ܾ௟ǡ௜ ሺ݆ሻ ൌ ܿ௟ǡ௝೗ǡೕ ܿ௞ǡ௝೗ǡೕ ܾ௞ǡ௜ ሺ݆ሻ ം೗ǡೕ

(5)

Using (5) in (3) and assuming the same header and motion bits for ܰ௥ remaining BUs, the BU bit bit budget is given by ௄

భ ം೗ǡೕ

ܶ௥ǡ௜ ሺ݆ሻ ൌ ෎

షభ ം೗ǡೕ

൭ሺܿ௟ǡ௝ ܿ௞ǡ௝ ܾ௞ǡ௜ ሺ݆ሻ

ംೖǡೕ ം೗ǡೕ

ሻ ൅ ܰ௥ ݄௟ǡ௜ ሺ݆ሻ൱ (6)

Fig. 1. Block Diagram of Rate Control Scheme for 2 Dependency Layers

A. Buffer Status The Buffer is a virtual one updated by the output bits and QP value so as to prevent from overflow and underflow. This occupancy of the buffer is calculated at the GOP level. The GOP level rate control calculate the remaining bits for the rest picture, which gives a choice of estimating the QP value for the next picture. When jth picture in the ith GOP is coded, then the total number of bits for the rest picture in the GOP is calculated as,

௟ୀ௞

Finally, the QP value for the kth BU in picture for the jth picture in the ith GOP obtained by ܳ௞ǡ௜ ሺ݆ሻ ൌ ൬

షభ ௕ೖǡ೔ ሺ௝ሻ ഀೖǡೕ

௔ೖǡೕ

൰

(7)

‫ܤ‬௜ ሺ݆ሻ ൌ ቐ

‫ܤ‬௜ ሺ݆ െ ͳሻ ൅

ோ೔ ሺ௝ሻ

ൈ ܰ௜ െ  ܸ௜ ሺ݆ሻǡ ݆ ൌ ͳ

௙ ோ೔ ሺ௝ሻିோ೔షభ ሺ௝ሻ ௙

ሺܰ௜ െ ݆ ൅ ͳሻ െ ܾ௜ ሺ݆ െ ͳሻǡ ݆ ൌ ʹǡ͵Ǥ Ǥ

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(8)

41

f-frame rate,ܰ௜ -Size of the ith GOP, ܴ௜ -Bit rate, ‫ܤ‬௜ ሺ݆ሻ-Actual bits generated, ܸ௜ ሺ݆ሻ-Buffer occupancy for the jth picture in the ith GOP But in our proposed Rate Control Algorithm, QP estimation not only on the BU but also depends on the buffer fullness status. So, the final QP estimation for quantizing the BUs of first picture (I/P) of all GOPs is given by the relation, ܳܲ ൌ ܳ௞ǡ௜ ሺ݆ሻ ൅ ‫ܤ‬௜ ሺ݆ሻ

(9)

B. Rate Distortion Optimization Rate Distortion optimization of mode decision and motion estimation or using lagrangian optimization achieves better coding efficiency for any rate controller in video coding. Interframe coding minimizes temporal distortion among successive frames, so this interframe coding can be realized by motion estimation (residual & motion vector). So, a lagrangian optimization method as implemented in JVT Reference model software is used to achieve coding efficiency. A constrained optimization problem using lagrangian multiplier is converted to an unconstrained problem using lagrangian multiplier method, by minimizing Distortion D and Rate R, such that, ‹ ‫ܦ‬ǣ ܴ ൏ ܴ஼

(10)

Here the above problem is converted using Lagrangian multiplier Ȝ to find a minimum Rate Distortion Cost as follows ‹ ‫ܬ‬௜ ‫ܬ  ׷‬௜ ൌ ‫ܦ‬௜ ൅ ߣ௜ ൈ ܴ௜

(12)

The minimum cost for temporal correlation is determined, now the correlation between the intra modes can be determined spatially by the lagrangian multiplier ȜMode. The Sum of Squared Differences (SSD) between the original and reconstructed picture in bits is considered as Distortion (Di) and the mode in bits is considered as Rate (RM) which are associated with mode decision. So the lagrangian parameter ȜMode is chosen to minimize the cost of each MB or picture as related by ‫ܬ‬ெ ൌ ܵܵ‫ܦ‬ெ ൅ ߣெ௢ௗ௘ ൈ ܴெ

(13)

The mode with minimum cost JM is taken as the best coding mode for the MB. In H.264, ȜMode, ȜMotion and QP are related [26] as follows ߣெ௢ௗ௘ ൌ ͲǤͺͷ ൈ ʹሺொ௉ିଵଶሻȀଷ ߣெ௢௧௜௢௡ ൌ  ඥߣெ௢ௗ௘

42

IV.

EXPERIMENTAL RESULT

The proposed Rate Control Algorithm is done using JSVM 9.19.15 [28] Reference Model Software. We took three different set of known yuv sequences such as Foreman, Bus & Mobile with QCIF (176x144@15fps) for Base Layer and CIF (352x288@30fps) for Enhancement Layer and the GOP size is set to 16 with adaptive interlayer prediction for Base Layer. Every key frame in a GOP is encoded as I frame and layer 0 which use QCIF@15 frames per second and Layer 1 which uses CIF@30 frames per second. As Suggested [32] the base layer and enhancement layer quantization parameters values have a difference of 6 is considered while estimating the minimum target bits for FixedQPEncoder. The performance of our Rate control algorithm is compared with the FixedQPencoder of JSVM and it shows that our algorithm has significant improvement in Bit rate and PSNR. Moreover JSVMs FixedQPencoder finds the optimum QP value after performing sequence of iterations with lack of buffer management, but our Rate Control Algorithm achieves without any iteration and rate of encoded output bits are controlled by a virtual buffer which also maintains buffer occupancy to prevent overflow and underflow. TABLE I.

COMPARISON IN BIT RATE AND PSNR OF PROPOSED RCA WITH FIXEDQPENCODER

(11)

As stated, Residual or prediction error or Mean Absolute Difference (MAD) is the difference between the original and reconstructed picture in bits is considered as Distortion (Di) and motion vector is considered as the Rate (RMV) which are associated with motion vector. So the lagrangian parameter ȜMotion is chosen to minimize the cost of each MB or picture as related by ‫ܬ‬ெ௏ ൌ ܵ‫ܦܣ‬ெ௏ ൅ ߣெ௢௧௜௢௡ ൈ ܴெ௏

Hence, the QP obtained from section 2 is utilized for estimating the lagrangian optimization parameters ȜMode and ȜMotion.

YUV Sequen ce

Fixed QP Encoder Resolution

Proposed RCA

Target Bit Rate (kbps)

Actual Bitrate (kbps)

Y PSNR (dB)

Actual Bitrate (kbps)

Y PSNR (dB)

Layer 0 QCIF@15fps

180

179.52

31.589

132.50

31.466

Layer 1 CIF@30 fps

600

613.82

29.086

516.43

28.201

Layer 0 QCIF@15fps

180

228.19

28.563

178.37

28.499

Layer 1 CIF@30 fps

600

781.43

24.963

604.30

23.958

Layer 0 QCIF@15fps

180

186.53

28.025

181.66

27.955

Layer 1 CIF@30 fps

600

810.22

21.813

665.84

21.248

Foreman

Bus

Mobile

(14) (15)

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800.00

BU US FIIXEDQPENCODER BIIT RATE

600.00

BU US PROPOSED RC CA BIT RATE

400.00 200.00

BU US FIIXEDQPENCODER Y PSNR

0.00 12

34

56 7 8

9

BU US PROPOSED RC CA Y PSNR

Fig. 2. Illustration of Bit rate & PSNR for Fixed QP Encoder and the Proposed RCA with Layer 0 & 1 for Bus Video

700.00 600.00 500.00 400.00 300.00 200.00 100.00 0.00

FO OREMAN FIX XEDQPENCODER BIIT RATE FO OREMAN PR ROPOSED RCA BIT RA ATE

12

34

FO OREMAN FIX XEDQPENCODER Y PSNR 56 7 8 9

FO OREMAN PR ROPOSED RCA Y PS SNR

Fig. 3. Illustration of Bit rate & PSNR for Fixed QP Encoder and the Proposed RCA with Layer 0 & 1 for Foreman Video

MO OBILE FIX XEDQPENCODER BIT T RATE

1000.00 800.00 600.00

MO OBILE PROPOSED RCA BIT RATE

400.00 200.00

MO OBILE FIX XEDQPENCODER Y PSNR P

0.00 12

34

56

78 9

MO OBILE PROPOSED RCA Y PSNR

Fig. 4. Illustration of Bit rate & PSNR for Fixed QP Encoder and the Proposed RCA with Layer 0 & 1 for Mobile Video

A comparative analysis is made for all the three video sequences (Bus, Foreman & Mobile) M with layer 0 and layer 1 for which the minimum numbber of bits required to maintain the Y-PSNR level is shown inn fig.2, fig.3, and fig.4 for the FixedQPEncoder in JSVM 9.19.15 and the proposed RCA. From the observation, we cann say that our proposed RCA outperforms FixedQPEncoder in i bit rate while maintaining the required PSNR. Also our propoosed RCA does not involve any iterations whereas the FixedQPEncoder can go for too many b rate. iterations to achieve the target bit V.

CONCLUSION O

We have proposed an Adaptive Rate Control Algorithm for H.264/SVC, here the algoriithm is two pass, first, initial QP value is estimated by Simplified R-D Model derived from Cauchy-Density based pdf andd Buffer fullness status. Second, this QP is used for obtaining minimum m cost function for mode decision through which motionn vector is computed under Rate Distortion Optimization. Ourr algorithm is compared with FixedQPencoder of JSVM 9.19.15 and shows that it achieves better PSNR and reduction in Bit rate. Furthermore our algorithm outperforms the FixxedQPencoder of JSVM 9.19.15 without any iteration and a virttual buffer management. REFER RENCES [1]

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[12] A. Ortega, “Variable bit-rate video coding,” in Compressed C Video over Networks, M.-T. Sun and A. R. Reibman, Eds. New York: Marcel Dekker, pp. 343–382, 2000. [13] MPEG-2, Test Model 5, Doc. ISO/IEC JTC1/SC29 WG11/93-400, 1993.4. [14] ITU-T/SG15. Video Codec Test Model, Near-Teerm, TMN8 ITU Study Group 16, Video Coding Experts Groups, Portlaand, USA, 1997, Doc. Q15-A-59. EC JTC1/SC29/WG11 [15] MPEG-4 Video Verification Model v8.0, ISO/IE Coding of Moving Pictures and Associated Auudio MPEG97/N1796, 1997.7. [16] Z. G. Li, F. Pan et al, Adaptive basic unit layerr rate control for JVT, ISO/IEC JTC1/SC29/ WG11 and ITU-T SG16/Q Q6, Doc. JVT-G012, In the 7th Meeting, Pattaya II Thailand, 2003.3. [17] H. Schwarz, D. Marpe, T. Wiegand, Overview of the scalable video EE Trans. Circuits Syst. coding extension of the H.264/AVC standard, IEE Video Technol., vol. 17, no. 9, pp. 1103-1120,20007 [18] M. Wien, H. Schwarz, T. Oelbaum, Performannce Analysis of SVC, IEEE Trans. Circuits Syst. Video Technol., vol.. 17, no. 9, pp. 11941203, 2007.9. [19] Julien Reichel, Heiko Schwarz, Mathias Wien, Joint Scalable Video Model JSVM-12 text, ISO/IEC JTC1/SC29/WG111 and ITU-T SG16/Q6, Doc. JVT-Y202, In the 25th Meeting, Shenzhen, 2007.10. 2 [20] Y. Liu, Z. G. Li, Y. C. Soh, Rate control off H.264/AVC scalable extension, IEEE Trans. Circuits Syst. Video Technnol., vol. 18, no. 1, pp. 116-121, 2008.1. main based rate control [21] Y. Pitrey, M. Babel, O. Déforges, J. Viéron, ȡ-dom scheme for spatial, temporal, and quality scalaable video coding, In Visual Comm. and Image Proces. 2009, SPIE Ellectronic Imaging, vol. 7257, 2009.1. [22] Hongtao Yu, Zhiping Lin and Feng Pan”An Im mproved Rate Control Algorithm for H.264,” 0-7803-8834-8/05 ©2005 IEEE. I [23] S. Sanz-Rodr´Õguez and F. D´Õaz-de-Mar´Õa, “RBF F-based QP estimation model for VBR control in H.264/SVC,” Circuits and a Systems for Video Technology, IEEE Transactions on, vol. 21, no. 9, 9 pp. 1263 –1277, Sep. 2011. [24] Tao ZHU and Xiong-wei ZHANG, “Rate Contrrol Scheme for Spatial Scalability of H.264/SVC,” PRZEGLĄD ELE EKTROTECHNICZNY (Electrical Review), ISSN 0033-2097, R. 87 NR 9a/2011 9 [25] N. Kamaci, Y. Altunbasak, and R. Mersereau, ”F Frame bit allocation for the H.264/AVC video coder via Cauchy-deensity-based rate and distortion models,” Circuits and Systems for Viddeo Technology, IEEE Transactions on, vol. 15, no. 8, pp. 994–1006, 20005. [26] T. Wiegand, H. Schwarz, A. Joch, F. Lossentinni, and G. J. Sullivan, “Rate-Constrained Coder Control and Comparison of Video Coding Standards”, IEEE Transactions on Circuits andd Systems for Video Technology, Vol. 13, No. 7,pp. 688-703, July 20033. [27] S. Ma, Z. Li, and F. We, ”Proposed draft of adaptive a rate control,” JVTH017, 8th JVT Meeting, Geneva, Switzerlandd, May 2003. [28] J. Vieron, M. Wien, and H. Schwarz, “JSVM 111 software,” 24th Joint Video Team (JVT) of ISO/IEC MPEG & IT TU-T VCEG Meeting, Geneva, Doc. JVT-X203, Jul. 2007. [29] M. Wien and H. Schwarz, “Testing conditioons for SVC coding efficiency and JSVM performance evaluation,” 16th JVT of ISO/IEC MPEG & ITU-T VCEG Meeting, Poznan, Docc. JVT-Q205, Poznan, Poland, Jul. 2005. E Transmission [30] Thyagharajan K K and Ramachandran V, ‘An Effective and Browsing Methodology for Streaming Video’, Journal of Computer Science, Vol. 2, No. 4, pp. 326-332. ISSN: 1549-33636. [31] Thyagharajan K K and Ramachandran V, “Segm mentation Analysis for Effective Usage of Network Resources inn Video Streaming”, International Conference on Computational Intelliigence and Multimedia Applications 2007, Dec 2007, pp. 383-387. Published in IEEE explore. [32] Balaji L and Thyagharajan K K, “Performance Annalysis of Quantization Parameter for Base and Enhancement Layer in H.264 Scalable Video Information and Coding,” National Conference on Communication,Vol1. pg,139-143, ISSN:2231-4946 Apr. 2013.

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Dr. K.K. Thyagharajan T obtained his B.E., degree inn Electrical and Electronics Engineerinng from PSG College of Technologgy (Madras University) and received his M.E., degree in Applied Electroniccs from Coimbatore Institute of Technologgy in 1988. He also possesses a Post Graaduate Diploma in Computer Applicatioons from Bharathiar University. He obtained his Ph.D. degree in Information and Communication Engineering (Computer Science) from College of Engineering Guinndy, Anna University. He has twenty five years of teaching experience. e Now he is the Dean (Academic) of R.M.D. Engineering College. He has written 5 books in Computing including “Flash MX 2004” published by McGraw Hill (INDIA) and it has been recommended as text and reference book by universsities and Polytechnics. He has published more than 50 paperrs in National and International Journals and Conferences. Hee is a grant recipient of Tamil Nadu State Council for Sccience and Technology. His biography has been published in the 25th Anniversary Edition of Marquis Who's Who in the World. He has been invited as chairperson and delivered speecial lectures in many National and International conferencess and workshops. His current interests are Multimedia Networks, Content Based Information Retrieval, Mobile Computing, Web services, Data Mining, e-learning, Image Prrocessing, Microprocessors and Microcontrollers. He is reviiewer for many International Journals and Conferences. He is a recognized supervisor for Ph.D by Anna University Chennai, MS University and Sathyabama University, and noow 10 students are doing Ph.D. under his supervision. Mr. L. Balaji B obtained his B.E., degree in Elecctronics and Communication Engineerring from University of Madras and recceived his M.E., degree in Communnication Systems from Anna Universitty. He also possesses a Diploma in Compputer Technology from DOTE, Chennai. Currently he is a Part time Ph.D. Research scholar of Annna University, Chennai under the faculty of Information and Communication Engineering (Computer Science) and workking as Assistant Professor at Velammal Institute of Tecchnology, Panchetti. He has published 3 papers in International Journals and Conferences and more than 5 papers in Nattional Conferences. His current interests are Video Coding, Wireless Communication & lines & Waveguides, Networks, Transmission Microprocessors, Microwave & Communication Engineering.

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