Fast Rate Control Algorithm in Frame-layer for H.264/AVC Video Coding

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IEEE Transactions on Consumer Electronics, Vol. 58, No. 3, August 2012

Fast Rate Control Algorithm in Frame-layer for H.264/AVC Video Coding Myoung-Jin Kim and Min-Cheol Hong, Member, IEEE Abstract — In this paper, we propose a fast rate control algorithm in frame-layer of H.264/AVC video coding standard. We derive a statistical relation between quantization parameter (QP) and encoded bits of inter-coded blocks. In addition, new complexity estimation is defined to determine weight of complexity of current frame. Using the statistical property and the complexity updating function, we determine the amount of bits as a function of quantization parameter, so that computational cost of rate control is significantly reduced with coding gain improvement and stable buffer management. Experimental results demonstrate the capability of the proposed algorithm.1 Index Terms — H.264/AVC, rate control, quantization parameter, complexity estimation, computational cost

I. INTRODUCTION H.264 has been standardized to obtain better coding performance than the other video coding standards [1]-[4]. It has the advantage of coding efficiency and improved network adaptation, and therefore it is expected that H.264/AVC will be widely used in many application areas. Applications related to video transmission are affected by time-varying bandwidth channels, and therefore it is necessary to exploit the bit rate control algorithm to maximize the coding performance in channel variation environment. The H.264 encoder employs complicated features such as variable block size motion estimation, spatial intra prediction, and so on. In addition, rate distortion optimization (RDO) has been considered as an important issue when it comes to maximizing the visual quality of a given channel bandwidth. However, RDO poses a big problem for rate control in H.264, the well-known chickenand-egg dilemma [5], [6]. To perform RDO, the quantization parameter (QP) should first be determined by using the mean absolute difference (MAD) of current frame and/or Macro-Block (MB). On the other hand, to perform rate control, QP can only be obtained according to the coding complexity and number of target bits, which are determined by the residue between an original frame and the predicted frame after the determination of the RDO mode. To resolve this problem, a linear model was presented to predict MAD in [7], where a fluid flow traffic model was used to allocate the target bit rate for a current frame or MB. In addition, a 1

Myoung-Jin Kim and Min-Cheol Hong are with the School of Electronic Engineering, Soongsil University, Korea (e-mail: [email protected]; [email protected]). Contributed Paper Manuscript received 05/29/12 Current version published 09/25/12 Electronic version published 09/25/12.

target bit estimation method for each frame was reported to satisfy hypothetical reference decoder (HRD) requirements in [6]. However, this leads to weakness in predicting picture characteristics since it does not take frame complexity into account. Many rate control methods have been exploited in previous works [8]-[10]. However, they cannot be directly applied to H.264 rate control due to the different video encoding mechanism. A peak-signal-to-noise-ratio (PSNR) based frame complexity measurement method was presented to improve the existing MADbased complexity measurement methods [11]-[13]. These approaches use the quadratic R-D model to determine a QP with an estimated target-bit rate and an estimated MAD. The estimated MAD is different from the actual computed MAD in scene transition frame, so that an inaccurate QP can be obtained due to very low correlation between a current frame and the previous frames. Furthermore, the pre-analysis procedure of the above methods requires expensive computational cost and therefore it may be a major bottleneck in real-time transmission applications of H.264/AVC. To resolve the complexity problem, we propose a lowcomputing frame-layer rate control algorithm for frame bit allocation by considering both buffer status and frame complexity for H.264/AVC. Using the statistical relation between QP and the inter-coding mode block of video sequences, we define a QP adjustment model for the inter-coding mode, and the frame complexity is estimated after encoding each frame to improve previous MAD-based complexity measurement. The experimental results show that the proposed algorithm has the capability to dramatically reduce computational cost with coding gain improvement and stable buffer management. This paper is organized as follows. In Section II, the background of H.264/AVC rate distortion optimization is briefly addressed. Section III describes the proposed rate control algorithm in the frame-layer level. A new QP adjustment model based on training video sequences is defined, and a target bit algorithm for each frame is proposed. Also, updating frame complexity determining the weight of current frame is explained. Finally the experimental results and conclusions are presented in Sections IV and V. II. H.264/AVC RATE CONTROL ALGORITHM As shown in Fig. 1, the number of generated bits per frame of H.264/AVC JM (Joint Model) is determined according to the redundancy variation, so that the picture quality for a group of target bits is maximized by the dynamical adjustment

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M.-J. Kim and M.-C. Hong: Fast Rate Control Algorithm in Frame-layer for H.264/AVC Video Coding

of encoder parameters. In this section, we briefly describe the background of the rate control algorithm of the H.264 video coding standard.

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Also, Np denotes the total number of P-frames remaining for encoding. Then, the target buffer Tbuf can be obtained on the basis of the actual buffer occupancy, the target buffer level, the frame rate, and the available channel bandwidth. It is defined as  (n)  Tbuf

u   Tbl ( n)  Bc (n)  , Fr



(3)



 ( n)  , Tbuf ( n)  min U ( n), max  L ( n), Tbuf

where θ represents a constant, and U(n) and L(n) denote the upper and the lower values of the buffer. Then, the number of target bits of the n-th P-frame, T(n) is determined as the weighted combination of Tbuf and the bits equally assigned to each frame. This can be written as: T ( n)    Tr ( n)  (1   )  Tbuf ( n), (  0.5) Tr ( n)  R / ( N  n),

where Tr and R represent the bits equally assigned to each frame and the number of remaining bits for not-coded frames, and λ denotes a weighting factor between Tr and Tbuf.

Fig. 1. Rate control structure of H.264/AVC JM reference model

To estimate the target bits of current frame, H.264/AVC employs a fluid traffic model based on the linear tracking theory [7]. For one group of pictures (GOP) consisting of the first I (Intra) frame and the subsequent P (Inter) frames, the virtual buffer occupancy of n-th frame in a GOP is written as    u  Bc ( n)  min  max  0, Bc ( n  1)  A( n  1)   , Bs  , Fr     Bc (1)  Bs / 8,

(1)

where A(n-1), u, and Fr represent the number of encoded bits by the (n-1)-th frame, the available channel bandwidth for the n-th frame, and the pre-defined frame rate, respectively. In addition, Bs denotes the buffer size which is defined by level and profile. In general, the initial buffer Bc(1) has any value. However, it can be set to a low level if the bit fluctuation is very small, so that the PSNR fluctuation can be greatly reduced. The bit allocation is implemented by pre-defining a target buffer level, so that the corresponding QP is determined. In general, QP value of the first P frame is given at the GOP layer. The target buffer level (Tbl) for P frames in a GOP is determined as: Tbl (2)  Bc (2), Tbl ( n)  Tbl (n  1) 

Tbl (2)  Bs / 8 , N p 1

(4)

(2)

where Bs and Bc(2) represent the buffer size and the actual buffer occupancy after encoding the first P-frame, respectively.

III. PROPOSED RATE CONTROL ALGORITHM It has been reported that H.264/AVC outperforms the other video coding standards in terms of coding efficiency. However, it may suffer from very expensive computational cost, so that the applications can be restricted. Therefore, it is necessary to exploit a low-complexity algorithm with minimizing the loss of coding efficiency. In this section, we address a new low-complexity frame-layer rate control algorithm for target bit allocation. Considering a target bandwidth, a spatial complexity of frames, and the fluid traffic model, we determine the optimal number of target bits for the current frame. The proposed algorithm consists of three major handling stages: 1) determination of target bits for frame, 2) choice of QP value for the target bits, 3) updating frame complexity. A. Determination of target bits in frame level In general, weight of the frame complexity, virtual buffer occupancy and target buffer level has been considered as important parameters for estimating a target bits for a frame. In this work, virtual buffer occupancy Bc(n) and target buffer level Tbl(n) are computed as Bc (n)  A( n  1) 

u  0.5, Fr

(5)

and Bc  if n  1  Bc  N  1 , p  Tbl ( n)   . Tbl ( n  1)  Bc , otherwise  N p 1

(6)

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As shown in (6), the target buffer level is reduced from the buffer occupancy for the first P frame when I frame in a GOP is encoded. In addition, the target buffer level is reduced from the previous target buffer level after the first P frame is encoded. In order to estimate the number of target bits that can be set to a frame when encoding the current frame, the following equation is used to predict buffer size on the basis of the buffer occupancy and target buffer level. Tbuf ( n) 

u    Tbl (n)  Bc ( n)  . (  0.8) Fr

(7)

Then, when the buffer size is predicted, the buffer conditions such as the upper limit and the lower limit is taken into account to decide on the final buffer size Tbuf(n), as shown in (3). In this work, the weight of buffer occupancy and buffer level is set to 0.8 as an initial value. This was done to prevent the buffer overflow of some conditions. Then, the final target bits T(n) of the n-th frame are determined by the target buffer size in (7) and the estimated target bits in (8). They are written as B ( n)   (8) Tr     c    (1   )  Tbuf  0.5  , N n  and (9) T ( n)  (   W p )   (1   )  Tr  0.5  , where α represents a weight of the predicted target buffer size and β denotes a complexity weight of current P frame. The proposed algorithm is different to H.264/AVC in that Tr is determined by buffer occupancy of the current frame and the weight of the predicted target buffer size. This is also intended to prevent buffer overflow by using current buffer occupancy instead of remaining bits. In previous approaches, the weights of the target buffer size and the encoding bits are applied on the basis of the remaining bits for the final target bits T(n). However, the assigned number of bits does not consider the bit information of the previous frames, and therefore the bits assignment for the current frame is not effective in terms of the temporal correlation. In this work, we use the weight of complexity of the current P frame (Wp), the weight of the bits for encoded frames, and the QP value so that the bits information of the previous encoded frame can be incorporated into determining the target bits of the current frame.

of the n-th frame, which requires additional operations to estimate distortion of previous frames. In this work, we exploit a statistical model for low computational rate control process, so that an adequate QP value can be directly determined from the model for a given predicted bits. For doing it, we performed JM 12.1 software with various CIF video sequences such as ‘Akiyo’, ‘Stefan’, ‘News’, ‘Costguard’, and ‘Foreman’. From the experiments, we obtained the statistical relation between encoded average bits and QP values for P frames, as shown in Table I. Using the statistics, the characteristics of average bits of test sequences as a function of QP can be drawn as Fig.2. Under the assumption that the function in Fig. 2 is piecewise linear, the statistical relation between QP value and the corresponding predicted bits can be written as

Bits (QPn )    e (  ( QPn )) , (0  n  51)

(11)

where Bit(QPn ) represents the predicted bits when QP value is equal to n, and γ and μ are constants which satisfy the statistical characteristics shown in Fig.2. TABLE I AVERAGE BITS OF P FRAMES FOR QP VALUES QP … 17 18 19 20 21 22 23 24 25 26 …

akiyo … 51,395 42,484 36,361 28,845 24,139 20,027 16,023 12,896 10,634 8,371 …

foreman … 82,768 68,656 59,772 49,106 42,086 36,069 30,299 25,432 22,152 18,285 …

costguard … 189,550 169,702 155,991 137,400 123,580 110,859 96,701 84,335 75,887 64,033 …

news … 28,541 24,412 21,704 18,580 16,430 14,542 12,610 10,993 9,831 8,398 …

stefan … 171,396 153,408 140,248 124,006 111,703 99,234 87,396 75,787 68,227 57,865 …

B. Choice of QP for target bits H.264/AVC JM (Joint Model) uses a QP conversion process to decide QP value of current frame by the predicted target bits. It is Fig. 2. Average bits as a function of QP

x  MADn x2  MADn T ( n)  1  , QP QP

(10)

where x1 and x2 represent the first and second order coefficients, and MADn denotes the mean absolute difference

Then, the problem at hand is to find an adequate QP value of current P frame for a given target bits. Therefore, it is necessary to define the range of possible bits that can be generated by a specific QP value. Using (11), we define a

M.-J. Kim and M.-C. Hong: Fast Rate Control Algorithm in Frame-layer for H.264/AVC Video Coding

simple QP conversion function for a given target bits as

QPec  QPn if Bits (QPn )  A  Tbits  Bits (QPn )  B , A

Bits (QPn )  Bits (QPn 1 )

, 2 Bits (QPn 1 )  Bits (QPn ) B , 2

(12)

where QPec and Tbits represent the estimated QP value and the given target bits, respectively. When QP value is determined for current P frame, QP difference between frames should be considered to reduce the deterioration of the video quality between frames. In order to minimize it, QP value of the previous frames is considered to determine that of the current P frame. It is





QPc  min QPp  QP, max(QPp  QP, QPec ) ,

(13)

where min (.) and max(.) denote the minimum and maximum operations, and QPp represents QP value of the previous frame. In addition, ΔQP is the maximum of QP difference between frames, where ΔQP is set to ±2. C. Updating frame complexity The determination of complexity degree of a frame is very important process to maximize coding performance. In general, control of buffer and virtual buffer occupancy for preventing overflow/underflow and encoded bits of previous P frames have been incorporated into complexity determination process of a current frame. In this work, we consider virtual buffer occupancy Bc and lower/upper limitation of buffer to determine a weight of complexity of previous encoded frames to estimate the complexity of current frame. Taking account into the difference between encoded bits and allocated bits (u/Fr) of a frame, the virtual buffer occupancy and upper/lower limits of the buffer for the n-th frame can be written as   u Bc  Bc   A(n  1)   0.5  , Fr  

(14)

  u U ( n)  U (n  1)   A( n  1)   0.5  , Fr  

(15)

  u L ( n)  L ( n  1)   A(n  1)   0.5  . F r  

(16)

Then, in order to estimate the target bits of the current P frame to be encoded, the weight of the complexity, Wp is decided by statistical information on the bits that were already generated. It is written as W p  (  A( n  1)  (1   )  Sbits )  QPp  0.5,

(17)

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where Sbits represents the average bits of the previous P frames in frame section from A(n-1) to A(n-3) that were already encoded. The above is reasonable because the current frame to be encoded is highly correlated with adjacent encoded frames when quantization parameter of current frame is same to that of the previous frames. In addition, λ controlling the relative degree of each term in (17) is experimentally determined. In this work, it is set to 0.67. IV. EXPERIMENTAL RESULTS The proposed algorithm was tested with various CIF video sequences, target bits rate, and frame rate, where test sequences used to determine the relation between QP value and encoded bits in (11) were excluded in the experiments. We compared the proposed algorithm with JM12.1 rate control algorithm and MAD-based complexity measurement method in [11]. In order to evaluate of the performance, PSNR (Peak Signal Noise to Ratio), encoded bits, running time, and buffer status were used, where a high-density measurement timer was used to compare the running time of each algorithm, and the range of measurement was the part which is controlled by rate control. In addition, the important coding conditions of H.264 JM 12.1 are shown in Table II. In this simulation, we considered only the statistical characteristics of P frames to predict the complexity of a frame, so that GOP structure of a sequence is consisted of P frames without B frames for all frames after the first I frame. TABLE II EXPERIMENTAL CONDITIONS Profile MV Resolution Hardamard Transfrom RDO Search Range Reference Frames Symbol Mode GOP size Encoding Frame Test Channel Bandwidth Frame Rate

Baseline 3.0 1/4 Pel ON ON ±16 1 CAVLC 30 300 128Kbps ~768Kbps @10, @15, @20, @30

Table III, IV, and V show performance comparisons in term of average PSNR, encoded bit rate, and running time of various CIF sequences when target bit rate is 384 kbps and 128 kbps at 15 fps (frame per second) and 30 fps, respectively. The results show that all approaches have the similar capability in terms of satisfying the target bits. On the other hand, it is verified that the proposed algorithm overcomes the other approaches in terms of coding gain (about 0.07-0.08 dB gain over the others), and that the proposed algorithm requires significantly low computational cost than the others. In order to evaluate the performance of computational cost, average encoding time reduction (AETR) is used. It is calculated by AETCOM AETR  (1  )  100. (18) AETJM

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Sequence (CIF) Container Mobile Hall Monitor M&D Paris Flower Bus Children Tempete Waterfall AVG

Sequence (CIF) Container Mobile Hall Monitor M&D Paris Flower Bus Children Tempete Waterfall AVG

IEEE Transactions on Consumer Electronics, Vol. 58, No. 3, August 2012

JM @15 383.80 384.07 384.36 384.01 383.92 383.50 384.42 383.73 384.48 383.95 384.02

JM @15 127.94 127.98 128.10 128.23 128.18 128.03 128.27 128.00 127.96 128.01 128.07

Ref.11 @15 384.22 384.13 384.19 384.07 384.10 383.34 383.98 383.73 384.09 384.06 383.99

TABLE III PERFORMANCE COMPARISONS AT TARGET BITS RATE 384 KBPS Bit rate (Kbps) Prop. JM Ref.11 Prop. JM Ref.11 @15 @30 @30 @30 @15 @15 385.08 384.83 374.03 383.68 40.66 40.70 384.24 386.34 384.13 383.41 29.61 29.63 383.90 384.38 383.94 383.91 40.22 40.22 384.01 384.64 384.14 385.48 45.16 45.17 383.94 384.31 384.08 383.54 38.24 38.26 383.33 383.53 383.84 384.12 30.03 30.03 383.70 384.29 384.26 383.66 32.41 32.37 384.10 385.86 384.66 384.01 38.92 38.86 383.93 384.35 384.47 383.42 32.24 32.24 383.60 384.48 384.26 384.61 38.05 38.04 383.98 384.70 384.18 383.98 36.55 36.55

PSNR Y (dB) Prop. JM @15 @30 40.79 37.94 29.76 26.85 40.23 38.63 45.13 43.00 38.37 33.90 30.10 26.84 32.45 29.18 39.02 34.09 32.32 29.56 38.14 35.28 36.63 33.53

Ref.11 @30 37.97 26.85 38.65 43.00 33.87 26.86 29.18 34.05 29.58 35.27 33.53

Prop. @30 38.01 26.94 38.70 43.03 34.14 26.90 29.22 34.06 29.61 35.36 33.60

Ref.11 @15 127.99 128.03 128.09 128.10 128.19 127.79 128.08 127.96 128.05 128.03 128.03

TABLE IV PERFORMANCE COMPARISONS AT TARGET BITS RATE 128 KBPS Bit rate (Kbps) Prop. JM Ref.11 Prop. JM Ref.11 @15 @30 @30 @30 @15 @15 127.78 127.85 128.06 127.89 36.53 36.46 127.77 127.99 127.93 127.94 25.20 25.23 127.96 128.12 128.08 127.98 37.71 37.75 127.90 128.32 128.41 127.86 41.56 41.58 127.82 128.45 128.53 127.96 31.90 31.62 127.98 128.25 127.95 127.80 25.19 25.18 128.12 130.56 129.02 128.22 27.43 27.42 127.83 127.85 128.05 128.85 31.56 31.57 127.92 128.27 127.97 127.90 28.07 28.08 127.73 128.24 127.86 127.64 33.75 33.70 127.88 128.39 128.19 128.00 31.89 31.96

PSNR Y (dB) Prop. JM @15 @30 36.60 34.25 25.32 22.35 37.76 35.72 41.59 38.85 31.93 29.20 25.25 22.48 27.47 24.59 31.62 28.04 28.13 25.59 33.83 30.94 31.95 29.20

Ref.11 @30 34.21 22.33 35.70 38.79 29.20 22.43 24.52 27.99 25.57 30.93 29.17

Prop. @30 34.29 22.50 35.87 38.84 29.32 22.53 24.50 28.19 25.64 31.04 29.27

TABLE V PERFORMANCE COMPARISONS OF ENCODING TIMES (PER FRAME) Sequence (CIF) Container Mobile Hall Monitor M&D Paris Flower Bus Children Tempete Waterfall Average AETR (%)

JM @15 4,986 4,971 4,972 4,977 4,898 4,751 4,873 4,718 4,859 5,143 4,915 0.0

ENCODING TIME (㎲) AT 384 KBPS Ref.11 Prop. JM Ref.11 @15 @15 @30 @30 6,573 9 4,773 6,356 6,556 6 4,749 6,321 6,571 6 4,831 6,387 6,554 9 4,809 6,399 6,492 6 4,697 6,272 6,311 17 4,545 6,171 6,448 12 4,648 6,261 6,254 13 4,411 6,017 6,432 6 4,607 6,173 6,831 10 4,940 6,531 6,502 9 4,701 6,289 -32.3 99.8 0.0 -33.8

Prop. @30 9 14 10 6 14 6 6 14 6 9 9 99.8

JM @15 4,689 4,592 4,751 4,781 4,547 4,457 4,483 4,228 4,463 4,887 4,588 0.0

ENCODING TIME (㎲) AT 128 KBPS Ref.11 Prop. JM Ref.11 @15 @15 @30 @30 6,239 6 4,440 6,041 6,208 16 4,485 6,065 6,336 6 4,549 6,143 6,370 6 4,529 6,152 6,120 8 4,359 5,977 6,019 7 4,249 5,855 6,084 13 4,227 5,782 5,847 6 4,037 5,628 6,087 13 4,200 5,797 6,428 6 4,566 6,138 6,174 9 4,364 5,958 -34.6 99.8 0.0 -36.5

Prop. @30 6 6 6 6 6 6 6 7 15 6 7 99.8

※ Encoding Time (㎲) is only measured time unit for the rate control algorithm part, especially at timer, which is the current value of the high-resolution performance counter.

In (18), AETJM represents average encoding time with JM rate control algorithm, and AETCOM denotes average encoding time with the comparative rate control algorithms such as [11] or proposed algorithm. Table V shows that the proposed algorithm has the capability to achieve 99% computational reduction than JM 12.1 rate control algorithm. In addition, it is observed that the computational reduction comes from the direct estimation of QP value and the adequate updating process of complexity, and that the coding

gain improvement is due to the accurate prediction of the weight of complexity. Fig. 3 and Fig. 4 show PSNR and buffer fullness comparisons as a function of frame number of ‘Mobile’ sequence at 128 kbps, respectively. Reference [11] results in higher PSNR fluctuation than the other methods since buffer fullness suddenly changes at certain frames, leading to annoying artifacts to human viewer. Also, it is observed that buffer overflow more frequently occurred than the others, so that there is more possible for encoded data to be lost through

M.-J. Kim and M.-C. Hong: Fast Rate Control Algorithm in Frame-layer for H.264/AVC Video Coding

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Fig. 3. PSNR comparisons as a function of frame number of CIF ‘Mobile’ sequence (128Kbps, @15)

Fig. 4. Buffer fullness comparisons as a function of frame number of CIF ‘Mobile’ sequence (128Kbps, @15)

(a) 15 frames per second

(b) 30 frames per second

Fig. 5. PSNR comparisons as a function of bit rate of CIF ‘Mobile’

network and transmission. JM leads to less PSNR fluctuation than [11]. However, buffer management is not relatively effective, so that it leads to lower PSNR for long period. On the other hand, the proposed algorithm has the capability to effectively manage the buffer occupancy, leading to stable PSNR.

Fig. 5 and Fig. 6 show PSNR comparisons as a function of bit rates at 15 fps and 30 fps for ‘Mobile’ and ‘Container’ sequence, respectively. The results denote that the coding gain of the proposed algorithm is consistently obtained, regardless of target bit rates.

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(a) 15 frames per second

(b) 30 frames per second

Fig. 6. PSNR comparisons as a function of bit rate of CIF ‘Container’

(a) 128Kbps and 15 frames per second

(b) 128Kbps and 30 frames per second

Fig. 7. Encoded bits comparisons of CIF ‘Mobile’

Fig. 7 shows the encoded bits comparisons per second of ‘Mobile’ sequence at 15 fps and 30 fps when the target bits are 128 kbps, respectively. It demonstrates that the proposed algorithm and [11] satisfy the target bits except for the first intra frame. On the other hand, the rate control algorithm of H.264/AVC does not frequently satisfy the target bit. It shows that the frequency of the deviation is higher, as the target bits are lower. Novelty of the proposed algorithm is that it has the capability to significantly reduce the computational cost with the improvement of coding performance, and to maintain stable buffer occupancy. Therefore, the proposed algorithm can be used for various applications.

with marginal improvement of coding performance (average 0.08 dB coding gain with 99% computational reduction than JM12.1 rate control algorithm), but also to effectively control the overflow/underflow of the buffer status. ACKNOWLEDGMENTS This work was supported by the Ministry of Knowledge Economy, Korea, under the Information Technology Research Center support program supervised by the National IT Industry Promotion Agency (NIPA-2012-H0301-12-2006). REFERENCES [1]

V. CONCLUSION In this paper, we propose a low computational rate control algorithm for H.264/AVC video coding standard. A new statistical model determining quantization parameter for a given target bits is proposed, and a new complexity updating process in frame layer is exploited so that the choice of the weighting coefficients of previous encoded frames can be possible. From the experimental results, it is demonstrated that the proposed algorithm has the capability not only to reduce the computational cost

[2] [3]

[4] [5]

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879 BIOGRAPHIES

Myoung-Jin Kim received the B.S. and M.S. degrees in the Department of Computer Science from the Korea National Open University (KNOU), Seoul, Korea, in 2002 and 2005, respectively. Dr. Myoung-Jin Kim got his Ph.D. from the Soongsil University at Seoul in August 2010. He has been working as a teaching and research professor at the Soongsil University. His research interests include video coding, fast image processing algorithms, and multimedia communications. Min-Cheol Hong (M’97) received B.S. and M.S. degrees from Yonsei University in 1988 and 1990, and Ph.D Northwestern University in 1997, respectively. He worked for LG Information and Communication Research Center and LG Electronics Corporate Research Center in 1990-1991 and 1998-2000, respectively. Also, he worked as a post-doctorial research Fellow at Northwestern University in 1997-1998. He joined Soongsil University in 2000, where he is currently an associate professor. His research areas include image/video restoration, nonlinear video processing, motion analysis and modeling, blind image deconvolution, and video coding.