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G. Jeon et al.: Application of Bayesian Belief Network in Reliable Analysis for Video Deinterlacing

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Application of Bayesian Belief Network in Reliable Analysis for Video Deinterlacing Gwanggil Jeon, Rafael Falcon, Donghyung Kim, Rokkyu Lee, and Jechang Jeong Abstract — In this paper, we illustrate that Bayesian networks (BNs), which are also known as belief networks, are well-suited for image processing. We provide case studies on video deinterlacing methods. The proposed efforts at modeling weight measuring process involved in weight assignment of conventional deinterlacing methods that are commonly used for industrial world. Using probabilistic BNs, the system determines the weights and interpolates the missing pixels robustly. The results of empirical trial show that the proposed system can deal successfully with several types of images containing motion or detail.1 Index Terms — Bayesian networks, deinterlacing, expert systems, reasoning under uncertainty modeling, statistical inference.

I. INTRODUCTION Deinterlacing converts each field into a frame, so the number of pictures per second remains constant, while the number of lines per picture is doubled [1]. However, an interlaced TV signal in the vertical direction does not satisfy the demands of Nyquist sampling theory. Effective linear sampling rate conversion theory cannot be employed for interpolation. This causes different visual artifacts, such as twitter artifacts, flicker artifacts, and unwanted staircase effects, which decrease the picture quality of the interlaced video sequence [2]. In order to resolve these issues, deinterlacing methods have been investigated by many authors and various methods have been proposed with different degrees of complexity and qualities of reconstruction [3-7]. Deinterlacing methods can be roughly categorized into two major groups: methods with motion compensation [3], [4] and methods without motion compensation (Bob [5], Weave [6], and STELA [7]). The latter are chosen more often due to less error propagation and computational complexity, especially in real-time applications. In this paper, we focus on a Bayesian network-based deinterlacing (BND) technique which does not utilize a motion compensation process. The proposed BND method is specific to sequences with both high and low motion regions, and also both edge and smooth regions. The idea This research was supported by Seoul Future Contents Convergence (SFCC) Cluster established by Seoul R&BD Program. Gwanggil Jeon, Jechang Jeong, and Rokkyu Lee are with the Department of Electronics and Computer Engineering, Hanyang University, 17 Haengdang-dong, Seongdong-gu, Seoul, Korea (e-mail: {windcap315, jjeong}@ece.hanyang.ac.kr, [email protected],). Rafael Falcon is with Computer Science Department, Universidad Central de Las Villas, Carretera Camajuani km 5 ½ Santa Clara, Cuba (e-mail: [email protected]). Donghyung Kim is with Radio and Broadcasting Research Division, Broadcasting Media Research Group, ETRI, 138 Gajeongno, Yuseong-gu, Daejeon, 305-700, Korea (e-mail: [email protected]). Manuscript received December 31, 2007

behind our proposed BND method is that motion or detail information is extracted by calculating four pixel-wise parameters between adjacent field images. Since the proposed method is based on pixel-wise processing, the complexity in terms of data bandwidth and memory usage is usually low. This paper widens the usage of Bayesian networks (BNs) to help experts make sense of complex deinterlacing systems using a Bayesian model as an interactive interpolation method. BNs have emerged as an effective tool for knowledge representation and inference, and are popular in descriptive modeling schemes. They work by providing a simple way to see links between attributes of a set of records. Computationally, BNs provide an efficient way to represent relationships between attributes and allow reasonably fast probability inference. In the past decade, a number of probabilistic graphical models have been proposed [8]. BNs are widely used for simulating computational models and improving understanding in many fields. Early successes in image processing were developed for applications dealing with constrained surrounding conditions. BN appears in many real world applications including medical imaging [9], financial issues [10], document processing [11], and military target recognition [12]. However, studies involving deinterlacing systems that are based on BNs have not been proposed yet. In this paper, a technique for constructing weight-measuring for three conventional deinterlacing methods through a BN selection rule is presented†. They complexity of the technique is also discussed. After the weight measuring process, we consider each method’s weight before multiplying candidate deinterlaced pixels. From this viewpoint, the proposed BND method is different from conventional motion adaptive deinterlacing algorithms. The remainder of the paper is structured as follows: in Section II, the concepts of BNs, a proposed BND method, and an interpolation strategy are described. The experimental results that demonstrate the usefulness of the approach are presented in Section III. These results are compared to other well-known, already existing deinterlacing methods. Finally, conclusions are presented in Section IV.

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Note that the proposed BND method does not provide any new ideas about deinterlacing algorithms. Rather, it uses complicated BNs for implementation and develops a weight measuring system that delivers correct weights at the right time. In order to simplify implementation, we adopt three old-fashioned but still popular deinterlacing methods: Bob, Weave, and STELA.

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IEEE Transactions on Consumer Electronics, Vol. 54, No. 1, FEBRUARY 2008

II. PROPOSED DEINTERLACING ALGORITHM A. Procedure of the Proposed Bayesian Network BNs can be considered knowledge representations. A BN is an inference engine that can be helpful for many issues. Its benefits include representation of domain-specific knowledge in a computationally efficient, rapid structure that is compatible with human reasoning, it is adaptable, easy to construct, contains explicit uncertainty characterization, is easily generalizable, and is easy to retrain after pruning or adding new features or training data [13]. These benefits make them particularly suitable for real-world applications where information can be partial, incorrect, or unfinished. In the proposed method, the BND is designed by following the procedure. First, we choose to limit which attributes to include in the BND model. Second, if the valuable attributes are chosen in the model, the range of the continuous attributes and the states of the discrete attributes are established. Third, we describe the graphical configuration that connects the attributes. Fourth, we design the quantitative part, i.e., we choose distributional relations for all attributes and fix parameters to stated distributions. Fifth, a testing process is completed with both sensitivity analysis and model behavior in well-known scenarios. Finally, the model is determined to be complete. B. Description of the Employed Deinterlacing Methods In this section, we briefly describe three of the previous algorithms. A 3D localized window was used to calculate directional correlations and to interpolate the current pixel, as shown in Fig. 1. Here, the set {u, d, r, l, p, and n} represents {up, down, right, left, previous, and next}, respectively. The pixel xm(i,j,k) is the m method utilized deinterlaced pixel. Bob is an intra-field interpolation method which uses the current field to interpolate the missing field and to reconstruct one progressive frame at a time. The current pixel xB(i,j,k) is then determined by: ⎧ xorg ( i, j , k ) , ⎪ xB ( i , j , k ) = ⎨ u + d , ⎪ ⎩ 2

j mod 2 = N mod 2 otherwise

(1)

where (i,j,k) designates the position, xorg(i,j,k) is the input field defined for j mod 2 = N mod 2, k is the field number, and xB(i,j,k) represents the Bob method utilized deinterlaced pixel. It is well known that Bob exhibits no motion artifacts and has minimal computational requirements. However, the input vertical resolution is halved before the image is interpolated, reducing the detail in the progressive image. Inter-field deinterlacing is a simple deinterlacing method. The output frame xW(i,j,k) is defined in (2), ⎧⎪ xorg ( i, j , k ) , xW ( i, j , k ) = ⎨ ⎪⎩ p,

j mod 2 = N mod 2 otherwise

(2)

where xW(i,j,k) represents a Weave method utilized deinterlaced pixel The Weave technique results in no

Fig. 1. Spatio-temporal window for direction-based deinterlacing.

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(a) (d) Fig. 2. Subjective view results for the 31st Stefan sequence: (a) the Bob method operational results; (b) the Weave method operational results; (c) the STELA method operational results.

degradation of static images, but edges exhibit significant serrations, which is an unacceptable artifact in a broadcast or professional television environment. For measurement of the spatio-temporal correlation of the samples in the window, six directional changes are considered. The measurement ICΦ,θ is the intensity change in the edge direction represented by θ(={45o, 90o, 135o}), in the Φ(={SD, TD}) domain. ICSD ,45o = ur − dl , ICSD ,90o = u − d , ICSD ,135o = ul − dr ICTD ,45o = pr − nl , ICTD ,90o = p − n , ICTD ,135o = pl − nr

(3)

Then, the output of the directional-based algorithm is obtained by: j mod 2 = N mod 2 ⎪⎧ xorg ( i, j , k ) , xS ( i, j , k ) = ⎨ otherwise ⎪⎩median ( Ω, u , d , p, n ) ,

(4)

Here, Ω is the average value of two samples with a minimum directional change among six ICΦ,θ values. The point xS(i,j,k) represents the STELA method utilizing a deinterlaced pixel. The STELA algorithm provides good performance, can eliminate the blurring effect of bilinear interpolation, and gives both sharp and straight edges. In the literature, conventional deinterlacing methods have been reported that interpolate missing pixels indiscriminately in the same way. Figure 2 shows an example of artifacts that are caused by Bob, Weave, and STELA methods. From a subjective point of view, Bob provides the best deinterlacing in the left part of the image. The Weave (or STELA) method offers the best deinterlaced image in the line on the ground (or advertising board) part. The purpose of our study is to assign a high weight to the methods that give a good deinterlaced image by interpolating the missing pixel.


C. Proposed Bayesian Network-Based Deinterlacing BNs are one of the best known ways to reason with uncertainty in artificial intelligence. The BNs with a graphical diagram provide a comprehensive method of representing relationships and influences among nodes. The BNs represent joint probability distribution and expert knowledge in a compact way. In mathematical terms, the concept of conditional independence is explained as follows. The BNs consist of a set of nodes that represent variables, and a set of directed edges between the nodes. Each node features a finite set of mutually exclusive states. The directed edge between nodes represents the dependence between the linked variables. The strengths of the relationship between variables are expressed as conditional probability tables. Thus, BNs efficiently encode the joint probability distribution of their variables. Each node in the BN is a random variable. The complete joint distribution of this set of q random variables Z1, Z2,…, Zq, given by the chain rule. Each entry in the joint probability table can be obtained as the product of all the appropriate elements of the prior probabilities or conditional probability tables that have been assigned to the nodes of the BNs by the chain rule in (5) [14]. q

P ( z1 ,..., z q ) = ∏ P ( z p | parent ( Z p )) i =1

(5)

where zp represents the value of the random variable Zp and parent(Zp) denotes the set of parents of the node Zp in the BNs. Instead of being conditioned on all its predecessors, the node Zp is conditioned only on its parents. Therefore, the structure learning problem of BNs is equivalent to the problem of searching for the optimum in the space of all directed acyclic graphs [15]. The proposed BN interpolator is shown in detail in Fig. 3. In the proposed BND method, four parameters SD, SMDW, TD, and TMDW are considered to be input variables of the BND. The characteristics of the above parameters are described in [16], where SW (or TW) is a 3-by-2 spatial (or temporal) domain window including {ul, u, ur, dl, d, dr} (or {pl, p, pr, nl, n, nr}), pixels in the window (NW) provides six. A constant value famp is an amplification factor that affects the size of membership functions resulting in SMDW and TMDW varying between 0 and 255. SD = u − d ⎛ max x i, j, k − min x i, j , k ⎞ × N ) (i , j ,k )∈SW ( )⎟ W ⎜ (i , j ,k )∈SW ( ⎠ SMDW = ⎝ × f amp x i j k , , ( ) ∑

⎛ max x i, j, k − min x i, j, k ⎞ × N ) (i , j ,k )∈TW ( )⎟ W ⎜ (i , j ,k )∈TW ( ⎠ TMDW = ⎝ × f amp ∑ x ( i, j , k ) ( i , j ,k )∈TW

Fig. 3. A Bayesian network model for the calculation of the probabilities required when solving the deinterlacing system.

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(6) (7)

( i , j , k )∈SW

TD = p − n

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(8) (9)

(d) Fig. 4. Histograms of (a) SD, (b) SMDW, (c) TD, and (d) TMDW.

The proposed BNs are defined by a graph with eight nodes in the domain. The root nodes (nodes without parents: Z1, Z2, Z3, and Z4) are associated with a prior probability distribution,

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TABLE I CONDITIONAL PROBABILITIES Z5 (OR Z6). THE THREE NUMBERS IN EACH ENTRY CORRESPOND TO THE STATES (Z5=HIGH, Z5=MID, Z5=LOW) (SD is High?)=yes (SD is High?)=no (SMDW is High?)=yes (0.75, 0.20, 0.05) (0.25, 0.50, 0.25) (SMDW is High?)=no (0.25, 0.50, 0.25) (0.05, 0.20, 0.75) TABLE II CONDITIONAL PROBABILITIES Z7. THE THREE NUMBERS IN THE TABLE REPRESENT THE PROBABILITY OF BOB, WEAVE, AND STELA (P(BOB), P(WEAVE), P(STELA)) High details? Mid details? Low details? High motions? (0.10, 0.10, 0.80) (0.20, 0.15, 0.65) (0.25, 0.15, 0.60) Mid motions? (0.10, 0.15, 0.75) (0.20, 0.20, 0.60) (0.40, 0.25, 0.35) Low motions? (0.15, 0.25, 0.60) (0.25, 0.40, 0.35) (0.45, 0.45, 0.10) TABLE III CONDITIONAL PROBABILITIES Z8. THE THREE NUMBERS IN THE TABLE REPRESENT THE WEIGHT OF BOB, WEAVE, AND STELA (W(BOB), W(WEAVE), W(STELA)) Inference=Bob Inference=Weave Inference=STELA P(Bob) (0.90, 0.05, 0.05) (0.45, 0.45, 0.10) (0.45, 0.10, 0.45) P(Weave) (0.45, 0.45, 0.10) (0.05, 0.90, 0.05) (0.10, 0.45, 0.45) P(STELA) (0.45, 0.10, 0.45) (0.05, 0.45, 0.45) (0.05, 0.05, 0.90)

and the non-root nodes (child nodes with parent nodes: Z5, Z6 Z7, and Z8) have local conditional probability distributions that quantify the parent-child probabilistic relationships. We assume that, in a situation when no other evidence is available, the probability of an event occurring is a prior probability. Here, the prior probability of “SD is High?” may be expressed as P(SD is High?)=0.5, meaning that, without the presence of any other evidence, one can assume that SD has a 50% chance of being bigger than the average value of SD in a field. Each row in a conditional probability table must sum to one. A probability term is also used to express random variables with two values, High (H) and Low (L), in the domain. This method of building a BN structure relies solely on domain expert knowledge. The probability table for z1 is made according to α, which represents the probability that SD is High. In the same manner, the probability table for z2, z3, and z4 is made according to β, γ, and δ, which represent the probabilities that SMDW is High, TD is High, and TMDW is High, respectively. Variables α, β, γ, and δ are determined as defined in (10-13), and both of them have values between 0 and 1. α = ( log 2 ( SD ) ) 8

β = ( log 2 ( SMDW ) ) 8

γ = ( log 2 (TD ) ) 8

δ = ( log 2 (TMDW ) ) 8

(10) (11) (12) (13)

The distribution of classified results according to SD, SMDW, TD, and TMDW, is exhibited in Fig. 4. Symbol H means that the value is larger than 24, and symbol L means that the value is smaller than 24. From Fig. 4, we observe that the SD, SMDW, TD, and TMDW values of most pixels are smaller than 24. Moreover, the low TD or low TMDW pixels are classified within the static area, and the remaining pixels are

(a) (b) (c) Fig. 5. Probabilities in the Bayesian network: (a) in case of (SD, SMDW, TD, TMDW)=(4, 8, 32, 32); (b) (38, 43, 2, 1); (c) (255, 255, 0, 0).

classified within the motion area. Moreover, the low SD or low SMDW pixels are classified within the smooth area; others are classified within the edge area. Based on the classification result, a different deinterlacing algorithm is activated in order to obtain the best performance. For quantitative modeling, we need the probability assessments P(A), P(B), P(C), P(D), P(E|A,B), P(F|C,D), P(G|E,F), and P(K|G,Inference). Let P(A)=P(B)=P(C)=P(D) =(0.5,0.5). The table for Z5 (or Z6) is given in Table I. Note that the table for P(E|A,B) (or P(F|C,D)) reflects the fact that the details (or motions) may be influential. We assume the conditional probabilities for Z6 are the same as for Z5. Each frame is passed through a region classifier, which classifies each missing pixel into three different categories. Tables II and III illustrate the conditional probabilities for Z7 and Z8, respectively, and these results will be used as weights for each candidate method. The best way to interpolate the missing pixel is to assign accurate weightings, according to Z7 and Z8. The network is extended with a single decision node Inference. The Inference node may have an impact on the structure and give Utility the information regarding three different deinterlacing methods: Bob, Weave, and STELA. Graphically, a utility function is represented as a diamondshaped node with incoming links from the nodes in its domain. The task is to determine the action that yields the highest expected utility. The weighting of each method from 0 to 1 will be decided by the Utility. The BN topology in Fig. 3 can express each entry of the joint probability table as in (14). P( z1 , z2 , z3 , z4 , z5 , z6 , z7 , z8 ) = P ( Z1 = z1 ) × P ( Z 2 = z2 ) × P( Z 5 = z5 | Z1 = z1 , Z 2 = z2 ) × P ( Z 3 = z3 ) × P ( Z 4 = z4 )× P( Z 6 = z6 | Z 3 = z3 , Z 4 = z4 )

(14)

× P ( Z 7 = z7 | Z 5 = z5 , Z 6 = z6 ) × P ( Z8 = z8 | Z 7 = z7 )

Our proposed interpolation algorithm is performed according to the inference rules. The BN model described above can be used to draw probabilistic inferences. We


consider the probabilities (or confidences) for the attribute nodes, based on prior knowledge. Note that probabilities are given in percentages rather than on the usual [0,1] scale. For example, if (SD, SMDW, TD, TMDW) is (4, 8, 32, 32), then (α, β, γ, δ) becomes (2/8, 3/8, 5/8, 5/8), and the weight of each method (wB, wW, wS) becomes (0.285042, 0.299064, 0.415894), as shown in Fig. 5(a). This means that one has 28.50, 29.91, and 41.59 (%) chances of using Bob, Weave, and STELA methods. However, if (SD, SMDW, TD, TMDW) is (38, 43, 2, 1), then (α, β, γ, δ) becomes (0.656, 0.679, 0.125, 0), and (wB, wW, wS) becomes (0.276054, 0.324088, 0.399858), as shown in Fig. 5(b). In the same manner, if (SD, SMDW, TD, TMDW) is (255, 255, 0, 0), then (α, β, γ, δ) becomes (1, 1, 0, 0), and (wB, wW, wS) becomes (0.249792, 0.311979, 0.438229), as shown in Fig. 5(c). D. Implementation We adopt BN concepts to design a weight-measuring technique. The weights are multiplied by the candidate deinterlaced pixels. The aim of our proposed algorithm is to measure the significance (measured by weight) of several deinterlacing methods and interpolate missing pixels by means of a weight multiplied interpolation method to better preserve image quality. The filter output can be illustrated with the operational image denoted by xBND(i,j,k), which represents fuzzy weight multiplied results, i.e., xBND ( i, j , k ) =

∑

m ={B ,W , S}

wm ( i, j , k ) ⋅ xm ( i, j , k )

∑

m ={B ,W , S }

wm ( i, j , k )

(15)

where xB, xW, and xS are candidate deinterlaced pixels. The weights (wm) in each method are calculated from the previous section. The attribute m represents the selected method which is the decision maker’s decision. III. SIMULATION RESULTS In this section, we compare the objective and subjective quality and computational CPU time for the different deinterlacing methods. The proposed methods were implemented on a Pentium IV processor (3.20 GHz). The algorithms are implemented in C++ and the BN software package Hugin [17] and were tested using our test sequences.

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Experiments were conducted to measure the performance of the proposed BND method. Experiments were performed with seven 352×288 CIF sequences (Akiyo, Flower, Foreman, Mobile, News, Stefan, and Table Tennis). The above sequences were chosen because they represent different classes of motions and edges, and as a result they give a more complete evaluation of the proposed algorithm. A. Performance Analysis After the deinterlacing process, the PSNR was chosen to provide an objective measure of the schemes’ performance. Table IV summarizes the average PSNR (dB) and computational CPU time (s) for each algorithm, and test sequence. The results show that the proposed BND method performed better than Bob or Weave methods in terms of PSNR, but does not show better PSNR results than the STELA method. Also, the proposed BND method requires 4.96 (or 5.19) times the computational CPU time of the Bob (or Weave) method while having a 1.87 (or 2.97) dB average PSNR gain. Additionally, the proposed BND method requires 1.44 times the computational CPU time of the STELA method while having a 0.36 dB average PSNR loss. For a subjective performance evaluation, the 121st frame of the CIF Stefan sequence and the 31st frame of the CIF Table Tennis sequence were adopted. Subjective views of the video sequences are shown in Figs. 7 and 8. In the stationary region (e.g., characters on the advertising board or line on the pingpong table), the Weave method is superior to the Bob method. On the other hand, if the sequences have a large amount of motion or a large number of scene changes (e.g., player’s body or a ping-pong ball), the Bob method looks superior to the Weave method in objective and subjective performance. In general, if the pixel is on the motion or edge region, then the STELA method is more effective than the Bob or Weave methods for the deinterlacing process. The STELA method provides relatively good performance, eliminates the blurring effect of bilinear interpolation, and gives sharp, straight edges. However, due to misleading edge directions, interpolation errors often propagate in areas with high-frequency components. In Figs. 7 and 8, flickering artifacts were found to occur only where there is a motion and edge region (e.g. characters on an advertising board or table tennis paddle). In order to solve the above problems, we proposed the BND method. Although the processing requirement for the BND method is somewhat higher than that of conventional

TABLE IV AVERAGE PSNR AND COMPUTATIONAL CPU TIME OF EACH ALGORITHM OVER THE CORRESPONDING SEVEN CIF TEST SEQUENCES Sequence Akiyo Flower Foreman Mobile News Stefan Table Tennis Average Spatial domain methods Bob(dB) 39.858 22.191 30.172 25.511 33.615 27.724 28.566 29.662 (s) 8.159 7.650 8.963 8.504 8.650 7.598 9.248 8.396 Temporal domain method Weave(dB) 43.786 20.294 26.307 23.537 36.471 21.549 27.996 28.563 (s) 11.301 7.289 7.984 6.537 6.947 7.191 9.093 8.049 Spatial-Temporal domain methods STELA(dB) 44.655 22.990 30.449 27.260 39.284 26.996 31.588 31.889 (s) 27.301 27.358 28.919 29.004 28.057 27.179 34.065 28.840 Proposed method BND (dB) 44.204 22.960 29.896 27.123 38.573 26.769 31.178 31.529 (s) 40.514 38.325 42.865 39.672 39.328 35.938 48.483 41.807

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(g) Fig. 6. PSNR values of the 250 frames from (a) Akiyo; (b) Flower; (c) Foreman; (d) Mobile; (e) News; (f) Stefan; (g) Table Tennis video sequences.

methods, it has the advantage of higher output image quality. The better performance of the BND method can be explained by the fact that the BND method preserved as much detail as possible in the motion region, while performing the temporal domain information based deinterlacing operations for the stationary region. B. The Discussion In this paper, we have researched the applicability of BN for reliability analysis. Also, we have described a causal model for reasoning about the deinterlacing system, using a BN. In performance analysis, the PSNR results of the BND method in all the tested video sequences do not outperform the STELA methods. However, the subjective performance evaluation does show significant improvement. This may occur because the STELA method gradually reduces the vertical detail as the temporal frequencies increase. The vertical detail from the previous field is combined with the temporally shifted current field, indicating that some motion blur occurred. We have described a number of advantages of incorporating empirical relationships into a causal framework using a BN.

For instance, a BN allows expert judgment to be used to supplement the data available. As a matter of fact, perfect statistical validation of the joint probability distribution of all the relevant attributes can never be achieved. The causal framework gives flexibility to match the empirical data to the metrics used in each organization. Also, a BN provides much more flexibility in the way the data can be used to support decisions: predicting what can be achieved, what is needed to meet a target or how to trade-off the different outcomes in an under resourced project. C. Limitations and Future Work The limitation of this research is that it is based on limited parameters, i.e., SD, SMDW, TD, and TMDW. For generalizability, the BN model should be based on more attributes. The proposed BN method can be used with continuous data. However, we should optimize the system to prevent mathematical complexity. Also, the dataset used in this research is too small to give an overall probability distribution for a BN; a large dataset would give better results.


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(d) (e) Fig. 7. Subjective quality comparison of the 121st CIF Stefan sequence: (a) original; (b) detail of Stefan sequence after deinterlacing process of Bob method; (c) Weave method; (d) STELA method; (e) proposed BND method.

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(d) (e) Fig. 8. Subjective quality comparison of the 31st CIF Table Tennis sequence: (a) original; (b) detail of Table Tennis sequence after deinterlacing process of Bob method; (c) Weave method; (d) STELA method; (e) proposed BND method.

Future work should consider several issues. Complexity is a critical issue to be further researched in the future. Because the system complexity depends on the image, it is important to

devise strategies to monitor and control the network complexity. More advanced features should be studied in this context as a way to improve the systems performance. In the

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treatment of BNs, we have so far only considered outdated deinterlacing methods. More advanced deinterlacing methods should be included in future BND as a way to improve the system performance. IV. CONCLUSION A new approach to BN representation for a deinterlacing system is described. The objective of our research was to develop an approach for weight measuring support in a deinterlacing system. In this paper, we have considered the applicability of BN for reliability analysis. Also, we have shown how the weights are determined and how complex the proposed method is. Moreover, we have shown that there are a number of advantages to incorporating the empirical relationships into a causal framework using a BN. REFERENCES [1] [2]

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Gwanggil Jeon received B.S. and M.S. degrees in electronic engineering from Hanyang University, Korea, in 2003 and 2005, respectively. He is currently a candidate for a Ph.D. degree in electronic engineering at Hanyang University. His research interests include fuzzy sets theory, rough sets theory, soft computing, motion estimation, scalable video coding, image compression, video processing and image enhancement.

Rafael Falcon received both his B.S. and M.S. at the Central University of Las Villas (UCLV), Cuba in 2003 and 2006, respectively. He is a member of the International Rough Set Society and is now actively pursuing his PhD degree. His research interests fall under the umbrella of Computational Intelligence, particularly fuzzy and rough set theories, swarm intelligence, knowledge-based clustering as well as video and image processing.

Donghyung Kim received B.S. and M.S. degrees in electronic engineering from Chungbuk National University, Korea, in 1999 and 2001, and a Ph.D. degree in electrical and computer engineering from Hanyang University, Korea, in 2007. Since 2007, he has worked at Electronics and Telecommunications Research Institute (ETRI). His research interests include video compression, coding standard, video processing, and image enhancement.

Rokkyu Lee received B.S degree in electronic engineering from Hanyang University, Korea, in 2007. He is currently working toward the M.S. degree in electronic engineering at Hanyang University. His research interests include rate control, rough sets theory, soft computing, motion estimation, scalable video coding, image compression, video processing, and image enhancement.

Jechang Jeong received a B.S. degree in electronic engineering from Seoul National University, Korea, in 1980, an M.S. degree in electrical engineering from the Korea Advanced Institute of Science and Technology in 1982, and a Ph.D. degree in electrical engineering from the University of Michigan, Ann Arbor, in 1990. From 1982 to 1986, he was with the Korean Broadcasting System, where he helped develop teletext systems. From 1990 to 1991, he worked at the University of Michigan, Ann Arbor, as a Postdoctoral Research Associate, where he helped to develop various signal processing algorithms. From 1991 through 1995, he was with the Samsung Electronics Company, Korea, where he was involved in the development of HDTV, digital broadcasting receivers, and other multimedia systems. Since 1995, he has conducted research at Hanyang University, Seoul, Korea. His research interests include digital signal processing, digital communication, and image/audio compression for HDTV and multimedia applications. He has published over 30 technical papers. Dr. Jeong received the “Scientist of the Month” award in 1998, from the Ministry of Science and Technology of Korea. He was also honored with a government commendation in 1998, from the Ministry of Information and Communication of Korea.