International Conference on Electronics, Biomedical Engineering and its Applications (ICEBEA'2012) Jan. 7-8, 2012 Dubai
Novel Intra-field Deinterlacing Algorithm Using Trilateral Filtering Interpolation
Xiangdong Chen and Jechang Jeong
The conventional deinterlacing methods interpolate the Abstract—This paper proposes an efficient intra-field missing pixels in two ways: (1) intra-field interpolation, and deinterlacing algorithm using trilateral filtering interpolation method (2) inter-field interpolation. Since inter-field interpolation which has outstanding visual effect. The conventional edge based methods usually use the temporal motion information to deinterlacing algorithms provide unsatisfied image visual effect due estimate the motion statement of objects, and then interpolate to wrongly estimation of edge direction or only taking limited numbers of edge directions into consideration; moreover, In order to pixels along the motion directions. In order to implement make accurate edge estimation, the existing deinterlacing algorithms interpolation properly, accurate motion estimation is essential, try to exhaust all the possible edge directions with setting complex complex motion estimation algorithm is necessary to refine conditions which will unavoidably enhance the computational burden the motion information which need high computational cost, while still producing the pinniform or blur artifacts at edge and especially, when fast and irregular motion exists, it is hard to complex regions. To avoid these problems, the proposed algorithm estimate the motion information or gain wrong motion introduces trilateral filtering interpolation which utilizes the correlation of adjacent 6 pixels by measuring the spatial closeness, information, in this situation, performing deinterlacing with intensity similarity and local gradient among them. Experimental the wrong motion information, artifacts cannot be avoided results show that the proposed algorithm provides satisfied inter-filed interpolation methods also need to use intra-field performances in terms of both objective and subjective image interpolation methods to improve the image quality, so in this qualities. What is more, it just exploits the local spatial similarity paper we focus here on the intra-field interpolation method. among the neighboring pixels without complex preset-conditions Because intra-field deinterlaced methods have a lower which is easier to implement than most of the existing algorithms. computational burden than inter-field methods and it only
Keywords—Bilateral filter, Deinterlacing, Edge-preserving, utilizes current frames, these methods are more suitable for Trilateral filter. real-time applications. Many intra-field interpolation methods have been proposed I. INTRODUCTION including line average (LA) and directional spatial interpolations. Edge directional interpolation algorithms such HE international industrial standard for interlaced as ELA (Edge-based LA) [4], EELA (Efficient ELA) [5], scanning technology has been widely applied in various T M-ELA (Modified ELA) [6], these methods interpolate a existing TV broadcasting standards, such as NTSC, PAL, and missing line linearly along the direction between adjacent SECAM. In interlaced scan fields, which contain half samples pixels which has the highest correlation. However, those of original image, that means, only the even or the odd lines of directional interpolation techniques have a low performance a frame, are scanned and displayed sequentially. The goal of due to wrongly estimate the direction or only use limited interlaced scanning is to achieve a tradeoff between frame rate direction models in high spatial frequency regions or and transmission bandwidth requirements [1]. However, due horizontal edge. to the adoption of interlaced scanning, current display systems One of the well-known direction oriented methods is such as HDTV, LCD, and 3DTV, suffer from rebarbative edge-based line average (ELA) [4] algorithm. This method visual artifacts such as interline flicker, line crawling and field considers correlations among neighboring six pixels in upper aliasing. Progressive scanning is preferred because interlacing and low lines around the center pixel to be interpolated, the reduces the vertical display resolution and causes twitter ELA has advantage in that it exhibits high performance with a effects for displaying pictures with high vertical frequency small computational load. However, ELA algorithm has [2]-[3]. Thus, various methods have been presented to reduce artifacts when edge direction is incorrectly estimated. these artifacts in digital display devices. The process to Moreover, ELA suffers from the degradation of the image due convert interlaced fields into progressive frames is called to the limitation of considering candidate edge directions, only de-interlacing. three direction, that is vertical, diagonal and anti-diagonal directions. In order to alleviate the disadvantages of ELA, Xiangdong Chen is with the Department of Electronics & Computer many improved edge-based algorithms, such as efficient ELA Engineering, Hanyang University, Seoul,133-791,Korea (phone: (EELA) [5],modified ELA (MELA) [6], low-complexity +82-2-2220-4370; fax: +82-2-2293-8877; e-mail:[email protected]). Jechang Jeong is now with the Department of Electronics & Computer interpolation method for deinterlacing (LCID) [7], fine Engineering, Hanyang University, Seoul,133-791,Korea (e-mail: directional deinterlacing (FDD)[8] and FDIF deinterlacing [9] [email protected])
300 International Conference on Electronics, Biomedical Engineering and its Applications (ICEBEA'2012) Jan. 7-8, 2012 Dubai have been proposed. Among these methods, FDIF functions for the spatial and intensity components are defined deinterlacing has outstanding performance since it combinates respectively as |(x,y) (x0,y0)|2 adaptive distance weighting scheme with fixed directional Ws (x, y) = ex p (2) x0,y0 2 2 interpolation filter based on MELA. It utilizes a 6-tap fixed − s And coefficients sinc interpolation filter to realize high accurate �− σ � |I(x,y) I(x0,y0)|2 interpolation on the edge estimated by MELA, though FDIF WR (x, y) = ex p (3) x0,y0 2 2 has high PSNR performance, it still yields jagged artifacts on − R Where I(., .) is the intensity value at the given position. small angle edge because of limited edge directions taken into �− σ � Then, the ensemble weight in the bilateralfilter is the product consideration. Since these algorithms consider more candidate of (2) and (3): edge direction and more accurate edge judgment condition W (x, y) = Ws (x, y)WR (x, y) (4) than ELA, they have better objective or subjective x0,y0 x0,y0 x0,y0 performance than ELA, while they still yields a pinniform-like In practice, each pixel is filtered using normalized weights noise in the complex or texture region and flicker on small as angle edges. To reduce this issue, we apply a trilateral filtering ( 0, 0) ( , ) ( , ) interpolator to interpolate the missing pixel by taking ( , ) ( 0, 0) 0, 0 𝐼𝐼=̃ 𝑥𝑥 𝑦𝑦 (5) closeness among the neighboring pixels and intensity ( , ) ∑ 𝑥𝑥 𝑦𝑦 (𝜖𝜖𝑁𝑁, )𝑥𝑥 𝑦𝑦( 0,𝑊𝑊0)𝑥𝑥 𝑦𝑦 0,𝑥𝑥0𝑦𝑦 𝐼𝐼 𝑥𝑥 𝑦𝑦 similarity among them into consideration, and also consider Where ( 0, 0) is the filtered image at location( 0, 0). ∑ 𝑥𝑥 𝑦𝑦 𝜖𝜖𝑁𝑁 𝑥𝑥 𝑦𝑦 𝑊𝑊𝑥𝑥 𝑦𝑦 𝑥𝑥 𝑦𝑦 the local pixel gradient correlation. Since the local pixel The parameters and are used to adjustthe influence gradient implies the edge information, we do not need to ̃ ofWS and𝐼𝐼 𝑥𝑥WR,𝑦𝑦 respectively. They can be treated as𝑥𝑥 rough𝑦𝑦 𝑅𝑅 𝑆𝑆 estimate edge directions. The problem we discussed thresholds for identifying𝜎𝜎 𝜎𝜎 pixelssufficiently close or similar to previously can be avoided. The proposed algorithm also has the pixel being filtered.Therefore, compared tothe merits of low complexity and good visual quality. conventional Gaussian filter, the bilateral filter caneffectively The remainder of the paper is organized as follows. The separate the textual and structural information ofthe image. conventional bilateral filter is briefly introduced in section 2. However, even though the bilateral filter is widelyused, no Also, the trilateral filtering method will be introduced in this theoretic manner has been established to determinethe optimal section. The proposed deinterlacing algorithm based on and . Therefore, these parameters aregenerally selected by trilateral filtering interpolation will be explained in Section 3, the empirical method. 𝑅𝑅 𝑆𝑆 and the experimental results are presented to evaluate the 𝜎𝜎 Bilateral𝜎𝜎 filteringtakes all the neighboring pixels into performance of the proposed method in Section 4. Finally, consideration which have better performance in image conclusions are presented in Section. 5. denoising application, because it make full use of the spatial closeness and intensity similarity of the neighboring pixels, II. CONVENTIONALBILATERAL FILTERAND TRILATERAL however, one of the main limitations of bilateral filtering is FILTER that the range filter coefficients rely heavily on actualpixel A. The Bilateral Filter intensity values, as it does not take into account any regional A bilateral filter is a nonlinear filter that depends on characteristics, which may in turn have beeninfluenced by underlying image data and smoothes images while preserving noise therefore potentially resulting in smoothed texture edges [10]. Bilateral filtering can be regarded as an extended regions and fuzzy boundary when denoising which is proved version of the Gaussian low-pass filtering (smoothing) but in [11]. Motivated by this, we present a novel framework for preserves edges by decreasing the weights of pixels where the interpolation based on the weighted averaging of image pixels difference in intensity is large. The filter has a spatial and a byfurther exploiting regional characteristics in order to radiometric part and the filter weights are determined by both overcome the above limitations. geometric closeness and intensity similarity to neighboring B. The TrilateralFilter pixels. Based on the traditional bilateral filter, a trilateral filter is The idea of the bilateral filteris to combine gray levels proposed by taking the local pixel gradient into consideration based on both the geometric closeness and intensity similarity to exploit the local regional characteristics in [12]. As is that is in favor of near values to distant values in both domain well-known, the edge information is the most important local and range. Twoweighting functions regarding spatial and feature information and local pixel gradient information can intensity are designed to replace a pixel value with anaverage reflect the edge distribution mathematically. So the local pixel of similar and nearby pixel values in a (2N +1)×(2N +1) gradient is introduced as a new weighting factor to measure neighborhood. In theory, any shape ofweighting functions can the filtering coefficients besides closeness and intensity be used but it is usually a Gaussian function in terms of the similarity of local neighboring pixels. In this paper, we use Euclidean distancebetween the arguments. More specifically, convolution of image with two Sobel operators to measure the let (x0, y0) be the location of the pixel under considerationand pixel gradient. It is defined by ( 0, 0) = {( , ):( , ) [ 0 , 0 + ] × [ 0 , 0 + ]} ( ) = 2( ) + 2( )(6) 𝑥𝑥 𝑦𝑦 (1) 𝑁𝑁be the pixels 𝑥𝑥in𝑦𝑦 the neighborhood𝑥𝑥 𝑦𝑦 ∈ 𝑥𝑥 − 𝑁𝑁of (𝑥𝑥x0, y0𝑁𝑁). The𝑦𝑦 we−ighting 𝑁𝑁 𝑦𝑦 𝑁𝑁 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝐼𝐼 �𝐺𝐺𝑥𝑥 𝐼𝐼 𝐺𝐺𝑦𝑦 𝐼𝐼
301 International Conference on Electronics, Biomedical Engineering and its Applications (ICEBEA'2012) Jan. 7-8, 2012 Dubai
Where ( )and ( ) are obtained by using the Sobel in(x0,y0). operator as 𝑥𝑥follows: 𝑦𝑦 A. Pre-interpolation of the Unknown Pixels 𝐺𝐺 𝐼𝐼 𝐺𝐺 𝐼𝐼 1 0 1 ( ) = 2 0 2 (7) This is a crucial step in the whole interpolation procedure, the − 1 0 1 more accurate estimation of I(x0, y0), the higher performance 𝑥𝑥 the interpolation filter will be. Here an efficient anti-aliasing 𝐺𝐺 𝐼𝐼 �1− 2 1� ∗ 𝐼𝐼 interpolation filter is used to interpolate the unknown pixel of ( ) = 0− 0 0 (8) I(x0, y0). The coefficients of the filter are shown in equation −1 −2 − 1 𝐺𝐺𝑦𝑦 𝐼𝐼 � � ∗ 𝐼𝐼 (11): Where the operator ‘*’denotes the 2-D convolution. = {1, 5,20,20, 5,1}/32 (11) Knowing the local pixel gradients, a new Gaussian filter This is a 6-Tap fixed coefficient wiener filter which is usually ℎ kernel is formed as used to −estimate −the sub-pixels in video codec, such as 0, 0( , ) MPEG-4, H.264/AVC. This filter can interpolate pixels in the 𝐺𝐺 | ( , ) ( 0, 0)|2 sub-pixel position accurately. A good example of the usage of =𝑊𝑊𝑥𝑥 𝑦𝑦 𝑥𝑥 𝑦𝑦 (9) 2 2 this kind of filter is FDIF[9] which has high performance in 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑥𝑥 𝑦𝑦 − 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑥𝑥 𝑦𝑦 deinterlacing. The pixels used to interpolate the missing pixel 𝑒𝑒𝑒𝑒 𝑝𝑝 �− � Where is a gradient scale𝜎𝜎𝐺𝐺 parameter to adjust the filter is shown in Figure.3.The pixel to be estimated in (x0,y0) is to performance. By taking (9) into (4), we can get a new filter be interpolated using the pixels in position A,B,C,D,E,F. The 𝐺𝐺 weighting𝜎𝜎 function as other pixels in position(x0, y0-1) and (x0,y0+1) are 0, 0( , ) interpolated by line average. = ( , ) ( , ) ( , ) (10) 𝑥𝑥 𝑦𝑦0, 0 0, 0 0, 0 The interpolation equation is defined as follows: 𝑊𝑊 𝑠𝑠 𝑥𝑥 𝑦𝑦 𝑅𝑅 𝐺𝐺 20( + ) 5( + ) + ( + ) Using𝑥𝑥 the𝑦𝑦 new filter𝑥𝑥 𝑦𝑦 weighting𝑥𝑥 𝑦𝑦function in (5), we can get , ( , ) = ( 0, 0) 𝑊𝑊 𝑥𝑥 𝑦𝑦 𝑊𝑊 𝑥𝑥 𝑦𝑦 𝑊𝑊 𝑥𝑥 𝑦𝑦 ( , ) = 32 (12) 𝐶𝐶 + 𝐷𝐷 − 𝐵𝐵 𝐸𝐸 𝐴𝐴 𝐹𝐹 the trilateral filter. 0 1 0+1 , ( 𝑥𝑥, 𝑦𝑦) ( 𝑥𝑥0, 𝑦𝑦0) 𝐼𝐼̃ 𝑥𝑥 𝑦𝑦 � 2 𝐼𝐼𝑥𝑥 − 𝐼𝐼𝑥𝑥 III. DEINTERLACING BASED ON TRILATERAL FILTERING 𝑥𝑥 𝑦𝑦 ≠ 𝑥𝑥 𝑦𝑦 INTERPOLATION The proposed deinterlacing method is implemented in a local 3×3 sliding window seen in figure 1. In Figure 1 where (x0, y0) is the location of the unknown pixel to estimate, the 8 neighboring pixels are used to calculate the filter coefficients. The trilateral filtering interpolation process has three steps. In the first step, we focus on the pre-estimation of the unknown pixel of I(x0, y0), the second step is calculating the filter coefficients of WS,WR and WG, where WR reflects the edge Figure.3 Pixels used in pre-estimationmethod degree information, and WSreflects spatial distance B. Calculating the Filter Coefficients information, the third partial weight of WG is to reflect photometricsimilarities between the primarily estimated After pre-interpolate the unknown pixels using the method in unknown pixeland its six neighbors. The last step is to the first step, we use the formula (2), (3) and (9) to calculate implementingthe procedure of intra-frame deinterlacing via the filter coefficients. Because the distance between the the trilateral filtering interpolation method.We have to neighboring pixels and the center position(x0,y0) are fixed, so mention that only the six neighboring pixels are used to the spatial weight WS can be pre-calculated to reduce the estimate the unknown pixel in position (x0, y0) which can be complexity. As to the intensity and gradient weight WR and seen in Figure 2. WG, we need to make modification according to the position of the neighboring pixels. As is well known, the nearest neighboring pixels have highest correlation. It is also the reason why LA has high objective performance. So here we introduce a new constraint factor k to enhance the weights of the pixels in the nearest position. By adding the constraint × Figure.1 The local 3 3 sliding window. factor k into the calculation of WR and WG, we get a new definition of WR and WG in (13) and (14).
Figure.2 The six neighboring pixels to interpolate the unknown pixel
302 International Conference on Electronics, Biomedical Engineering and its Applications (ICEBEA'2012) Jan. 7-8, 2012 Dubai
TABLE I 0, 0( , ) = COMPARISON OF IMAGE OBJECTIVE QUALITY MEASURED BY AVERAGE PSNR 𝑅𝑅 | ( , ) ( 0, 0)|2 𝑥𝑥 𝑦𝑦 (13) Method Airplane Boat Lena Peppers Toys Average 𝑊𝑊 𝑥𝑥 𝑦𝑦 2 2 𝑘𝑘 𝐼𝐼 𝑥𝑥 𝑦𝑦 −𝐼𝐼 𝑥𝑥 𝑦𝑦 LA dB 34.21 35.40 37.67 33.80 33.30 34.88 𝑒𝑒𝑒𝑒and𝑝𝑝 �− 𝜎𝜎𝑅𝑅 � s 0.01 0.01 0.01 0.01 0.01 0.01 ELA dB 32.96 32.38 35.82 34.26 32.47 33.58 0, 0( , ) 𝐺𝐺 | ( , ) ( 0, 0)|2 s 0.29 0.22 0.23 0.22 0.21 0.24 =𝑊𝑊𝑥𝑥 𝑦𝑦 𝑥𝑥 𝑦𝑦 (14) 2 2 EELA dB 33.30 33.60 36.93 34.29 33.03 34.23 𝑘𝑘 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑥𝑥 𝑦𝑦 − 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔 𝑥𝑥 𝑦𝑦 s 0.25 0.26 0.25 0.25 0.25 0.25 Where𝑒𝑒𝑒𝑒 𝑝𝑝the�− k is defined as 𝐺𝐺 � 𝜎𝜎 MELA dB 34.12 35.29 37.89 34.21 33.34 34.97 0.5, ( , ) ( ( 0, 0)) = (15) s 0.26 0.26 0.26 0.26 0.26 0.26 1, 𝑥𝑥 𝑦𝑦 ∈ 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑁𝑁 𝑥𝑥 𝑦𝑦 FDD dB 34.12 35.63 37.98 33.70 32.22 34.73 Us𝑘𝑘 ing� (13), (14) in (10), we get a new weighted filter coefficients. 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 s 1.66 1.93 1.97 1.84 1.69 1.82 The last step is utilizing the trilateral filtering interpolation LCID dB 34.63 34.87 37.79 33.95 32.96 34.84 method in (5) to interpolate the unknown pixels I(x0, y0) with s 0.26 0.22 0.23 0.22 0.21 0.23 the weighted filter coefficients in the second step. As to the FDIF dB 34.70 36.17 38.19 33.87 33.71 35.33 parameters , and , we use a wide range of images to implement the proposed deinterlacing methods with the goal s 0.65 0.41 0.42 0.45 0.42 0.47 𝑅𝑅 𝑆𝑆 𝐺𝐺 to test the value𝜎𝜎 𝜎𝜎 of these𝜎𝜎 constrain parameters. The value is set Proposed dB 35.22 36.20 38.35 34.62 33.84 35.65 as 28, 0.53 and 135 respectively when best PSNR performance s 0.87 0.88 0.86 0.86 0.86 0.87 is gained.
method is better than other methods. IV. EXPERIMENTAL RESULTS
In this section, a comparison is made of objective and subjective qualities, and CPU time for the different deinterlacing methodsincluding the proposed method. To perform experiment, we first separated odd and even fields from an image, and then we performed de-interlacing. And then, we compare the proposed algorithm with the LA, ELA, EELA, MELA, FDD, Low-complexity Interpolation Method for Deinterlacing (LCID),and FDIF methods subjectively and objectively. Nine standard test images were used: Airplane,Boat, Lena, peppers and Toys in 512x512 sizes (PNG and BMP). To evaluate objective picture quality, we calculate average PSNR of these images and average CPU time. Table 1 shows a comparison of image objective quality (A) Original image measured by average PSNR. The proposed algorithm achieves 0.3~2.1dB better than other methods even though it can vary image by image. As to CPU time, which reflects the efficiency of the deinterlacing methods, is also compared here. From table 1,we can see LA has shortest CPU time and FDD has longest CPU time, though the proposed method has longer CPU time than LA,ELA,EELA,MELA,LCID and FDIF, (B) Zoomed local image(C) LA (D) ELA however, it has best visual effect. Figure 4 demonstrates the subjective deinterlacing performance using airplane image.By comparation the proposed method with the conventional methods mentioned in this section, we can clearly see the proposed method has best visual effect, and the edge we get is smoother and sharper than most of the other methods.The proposed method has the smoothest and sharpest edge, and it doesn’t causes artifacts in (E) EELA(F)MELA (G) FDD weak edge region which can be seen from (J) in Figure 4.When comparing the marked parts in (B), the proposed
303 International Conference on Electronics, Biomedical Engineering and its Applications (ICEBEA'2012) Jan. 7-8, 2012 Dubai
Xiangdong Chen Received the B.S. and M.S. degrees from the College of Informatiion and Engineering, Northwest A&F University, Yangling, China, in 2006 and 2010, respectivvely. He is currently studying towards the Ph.D. degree inn the department of Electronics and Computer Engineering, Hanyang Universittyy, Seoul, Korea. His research interests fall under the umbrella of image processing, such as image denoising, and image enhancement as well as high-resolution image reconstrucction, and video coding standards, such as H.264/AVC, video (H) LCID (I) FDIF (J) proposed method sequences deinterlacing. Figure. 4 Comparation of the subjective performance of different Mr. Chen is a recipient of Chinese government scholarship to pursue his Ph.D deinterlacing methods with the proposed method. in Hanyang University, Seoul, Korea.
V. CONCLUSION In this paper, we have proposed an efficient intra-field deinterlacing algorithm using trilateral filtering interpolation method, which consider not only spatial closeness of local neighboring pixels, but also consider the intensity similarity and gradients of them, so the proposed method can reflect the real local edge and intensity information, so when using trilateral filtering interpolation method, the deinterlaced image has sharp and smooth edge. The experimental results show that the trilateral filtering interpolation methodo performs better than most of the existing intra-field deinterlacing methods. Since it can more reliably estimate the edge information, the proposed method can significantly improve both the subjective and objective performances of reconstructed images.
ACKNOWLEDGMENT This research was supported by the Brain Korea 21 Project in 2011.
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