PERFORMANCE AND ANALYSIS OF RINGING ARTIFACTS REDUCTION USING DEBLOCKING FILTER

PERFORMANCE AND ANALYSIS OF RINGING ARTIFACTS REDUCTION USING DEBLOCKING FILTER 1 M.Anto bennet 2 Dr.I.Jacob Raglend 3 Dr.C.Nagarajan 4 P.Prakash 1 Department of Electronics and Communication Engineering, Nandha Engineering College, Erode- 638052,India. 2 Department of Electrical and Electronics Engineering, Noorul Islam University, TamilNadu- 629025,India. 3 Department of Electrical and Electronics Engineering, Muthayammal Engineering College, Rasipuram,India. 4 Final UG Student, Department of Electronics and Communication Engineering Vel Tech Engineering College, Chennai, India. ABSTRACT Data compression is commonly used in images in order to reduce the storageand transmission costs. However, compression introduces spurious oscillations known as ringing artifacts near major edges. Many algorithms were proposed to solve this problem but fail to preserve image details while removing ringing artifacts we propose a novel adaptive algorithm to reduce ringing artifact in compressed images. In order to preserve image details, the proposed algorithm first detects the position of ringing artifact before filtering. The blocks with ringing artifact is detected according to each pixel in the block and its neighboring regions. Simulation results show that our proposed method can outperform the related algorithms in subjectively and objectively. By comparing JPEG compressed image, the decompressed image using our algorithm can achieve the better PSNR as well as the visual performance, especially at the lower quality coding (the higher compression rate). Keywords: Ringing Artifacts, Deblocking filter, Range filter,(psnr)peak Signal To Noise Ratio and Edge Detection. I. INTRODUCTION When performing block-based coding for quantization, as in JPEG-compressed images, several types of artifacts can appear. Ringing Contouring Posterizing Staircase noise along curving edges Blockiness in "busy" regions (sometimes called quilting or checker boarding) Blocking artifacts, which are mainly due to the coarse quantization of low-frequency DCT coefficients yielding decompressed image look like a mosaic at smooth regions, and Ringing artifacts, which are mainly due to the coarse quantization of high-frequency DCT coefficients making the decompressed image exhibit noisy patterns known as ringing or mosquito noise near the edges. 611

scheme for JPEG decompressed document images is useful to improve visual quality of the image. II. RELATED WORK (a) (b) Fig1 (a).original uncompressed images, (b) JPEG compressed with Q = 10. Ringing artifacts are visible Although the primary targets of JPEG compression are natural images, several other types of images such as document images can also be encountered in digital environments. Typical document images contain both textual and pictorial regions. Artifacts can also be observed on these regions. However, unlike natural images, which generally exhibit blocking artifacts at high compression ratios, in document images significant ringing artifacts are observed near the edges of textual components. Fig.1a: shows a magnified portion of an original document image including the word document and its JPEG decompressed form obtained by JPEG compressing the image with Q = 10 shown in fig1 b:ringing artifacts can be seen easily on the background region near the edges and visual appearance of the image is degraded. Depending on the application these artifacts can become intolerable and make the document even illegible. A trivial remedy to this problem can be using higher quality factors during the compression; however, high compression ratios can still be desirable for different applications. For instance, storage requirements for a book scanning project can be significantly reduced by utilization of high compression ratio. Similarly, the productivity of an image processing and printing system can be significantly increased by using higher compression ratios to better utilize the available network bandwidth. Thus, an artifact reduction The block based discrete cosine transform (DCT) [1] is the most important basic component of many image and video compression standards like JPEG and MPEG [2],because its performance is close to that of Karhunen- Loeve transform (KL T) which is optimal in the mean square error sense [3]. A major problem with the block DCT schemes is that the decoded images exhibit annoying blocking artifacts at high compression ratios. Many approaches have been proposed to eliminate the blocking artifacts. Reeve and Lim [4] reduced the blocking artifacts using a 3x3 Gaussian filter at the block boundaries. The main problem with this simple method is that the true edges at the block boundaries may also get blurred. Minami and Zakhor [5] proposed to reduce the blocking artifacts by minimizing a criterion called mean squared difference of slope (MSDS). The projection onto convex sets (POCS) algorithms [6,7,8,] have shown good results but the main problem with the POCS method is its iterative nature and the high computational complexity. A. RINGING ARTIFACTS In order to accurately detect and efficiently reduce ringing artifacts First, considering DCT-based compression, at the encoder side, pixels in spatial domain are transformed into the frequency domain, and then go through quantization. At the decoder side, these coefficients in frequency domain go through inverse quantization and inverse DCT to reconstruct pixels. Because human visual system is less sensitive at high frequency information, highfrequency coefficients will be quantized more heavily than low-frequency coefficients. However, 612

the block containing sharp edges correspond to more high-frequency components than normal one. These blocks will undergo much heavier losses after quantization step. At decoder side, pixels in spatial domain can be regard as sum of all frequency components. The sum of low frequency components will assume a wave look (i.e. a series of rings). These rings should be compensated with high frequency components. In the case of blocks containing sharp edges, high frequency components are over eliminated, so the rings will not get enough compensation and will appear perceptibly. III. PROPOSED SYSTEM A. RANGE FILTER Range filter considers photometric similarity between the center pixel and the neighboring pixels instead of geometric closeness. The filter is intuitively good on the images with obvious global edges. Global edges indicate the boundary lines between two or more large color region but not short lines. Because ringing artifacts usually appear near global edges, the filter is suitable for image deranging A low pass filter applied to an M * N image f (x) (1 = i = M, 1 = j =N ) produces an output image, where k(x)is the normalization factor for preserving the DC component after filtering, and Decomposed Image Edge Detection Pixel Classification Check Neighbors Calculate Variance Diff<R Range Filter Output Fig 2.Proposed Block Diagram 613

s(x) represents the photometric similarity between the pixel at the neighborhood center x and a nearby point. The simplest but robust similarity function s(x) usually adopts shift-invariant Gaussian function of the Euclidean distance. The photometric spread parameter sin the image range is set to achieve the desired amount of combination of pixel values. The pixels with photometric values of distance shorter than s are mixed together, but not for the pixels with distance longer than s Smooth if G(x, y) < Tw According to the characteristic of the image, adaptively set Ts and Tw by the following criteria: 300 if MaxG> 1000 Tw=100ifMaxG<1000 Ts=1/3Tw Where MaxG is the maximum pixel value in Gradient Image G Gmax=Max (G). If Gmax>1000, set threshold as T=300; where the constant value σr of range filter is small, the range filter can preserve edges. B. PIXEL CLASSIFICATION It is a technique which is used to check whether a pixel belongs to edge pixel or not an edge pixel. Since ringing artifacts occur along edges, we first detect edges in the decompressed image. The edge is calculated by Sobel operators. Sobel operators use two kernels Hh and Hv(horizontal)and vertical gradients Gh and Gv are obtained by linear convolution with Sobel kernels Gh(x,y)=Hh*I(x,y) GV(x,y)=HV*I(x,y) Where * is the linear convolution operator. The magnitude of the gradient is calculated using Ghcalculated using Gvas below: G(x,y)= Gh(x,y) + GV(x,y) Based on G(x, y), we classified each pixel as follows: Edge if G(x, y) >Ts Pixel(x, y) = Detail if Tw= G(x, y) = Ts If Gmax<1000, set threshold as If the pixel in G is classified as an edge pixel according to the following conditions. Pixel=edge pixel if pixel in G > T. Pixel=Not an edge pixel if pixel in G <T. The edge pixel map = {1,sobel( x y)< GT 0, edge x y)> GT T=100; These detected edge pixels are strong and clear. No matter what they are (real object edges or ringing artifacts), the block which contains edge pixels is regarded as a strong ringing block. ring artifact = 2,{ Edge( x y),( x y) Block}, 0, otherwise, Where ring_artifact=2 denotes a strong ringing artifact is detected in the current block, and ring_artifact =0 denotes no ringing artifact exists and no further process is needed. C. EDGE DETECTION Edge detection is used to determine the sharp luminance edges from the reference image. These sharp luminance edges are either due to the blockiness artifact introduced in coding process or due to the textual details present in reference 614

image. This spatial activity of both, reference and coded images, are determined by using sobel edge detectors. The edge detection is performed horizontally and then vertically on both images shown in fig 3.a: As mentioned, the DCT-based image coding will produce the ringing artifacts which always occur around the edge of an object though there is only one edge pixel value shown in fig 3.b: (a) (b) Fig 3.Example of one edge pixel of the 8 8 block (a) Uncompressed (b) DCT-based compressed Therefore, the edge detection of an image is a sufficient procedure to predict the region of the ringing artifacts. Based on the Sobel operator with two 3 3 kernels, the gradient value of a pixel with vertical and horizontal given by the derivatives in vertical (Gv) and horizontal (Gh) directions. Suppose I is an image, the derivative approximations are as follows. The gradient of the image is calculated for each pixel position in the image. D.NEIGHBOURING REGIONS OF THE POSSIBLE EDGE BLOCK Since ringing artifacts are usually perceptible around the edges and there exist nonedge regions such as smooth/detail, we need further check the possible block to avoid determining a non-edge block (textures) as an edge block. Therefore, we consider four 4 4 sub blocks from the left and right sides of the possible edge block to check the possible edge block as shown in Fig.4 After checking, the possible edge block will be determined as the true edge block Be if at least one of four neighbors belongs to the non-edge (i.e., all 615

gradient value of 4 4 are less than Te), and it will be filtered by the range filter to obtain Be_ RF. F. RINGING ARTIFACTS DETECTION Fig 4.The possible edge block and four nearby 4 4 sub blocks. E. CONTENT-AWARE DETERMINATION Finally, some blocks still being uncertain blocks Bp followed the above procedure. We employ the difference of variance between the edge block and filtered edge block to verify these uncertain blocks. As shown in Fig. 4, the range filter can preserve the edge. The variance of the edge block and filter edge block may be similarity. The possible edge block Bp can be determined as the true edge block if it satisfies the following condition. Let as consider the block which is identified as edge block =BP; Block after filtering by range filter=bp_rf; Find the variance of both blocks. Fig 5 :Detection criteria Before detecting ringing artifact, the block classification is performed using the result of pixel classification to determine an edge block. If a 8 8 block with edge pixel (i.e., pixel(x, y )Edge), it is classified as an edge block Since ringing artifacts occur around the sharp edges, the proposed method skips all smooth and texture blocks and focus on edge blocks only. Then neighboring region information is also utilized to preserve image details shown in fig 5. Each edge block is surrounded by 124 4 small regions. Since ringing artifacts are usually more visible near smooth regions, we classified the block as detected block if any one of the surrounding 124 4 regions is smooth (i.e., all pixels in the 4 4 regions Smooth).The flow chart of the proposed algorithm is shown in Fig 2.Each pixel in the detected block is filtered by range filter in a 5 5 window. IV. SIMULATION RESULTS Several gray-scale test images are used in our simulation. These images are compressed by JPEG with different QP. The compressed images show different degree of ringing artifact different 616

characteristic. The parameter σr of range filter used in our simulations is 30 if Gmax>950; σr = 10 or otherwise. A coded picture in this standard refers to a field (interlaced video) or a frame (progressive or interlaced video).each coded frame has a unique frame number, which is signaled in the bit stream is shown in fig6.each previously coded picture can be used as the reference picture for future decoded pictures Improves visual and objective quality of decoded picture is shown in fig 7. Significantly superior to post filtering it mainly removes blocking artifacts and does not necessarily blur the visual content is shown in fig 8. For future enhancements, different types of images will be taken in to account for verifying deblocking effects as well as the PSNR ratio will be calculated for the compression as well as for deblocking. Performance evaluation of PSNR improvement shown in table 1: PSNR is usually expressed in terms of the logarithmic decibel scale. The signal in this case is the original data, and the noise is the error introduced by compression. When comparing compression codec s it is used as an approximation to human perception of reconstruction quality, therefore in some cases one reconstruction may appear to be closer to the original than another, even though it has a lower PSNR (a higher PSNR would normally indicate that the reconstruction is of higher quality). Fig 6: Input image of the cameraman deblocking filter Fig 7: Before Deblocking filter applied to the cameraman MSE=1/mn ΣΣ [I(i,j)-K(i,j)] 2 The PSNR is defined as: PSNR=10. log 10 (MAX 2 I/MSE) 20. log10 (MAX I / MSE) 20. log10 (MAX I )-10 log 10 (MSE) Fig.8 :After Deblocking filter applied to the cameraman 617

Image Name Image Type Achieved PSNR-Proposed Previou s method Camera bmp 50 42 man Lena bmp 47.8 38.2 Baboon jpg 53.6 37 Barbara jpg 43.2 39.2 Table.1: Performance Evaluation of cameraman compared to lena, baboon and Barbara. V. CONCLUSION A new effective method for ringing artifact removal in com-pressed images is proposed. Blocks with ringing artifacts are accurately detected by our ringing artifact detection scheme. Range filter is applied to the detected block to remove ringing artifact. Simulation results demonstrate that the proposed algorithm effectively reduces ringing artifact and preserves image details as well. It also improves both visual quality and PSNR of compressed images compared to existing approaches. [5] S. Minami and A. Zakhor, "An optimization approach for removing blocking artifacts in transform coding," IEEE Transactions on Circuit and Systems for Video Technology.Vol.5, pp. 74-82, 1995. [6] Y. Yang, N. P. Galatsanos and A. K. Katsaggelos, "Regularized reconstruction to reduce blocking artifacts of block discrete cosine transform compressed images ", IEEE Transactions on Circuits and Systems for Video Technology. Vol.3, pp. 421-432,1993. [7] Y. Yang, N. P. Galatsanos, and A. K. Katsaggelos, "Projection based spatially adaptive reconstruction of block-transform compressed images," IEEE Trans. Image Processing, vol. 4, no.7, pp. 896-908, 1995. [8] Y. Yang and N. P. Galatsanos, "Removal of compression artifacts Using projections onto convex sets and line process modeling," IEEE Transactions on Image Processing, Vol. 6, pp.1345-1357,1997. REFERENCES [1] N. Ahmad, T. Natarajan, and K. Rao, "Discrete Cosine Transform," IEEE transactions on Computers, vol. C-23, no. I,pp. 90-93, 1974. [2] V. Bhaskaran and K. Konstantinides, Image and video compression standards - Algorithms and architectures, Kluwer Academic Publishers. 1997. [3] A. K. Jain, Fundamental of Digital Image Processing.Englewoods Cliffs, NJ: Prentice Hall, 1989. [4] H. Reeve and 1.Lim, "Reduction of blocking effects in image coding," Opt. Eng., vol. 23, no. I, pp. 34-37,1984. 618