Proceedings of International Congress of Imaging Science 2002 (ICIS ’02, Tokyo), pp. 678-679, May 13-17, 2002, Arcadia Ichigaya, Tokyo, Japan.
Selective Reduction Method of the Mosquito Noise in JPEG Decoded Image Tsutomu Shohdohji*, Yohsuke Sasaki#, and Yasushi Hoshino* * Nippon Institute of Technology, Saitama, Japan # ANDOR System Support Co., Ltd., Tokyo, Japan Abstract The JPEG (Joint Photographic Experts Group) compression algorithm is possible to decode an image effectively, by considering the visual characteristic of human beings. However, the mosquito noise generates in the block, when a compression rate is high. It is lost to not only the mosquito noise but also important image information which constitutes an image, when the usual noise reduction filter was applied for an image including the mosquito noise. As the result, image quality falls off. Then, our proposed method is possible to restrain degradation of image quality, while reducing noise, by changing weight of filtering every block. We confirmed that our proposed method was effective by the computer experiment.
Introduction JPEG2) is one of the most famous coding methods that are used widely to compress a digital image. The lossless coding method and lossy coding method are included to the JPEG compression system. Lossy coding method, one of the JPEG base line algorithms that used DCT (discrete cosine transform), is able to materialize high compressibility is used generally. As for the image by an impassable reverse method high compression is obtained dramatically. On the other hand, the deterioration and various noise of the image quality sometimes occur. There is block noise (false contour is included) and mosquito noise (it is able to consider as kind of block noise), in the main noise that occurs with JPEG coded method. There exists Shohdohji and others’ research5, 6) regarding the smoothing of false contour. The target of this research is squeezed to mosquito noise in these noises. We propose the new algorithm that reduces mosquito noise by using the ε-filter (see Fig. 1) 1).
The outline of algorithm Our proposed algorithm We carried out the computer experiment by using many filters. As a result, we understood that a kind of the ε-separated nonlinear digital filter is effective to remove mosquito noise while the outline is kept. However, the improvement of the image quality is not able to expect because filtering has been implemented to the whole image in the case that the ε-filter is applied alone. For example, we remove mosquito noise, in the case that a change of the luminance value of mosquito noise, is
bigger than a change of the luminance value that is likely to be included to the original image. In addition, it has been turned even to the useful information that is included to the original image and the image quality has been damaged. Therefore, the mechanism that does filtering only to the necessary range is important. First of all, we do the collection of the image information (it is the variance of a luminance value) that is included to the original image in the Phase I. We propose the new method that the filter is adapted by utilizing the information in the Phase II. Next, we express our proposed algorithm. 3, 4) Phase I (the collection of the image information that used the variance of the luminance value) Step 1 We request the variance of the luminance value to the N × N pixels centering around the attention pixel every one pixel to each pixel of the whole image, to detect the sharp outline that becomes the occurrence condition of mosquito noise. Step 2 We process the 8×8 pixels as one block to each pixel of the JPEG decoded image. Mosquito noise occurs every block to the surroundings of a sharp outline. We compare the variance of each pixel one by one that we requested with Step 1 in consideration of this case, and the biggest variance inside the block is made the representative of the variance for each block (see Fig. 2). Phase II (the implementation of the noise removal filter) The value that multiplied coefficient A to the dispersion value for each block that we requested in Phase I is set up as the ε value of the ε-filter1) for each block. The ε-filter is performed every block.
ε-filter that we applied We adopted the filter that made the mean value of all pixels to be the output pixel. It is included in the central luminance value of the filter window to the range of ± ε. The ε-filter is the filter that can remove the small noise below the ε value that set up it while kept a steep change (see Fig. 4). In ε = 0, the ε-filter does not have the function of the filter. In ε = ∞, the ε-filter has a nature to bring about the same action as a low pass filter.
Experimental results The result that experimented by using the above algorithm is shown to Figs. 6 and 7. Setting up N of Phase I with 7 in our proposed algorithm, we used 0.01 as
the coefficient of the ε-filter in Phase II. Where variance is calculated in 7×7 blocks, and Fig. 5 is the JPEG decoded image of an original image. Fig. 6 shows the JPEG image that we applied our proposed algorithm in Fig. 5. Fig. 7 shows the JPEG image that the ε-filter (ε = 30) is applied in Fig. 5. And Fig. 8 is the image which emphasizes the edge of Fig. 7. We understand that the image quality of trees is getting worse in the image by the ε-filter (see Fig. 7). The image quality by using our proposed algorithm obtained good evaluation in subjective evaluation.
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Conclusions In this paper, we proposed an efficient algorithm to the reduction of mosquito noise for JPEG decoded image by using the ε-filter. 5) We understood that our proposed algorithm has the nature to remove mosquito noise without damaging the image quality. By combining the different method that removes block noise and our method, the image quality of the JPEG decoded image is considered to be able to improve furthermore. It is left as a future subject that we develop the algorithm that is set up the value of an optimal parameter automatically in accordance with characteristic of the JPEG compression image.
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References
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Fig. 1 The fundamental idea of ε-filter1) j i
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Calculation of Variance i
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JPEG Decoded Image
Variance Matrix
Fig. 2 The concept of calculation of variance Maximum Value of Variance × A
1) H. Harashima, K. Odajima, Y. Shishikui, and H. Miyakawa, The Transaction of the Institute of Electronics and Communication Engineers of Japan, J66-A, 4, pp. 297- 304(1982). 2) W. B. Pennebaker and J. L. Mitchell, JPEG Still Image Data Compression Standard, Van Nostrand Reinhold, New York(1993). 3) S. Sasaki, T. Shohdohji, Y. Hoshino, Proceedings of the Annual Conference of the Imaging Society of Japan (Japan Hardcopy 2001), pp. 181- 184 (2001). 4) S. Sasaki, T. Shohdohji, Proceedings of the 44th Japan Joint Automatic Control Conference, pp. 528531 (2001). 5) T. Shohdohji, N. Iijima, S. Kitakubo, and Y. Hoshino, Journal of the Imaging Society of Japan, 38, 2(1999). 6) T. Shohdohji and Y. Hoshino, Applied Mathematics and Computation, 120, 1- 3, pp. 301- 311(2001).
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8 8 ε Matrix
Variance Matrix
Fig. 3 Relation between variance matrix and εmatrix j
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Fig. 5 JPEG decoded image (1.2 bits/pixel)
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Fig. 6 An image by our proposed method
ε Matrix ε-Filter j
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Fig. 4 Process ofε-filter
Fig. 7 An image by the ε-filter method (ε = 30)
Fig. 8 Emphasis of the outline of Fig. 7
