iccv iccv2013 iccv2013-19 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Inchang Choi, Sunyeong Kim, Michael S. Brown, Yu-Wing Tai
Abstract: Single image matting techniques assume high-quality input images. The vast majority of images on the web and in personal photo collections are encoded using JPEG compression. JPEG images exhibit quantization artifacts that adversely affect the performance of matting algorithms. To address this situation, we propose a learning-based post-processing method to improve the alpha mattes extracted from JPEG images. Our approach learns a set of sparse dictionaries from training examples that are used to transfer details from high-quality alpha mattes to alpha mattes corrupted by JPEG compression. Three different dictionaries are defined to accommodate different object structure (long hair, short hair, and sharp boundaries). A back-projection criteria combined within an MRF framework is used to automatically select the best dictionary to apply on the object’s local boundary. We demonstrate that our method can produces superior results over existing state-of-the-art matting algorithms on a variety of inputs and compression levels.
Reference: text
sentIndex sentText sentNum sentScore
1 Brown2 Yu-Wing Tai1 Korea Advanced Institute of Science and Technology (KAIST)1 National University of Singapore (NUS)2 Abstract Single image matting techniques assume high-quality input images. [sent-2, score-0.387]
2 JPEG images exhibit quantization artifacts that adversely affect the performance of matting algorithms. [sent-4, score-0.499]
3 To address this situation, we propose a learning-based post-processing method to improve the alpha mattes extracted from JPEG images. [sent-5, score-1.091]
4 Our approach learns a set of sparse dictionaries from training examples that are used to transfer details from high-quality alpha mattes to alpha mattes corrupted by JPEG compression. [sent-6, score-2.212]
5 Three different dictionaries are defined to accommodate different object structure (long hair, short hair, and sharp boundaries). [sent-7, score-0.142]
6 We demonstrate that our method can produces superior results over existing state-of-the-art matting algorithms on a variety of inputs and compression levels. [sent-9, score-0.502]
7 Introduction Single image matting is a problem that has been extensively studied in computer vision and graphics. [sent-11, score-0.381]
8 Virtually all image matting techniques assume that the input image is free of notable compression artifacts. [sent-13, score-0.521]
9 The vast majority of these images are encoded using JPEG compression that exhibits compression artifacts that adversely affect the quality of the extracted alpha matte. [sent-15, score-1.16]
10 Figure 1demonstrates how various levels of JPEG compression (expressed as quality levels) affect the performance of several state-of-the-artmatting methods. [sent-16, score-0.213]
11 The RMSE of alpha mattes extracted from several stateof-the-art methods are plotted for different JPEG compression qualities (1 = lowest; 12 = highest)1 . [sent-29, score-1.241]
12 Compared to the RMSE of the pixel values, the RMSE of the corresponding alpha mattes increases dramatically as the compression ratio increases. [sent-30, score-1.196]
13 Note that the root mean square error (RMSE) of the alpha mattes suffers worse than the RMSE of the compressed image itself. [sent-31, score-1.137]
14 g quality 92), which represent common compression level used on camera phones and social media sites, have subtle artifacts that can adversely affect the resulting alpha mattes. [sent-33, score-0.986]
15 The contribution of this paper is to propose a learningbased post-processing method for improving alpha mattes in the face of JPEG compression artifacts. [sent-34, score-1.215]
16 Specifically, we propose a technique that learns a set of sparse dictionaries to transfer high-quality details to low-quality alpha mattes extracted from compressed images. [sent-35, score-1.254]
17 Because matting boundaries are far more complicated than natural image boundaries, our methods uses three different dictionaries (i. [sent-36, score-0.488]
18 While our approach cannot compete with mattes extracted from uncompressed input images, it is useful in improving the quality of alpha mattes over the 2Photoshop categorized quality 10-12 to be maximum quality, 8-9 to be high quality, 5-7 to be medium quality, and 1-4 to be it low quality. [sent-39, score-1.701]
19 We tested our approach extensively on a variety of inputs and compression levels which demonstrates superior remedy results over state-of-the-art matting algorithms in comparisons with their initial corrupted alpha mattes. [sent-41, score-1.176]
20 JPEG compression The Joint Photographic Experts Group (JPEG) compression standard developed over two decades ago is the most widely adopted image compression method to date (for further details see [25]). [sent-45, score-0.402]
21 Because JPEG is lossy, the uncompressed image contains errors that are in the form of frequency domain ringing and blocking artifacts that are collectively referred to as compression artifacts. [sent-46, score-0.326]
22 For natural images it is often difficult to perceptually see these errors even for images with medium compression qualities around 5-7. [sent-47, score-0.184]
23 While the compressed images may be perceptually acceptable, the resulting compression artifacts are well known to adversely affect low-level image processing routines. [sent-48, score-0.362]
24 As shown in Figure 1, image matting is no exception. [sent-49, score-0.368]
25 These approaches target low-quality compressed images and perform various types of filtering to reduce blocking and ringing artifacts. [sent-53, score-0.143]
26 As shown in our results, we achieve better results than deblocking applied either as pre-processing to the input image or as post-processing to the alpha matte. [sent-55, score-0.793]
27 Image matting Image matting approaches (for a nice overview see [26]) can be roughly classified into two categories: affinity-based methods and sampling-based methods. [sent-56, score-0.748]
28 [19, 12, 11, 3]) estimated alpha values for the unknown region by propagating the known alpha values in accordance with the pixel affinities. [sent-59, score-1.322]
29 Affinity-based approaches propagate alpha values well in uncompressed and maximum-quality JPEG images (e. [sent-60, score-0.719]
30 However, these methods fail to effectively propagate the alpha values across block artifacts when an image is compressed. [sent-63, score-0.744]
31 [4, 27, 7, 8]) estimate alpha mattes by sampling the foreground and background color. [sent-66, score-1.101]
32 For each pixel with an unassigned alpha value, these approaches find the most plausible pair of the foreground and the background pixels around it and solve the matting compositing equation with the sampled color pairs. [sent-67, score-1.078]
33 As with affinity-based methods, sample-based methods are adversely affected by the ringing artifacts and quantization across different blocks. [sent-68, score-0.149]
34 For both approaches, obtaining mattes with detailed structure is difficult due to the blurring effect introduced by the DCT quantization. [sent-69, score-0.401]
35 This is often because these methods first apply a smoothing to the input image to reduce ringing and blocking artifacts (e. [sent-76, score-0.153]
36 [9]) which can remove high-frequency information before the matting is applied. [sent-79, score-0.368]
37 Our work, however, directly operates on alpha mattes instead of pixel intensity. [sent-81, score-1.062]
38 Since matte boundaries are more complicated than natural image boundaries (e. [sent-82, score-0.318]
39 Matting for JPEG images As previously discussed, we adopt a learning-based approach used in single image super-resolution for our matting problem. [sent-87, score-0.368]
40 Our input is a low-quality alpha matte extracted from conventional matting algorithms, e. [sent-88, score-1.311]
41 Our goal is to estimate a high-quality alpha matte by transferring details via a dictionary learned from highquality and low-quality alpha matte patch pairs. [sent-91, score-1.92]
42 In the following, we denote y be the input low-quality alpha matte, x be the output high-quality alpha matte. [sent-92, score-1.341]
43 Problem definition and overview Following the work from [29], we assume the alpha matte within each 8 8 block can be sparsely represented as a linear combination of a set of basis functions: y = Dlφ, (1) where φ ∈ Rk is a vector of sparse coefficients, e. [sent-95, score-0.928]
44 k, and Dl is a dictionary containing basis functions learned from low-quality alpha mattes extracted from JPEG compressed images. [sent-100, score-1.261]
45 In a similar context, we define the high-quality alpha matte patch as: x = Dhφ, (2) where Dh is a dictionary learned from high-quality alpha mattes extracted from images without any compression. [sent-101, score-2.099]
46 The learned dictionaries computed from different training examples group as long hair, short hair, and sharp boundary. [sent-103, score-0.145]
47 The two dictionaries, Dl and Dh, are co-trained using a set of alpha matte pairs. [sent-105, score-0.895]
48 Where each pair contains an alpha × × matte extracted from the high-quality input image (either uncompressed or compressed at a high-quality, e. [sent-106, score-1.076]
49 JPEG quality 12) and the alpha matte extracted from the same image with lower-quality compression, e. [sent-108, score-0.984]
50 Hence, for a given low-quality alpha matte input, we can first estimate the sparse coefficients φ and then replace the low-quality dictionary Dl with the high-quality dictionary Dh to reconstruct a high-quality matte. [sent-112, score-1.137]
51 Joint dictionary training Sparse coding is used to learn the dictionary as follows: Dc = arg mDci,nZ= ? [sent-115, score-0.19]
52 , xn} is the set of high-quality 8 8 alpha mattes and Yl = {y1, y2, . [sent-127, score-1.062]
53 , yn} is the set of the corresponding low-quality 8 8 alpha mattes. [sent-130, score-0.661]
54 Since the size of the high- and low-quality alpha matte pairs are the same, JPEGinputLow-quailtyaplhamate Figure3. [sent-131, score-0.895]
55 Matte reconstruction from dictionary × Our next step is to reconstruct a high-quality alpha matte given a low-quality alpha matte input. [sent-145, score-1.897]
56 For each 8 8 block in the input alpha matte, we estimate the sparse coefficients by minimizing: φ∗ = argmφin? [sent-146, score-0.737]
57 In order to guarantee compatibility between neighboring blocks, we follow [29] and use an overlapping window in the reconstructed high-quality alpha matte to constrain the coefficients estimation in Equation (6). [sent-152, score-1.001]
58 ,(8) where P is a matrix that extracts the overlapping region between the current patch and the previous patches, and w is the alpha values of the previously reconstructed alpha mattes in the overlapping areas. [sent-161, score-1.818]
59 Optimal dictionary selection for dif erent regions along matting boundary. [sent-164, score-0.463]
60 Red: Long hair dictionary; Green: Short hair dictionary; Blue: Sharp boundary dictionary. [sent-165, score-0.145]
61 Implementation: dictionary selection As previously mentioned, we found that using a single dictionary learned from generic images did not produce the best quality results. [sent-168, score-0.25]
62 First, a highquality alpha matte is first reconstructed using all three dictionaries individually. [sent-172, score-1.035]
63 This allows us to reconstruct a high-quality image patch using the matting composite equation [16]. [sent-174, score-0.426]
64 Finally, the appropriateness of each dictionary is evaluated by measuring the RMSE between the compressed reconstructed patch and the original input patch. [sent-176, score-0.269]
65 With our multiple dictionaries approach, hairy structures of matte can be better reconstructed. [sent-186, score-0.332]
66 Figure 5 shows the comparisons of the reconstructed alpha matte between single dictionary approach and multiple dictionaries approach. [sent-190, score-1.113]
67 Experimental results Our approach is tested extensively using the following matting algorithms: closed-form matting [12], KNN matting [3], global sampling matting [8], and learning- based matting [30]. [sent-192, score-1.876]
68 Closed-form matting [12] was applied to the uncompressed image and compressed image to produce the training example pairs3. [sent-199, score-0.501]
69 Figure 6 shows several results using different quality factors and matting methods. [sent-202, score-0.428]
70 In each example, the initial extracted matte is used as input in our method. [sent-203, score-0.282]
71 The initial mattes all suffer from blurriness and blocky artifacts due to the input’s JPEG compression. [sent-208, score-0.511]
72 Applying our 3We found little difference among the results when using other matting methods to prepare the training examples. [sent-209, score-0.368]
73 We compared RMSE between results of matting algorithms and their reconstructed 10−3. [sent-260, score-0.419]
74 GT21 GT25 GT26 alpha mattes by our algorithm. [sent-261, score-1.062]
75 The unit is CFM, KNN, GSM, and LBM stand for closed-form matting [12], KNN matting [3], global sampling matting [8], and learning- based matting [30], respectively. [sent-262, score-1.495]
76 These are applied in two ways: in one case, we deblocked the JPEG image and then applied matting; in the other case, we applied matting to the JPEG image and applied deblocking to the alpha matte. [sent-266, score-1.192]
77 The alpha mattes extracted from the pre-processed deblocked input images are blurry and have poorly defined boundaries. [sent-267, score-1.16]
78 Interestingly, post-processing the extracted alpha mattes with the deblocking algorithm gives better results. [sent-268, score-1.204]
79 It preserves the shape well and succeeds in removing the blocky compression artifacts to some degree. [sent-269, score-0.233]
80 When compare the results quantitatively, our approach produces the best alpha mattes with the minimum RMSE as compared to the ground truth alpha mattes. [sent-270, score-1.723]
81 Not only does our algorithm eliminates the JPEG compression artifacts, but also results in well defined boundaries of the target object. [sent-271, score-0.17]
82 We compute the RMSE from the initial alpha mattes and those after applying our detail transfer. [sent-272, score-1.062]
83 Our results reconstructed the hairs with sharper details but does not fully align with the ground truth alpha mattes. [sent-275, score-0.723]
84 Finally, we apply our algorithm to images captured Images from both cell phone cameras have compressions quality between quality 10 and quality 11but closer to quality 10. [sent-278, score-0.299]
85 Figure 8 shows our results which apply to the alpha mattes extracted from the global sampling matting [8] and closed-form matting [12] with the original files from the cell phone cameras as input. [sent-280, score-1.909]
86 Note that compression artifacts show up in the estimated alpha mattes but they are almost invisible in the original input images. [sent-281, score-1.281]
87 Our approach can successfully refines the alpha mattes with better visual quality. [sent-282, score-1.062]
88 Discussion and conclusion We have presented a method to refine alpha mattes from JPEG compressed images. [sent-284, score-1.137]
89 While there is a previous work that targets matting for degraded image [13, 17], as far as we are aware, this is the first work to seriously address the problem of compression artifacts on image matting. [sent-285, score-0.582]
90 Our method works directly on the alpha mattes using three separate dictionaries to accommodate various boundary structures as well as a back-projection method to select the appropriate dictionary for detail transfer. [sent-287, score-1.27]
91 Our method is able to improve the current state-of-the-art image matting results and preforms better than applying JPEG deblocking to the input or extracted mattes. [sent-288, score-0.54]
92 We are interested in extending this scheme to the wavelet-based JPEG2000 compression from cell phone cameras. [sent-290, score-0.193]
93 2884 (JPEG quality 7), the third example is extracted by Global sampling matting [8] (quality 5), and the final example is extracted by learningbased matting [30] (JPEG quality 7). [sent-296, score-0.956]
94 In the zoomed-in areas, images on the top were produced using JPEG alpha mattes, and the middles are our reconstructed alpha mattes. [sent-297, score-1.373]
95 2885 × (b) Closed-form matting [12], (c) KNN matting [3], (d) Global sampling matting [8], (e) learning-based matting [30]. [sent-299, score-1.495]
96 In the example, from the top, alpha mattes of JPEG input images (the first row), alpha mattes of deblocked input images and deblocked alpha mattes as post-processing by DeJPEG (the second and third rows), and by BM3D (the fourth and fifth rows), and our reconstructed alpha mattes (the final row). [sent-300, score-4.437]
97 Zoomed-in regions show the input images, estimated alpha reconstructed alpha mattes and the corresponding composition respectively. [sent-307, score-1.793]
98 Pointwise shapeadaptive dct for high-quality denoising and deblocking of grayscale and color images. [sent-347, score-0.146]
99 Efficient learning-based image enhancement: Application to superresolution and compression artifact removal. [sent-371, score-0.149]
100 A deblocking algorithm for jpeg compressed images using overcomplete wavelet representations. [sent-510, score-0.489]
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