Efficient image structural similarity quality assessment method using image regularised feature

IET Image ProcessingVolume 14, Issue 16 p. 4401-4411 Research ArticleFree Access Efficient image structural similarity quality assessment method using image regularised feature Yajing Li, Yajing Li College of Computer Science and Technology, Qingdao University, Qingdao, 266071 People's Republic of ChinaSearch for more papers by this authorBaoxiang Huang, Corresponding Author Baoxiang Huang hbx3726@163.com orcid.org/0000-0002-0380-419X College of Computer Science and Technology, Qingdao University, Qingdao, 266071 People's Republic of ChinaSearch for more papers by this authorHuan Yang, Huan Yang College of Computer Science and Technology, Qingdao University, Qingdao, 266071 People's Republic of ChinaSearch for more papers by this authorGuojia Hou, Guojia Hou orcid.org/0000-0001-6509-6259 College of Computer Science and Technology, Qingdao University, Qingdao, 266071 People's Republic of ChinaSearch for more papers by this authorPengfei Zhang, Pengfei Zhang School of Computer Science, University of Nottingham, University Park, Nottingham, NG7 2RD UKSearch for more papers by this authorJinming Duan, Jinming Duan School of Computer Science, University of Birmingham, Edgbaston, B15 2TT UKSearch for more papers by this author Yajing Li, Yajing Li College of Computer Science and Technology, Qingdao University, Qingdao, 266071 People's Republic of ChinaSearch for more papers by this authorBaoxiang Huang, Corresponding Author Baoxiang Huang hbx3726@163.com orcid.org/0000-0002-0380-419X College of Computer Science and Technology, Qingdao University, Qingdao, 266071 People's Republic of ChinaSearch for more papers by this authorHuan Yang, Huan Yang College of Computer Science and Technology, Qingdao University, Qingdao, 266071 People's Republic of ChinaSearch for more papers by this authorGuojia Hou, Guojia Hou orcid.org/0000-0001-6509-6259 College of Computer Science and Technology, Qingdao University, Qingdao, 266071 People's Republic of ChinaSearch for more papers by this authorPengfei Zhang, Pengfei Zhang School of Computer Science, University of Nottingham, University Park, Nottingham, NG7 2RD UKSearch for more papers by this authorJinming Duan, Jinming Duan School of Computer Science, University of Birmingham, Edgbaston, B15 2TT UKSearch for more papers by this author First published: 18 February 2021 https://doi.org/10.1049/iet-ipr.2019.1570AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Image regularised features play a critical role in image processing domain, by integrating regularised feature and structural similarity, a new full-reference image assessment method (IRF_SSIM) is proposed in this study. As well known, the gradient operator always be used to capture the edge information of the image, while the total variational regularised features can be adopted to calculate the detailed change information of image contrast and texture, as well as noise removal and edge retention. Therefore, the IRF_SSIM method extends the gradient features into the image regularised features to measure the structural changes in the image. In addition, image quality is also affected by variations of luminance and contrast. For a more comprehensive image quality assessment, the IRF_SSIM method considers the changes in structure, luminance and contrast simultaneously. In other words, the total image quality is estimated by structural similarity calculated by integrating the effects of image structure, luminance and contrast changes. Comparing with the representative methods, the experimental results illustrate that the IRF_SSIM method is highly consistent with the subjective assessment results. 1 Introduction Image quality assessment (IQA) is a popular topic in image research domain. In general, the IQA methods can be divided into two categories: subjective and objective assessment methods [[1]]. Subjective assessment results are directly provided by observers [[2]], so it is an accurate and reliable way to evaluate the difference between reference and distorted images. However, subjective assessment methods are labourious, time-consuming and expensive. Meantime, assessment results are affected by various factors, such as observer, image type and observation environments [[3], [4]]. On the other hand, results of objective [[5]] assessment methods are mainly obtained by some mathematical models [[6], [7]]. In addition, the mathematical expression processes are simple and convenient. Further, objective assessment manners can be divided into three types [[4]]: full-reference, no-reference and reduced-reference methods. The full-reference methods have access to all the information of the reference image [[8]-[10]], so there are the most reliable ways of objective assessment methods. Correspondingly, the no-reference methods only evaluate the distorted image and do not require any details of original image [[2]]. The reduced-reference methods [[11], [12]] have access only to obtain partial details in the reference image. Consequently, full-reference methods will be used in this paper. The traditional objective assessment methods mainly include mean square error (MSE) [[13]] and peak signal to noise ratio (PSNR) [[14]]. These two methods intuitively can reflect the difference between the original image and the test image as a whole, but can not reflect the situation where a small number of pixels have large greyscale differences and most pixels have small greyscale differences. Therefore, the results of these methods cannot be consistent with the subjective feelings of the human eye [[6], [7]]. In order to make the final assessment results consistent with the subjective perception of the observer, the human visual system (HVS) will be combined with the objective assessment methods [[15], [16]]. With an in-depth understanding of the HVS, people have begun to evaluate certain features of the human vision system [[10], [17], [18]]. The visual model is constructed by obtaining the characteristics of the HVS through physiological and psychological experiments, so as to simulate human vision for the objective appraisal of image quality. Typical methods are noise quality measure (NQM) [[16]] and visual signal-to-noise ratio (VSNR) [[19]], but these methods have not yielded satisfactory results. Later, Wang and Bovik [[20]] and Wang et al. [[4], [21]] have measured the degree of degradation of the structural information of deformed image to assessment the image, and proposed the theory of structural similarity (SSIM). The principle of SSIM is to compare luminance, contrast and structural information between the original image and the deformed image. Thinking of the impact of the human vision [[4], [7]], the SSIM method has been widely used because it achieves a good result. Compared with the traditional MSE [[13]] and PSNR [[14]], the SSIM has been significantly improved. However, the SSIM-related methods do not take into account the physiological and psychological factors in human vision, so this method has a certain distance from the ideal assessment effect [[22], [23]]. A large number of experiments illustrate that human vision is extremely sensitive to the edge of the image. The gradient can reflect the slight detail contrasts and texture changes in the image [[2], [24]], and assess the clarity of the image, thus introducing the total variational gradient feature into the main structural information to measure the image [[25], [26]]. Image regularised features are functions of image gradient, which can be used to express the image structure. Consequently, the purpose of this study is to extend the gradient feature to image regularised features for constructing a new image assessment methodology to provide a more comprehensive evaluation. In this study, we propose a full-reference IQA method based on regularised features and verify its effectiveness through a large number of experiments. Similar to the SSIM method, the novel assessment method takes into account the changes in luminance and contrast of the image. The remaining part of this research is arranged as follows. Section 2 is concerned with the proposed methodology, mainly includes image regularised features and structural alternation, luminance change and contrast variation. The experimental results on LIVE, TID2008 and TID2013 databases are reported in Section 3. Finally, Section 4 summarises the paper. 2 Proposed image assessment method To achieve a satisfactory IQA result, the regularised features are integrated with the SSIM objective evaluation. In this section, we first introduce the proposed quality assessment method in detail. Then we analyse the image structural texture alteration and indicate the application of the regularised features in the first subsection. In addition, the high order regularised feature discrete method is introduced in the second subsection. Following that, we give the computation of luminance change and contrast variation. The framework of IRF_SSIM method is provided in the final subsection. 2.1 Structural information alternation and regularised features The variational model [[27], [28]] based on L 1 norm [[29]] can remove noise while maintaining the edges of the image. Therefore, the regularised feature based on L 1 specification can be used to measure the structural changes of reference and compressed images. The description of the total variational equation is as follows: E u = ∫ Ω ∇ u d x (1) ∇ u = u x u y (2) where Ω represents the area of image and u represents the image. The central difference operators are defined as ∂ x − u i , j = u i , j − u i − 1 , j h ∂ y − u i , j = u i , j − u i , j − 1 h ∂ x + u i , j = u i + 1 , j − u i , j h ∂ y + u i , j = u i + 1 , j − u i , j h (3) The total variational regularised features can be defined as ∂ x 0 u i , j = u i + 1 , j − u i − 1 , j 2 h ∂ y 0 u i , j = u i , j + 1 − u i , j − 1 2 h ∇ u i , j = u x u y = ∂ x 0 u i , j ∂ y 0 u i , j ∇ u i , j = u x 2 + u y 2 = u i + 1 , j − u i − 1 , j 2 h 2 + u i , j + 1 − u i , j − 1 2 h 2 (4) Traditional discrete total variational regularised features with only four nearest neighbours do not express the structural information of the image well. To better represent the image edge and texture information, the neighbourhood window can be extended, such as adding 4 diagonal neighbourhoods or using 16 weighted neighbourhoods [[30]]. Fig. 1 describes the selection of neighbourhoods of different sizes for discretesation ∇ u : Discrete form with four nearest neighbours is as follows: u i , j + 1 u i − 1 , j u i , j u i + 1 , j u i , j − 1 Discrete form adding four diagonal neighbours is as follows: u i − 1 , j + 1 u i , j + 1 u i + 1 , j + 1 u i − 1 , j u i , j u i + 1 , j u i − 1 , j − 1 u i , j − 1 u i + 1 , j − 1 Discrete form using 16 weighted neighbours is as follows: u i − 1 , j + 2 u i + 1 , j + 2 u i − 2 , j + 1 u i − 1 , j + 1 u i , j + 1 u i + 1 , j + 1 u i + 2 , j + 1 u i − 1 , j u i , j u i + 1 , j u i − 2 , j − 1 u i − 1 , j − 1 u i , j − 1 u i + 1 , j − 1 u i + 2 , j − 1 u i − 1 , j − 2 u i + 1 , j − 2 As shown in Fig. 2, the dispersion of the 4 and 16 points can not reflect the details and texture characteristics of the image well, while the discrete effect of eight points describe the structural information of the picture in detail. Therefore, the separation of eight neighbourhoods is adopted in this study. Refer to Section 3.1.3 for the correlation analysis of choosing different discrete neighbours. Fig. 1Open in figure viewerPowerPoint Choice of neighbourhoods of different sizes (a) 4 neighbours, (b) 8 neighbours, (c) 16 neighbours Fig. 2Open in figure viewerPowerPoint Regularised feature map based on different discrete neighbours. Fisrt column: reference image; Second column: using 4 neighbours; Third column: using 8 neighbours; fourth column: using 16 neighbours According to the description of the variational regularised features and point discretisation, image SSIM is defined as IRF x , y = 2 ∗ IRF x ∗ IRF y + C IRF x 2 + IRF y 2 + C (5) where I R F x and I R F y are the regularised features for the reference image x and distorted image y, respectively, and C is the small constant to avoid the denominator IRF x 2 + IRF y 2 + C being zero; IRF is the SSIM between image x and y. 2.2 High order regularised feature discrete method Variational model plays a significant role in the field of image processing, and variational regularised features are often used to express the smoothness of images [[31]]. According to the different regularised features, the variational models are mainly divided into two types: L 1 and L 2 norm [[29]]. As mentioned in Section 2.1, ∇ u is a variational term based on the L 1 norm. Regularised feature based on L 2 norm commonly includes Laplacian item Δ u [[32]], Hessian item ∇ 2 u [[33]] and curvature item ∇ ⋅ ∇ u / ∇ u [[34], [35]]. The discrete rules for regularised feature have a spatial grid of size h. The Laplacian, curvature and Hessian item are defined as mathematical formulas (6) –(8), respectively. Δ u i , j = u i + 1 , j + u i − 1 , j + u i , j + 1 + u i , j − 1 − 4 u i , j h 2 (6) ∇ ⋅ ∇ u ∇ u ε i , j = ∂ x − ∂ x + u i , j ∂ x + u i , j 2 + ∂ y 0 u i , j 2 + ε 2 + ∂ y − ∂ y + u i , j ∂ y + u i , j 2 + ∂ x 0 u i , j 2 + ε 2 (7) ∇ 2 u = ∂ 2 u ∂ x 2 2 + ∂ 2 u ∂ x ∂ y 2 + ∂ 2 u ∂ y ∂ x 2 + ∂ 2 u ∂ y 2 2 (8) Variational regularised features typically have the characteristics of noise removal. As shown in Fig. 3, the regularity terms based on the L 2 norm have the function of overcoming edge blurring and staircase effects, while the L 1 norm can better reflect the small contrast and texture changes in the image. Therefore, the total variational regularised feature based on the L 1 norm will be adopted in this paper. Fig. 3Open in figure viewerPowerPoint Image based on L 1 norm or L 2 norm. Fisrt column: original image; second column: total variational image based on L 1 norm; third column: curvature item image based on L 2 norm; fourth column: Hessian item image based on L 2 norm; fifth: Laplacian item image based on L 2 norm 2.3 Luminance change and contrast variation The three most important elements image structure, luminance [[36]] and contrast [[37], [38]] are considered comprehensively in this paper. The regularised features of the image proposed in Section 2.1 formula (5) can be used for assessing the SSIM between the original and deformed image. In this section, we mainly discuss how to use luminance and contrast similarity. Supposing x and y are two non-negative signals in the image that are aligned with each other [[4]], we specify that one of the signals is complete and correct, so it can be served as a measure of the similarity of another signal quality [[4], [20], [39]]. The IQA method of SSIM mainly includes three aspects: luminance, contrast and structure. Two significant aspects of luminance and contrast information are applied to research. The SSIM model is defined as follows: S x , y = l x , y α ⋅ c x , y β ⋅ s x , y γ (9) where α , β and γ are all in a positive range and generally used to adjust the weight of luminance, contrast and structure, respectively. It needs to note that these three aspects are independent of each other. l x , y is a function related to μ x and μ y . Fig. 4 provides the luminance map. The definition of the luminance function is as follows: l x , y = 2 μ x μ y + C 1 μ x 2 + μ y 2 + C 1 (10) where μ x and μ y express the mean of x and y, respectively, and C 1 is a constant avoiding the denominator μ x 2 + μ y 2 being zero. Specifically, we define C 1 as C 1 = K 1 L 2 (11) where L is the dynamic range of the pixel values (255 for 8-bit greyscale images), and K 1 ≪ 1 is a small constant. Fig. 4Open in figure viewerPowerPoint Luminance similarity image. First column: reference image; second column: comparison map of luminance; third column: distorted image c x , y is a function related to σ x and σ y . Fig. 5 provides the contrast map. The definition of contrast function is similar to the luminance function. c x , y = 2 σ x σ y + C 2 σ x 2 + σ y 2 + C 2 (12) where σ x and σ y express the standard deviation of x and y, respectively, C 2 = K 2 L 2 , and K 2 ≪ 1. Fig. 5Open in figure viewerPowerPoint Contrast similarity image. First column: reference image, second column: comparison map of contrast, third column: distorted image s x , y is a function reflecting the similarity of structure information. s x , y = σ x y + C 3 σ x σ y + C 3 (13) where σ x y represents the correlation coefficient of x and y, and C 3 = C 2 / 2. Document [[4]] uses a Gaussian weighted window of 11 × 11 to move pixel by pixel in the complete image and the test image. Correspondingly, the SSIM of the two images is calculated in the window of 11 × 11, and finally, all the image blocks are combined for overall assessment. 2.4 Integration for overall quality assessment The structural flowchart describing computation of the IRF_SSIM assessment method is shown in Fig. 6. (1) First, the regularised feature transform is utilised to acquire the difference image of the reference and the distorted image, respectively; the 'IRF_Map' module is used to calculate the score of SSIM as in (5). Fig. 6Open in figure viewerPowerPoint Proposed IRF_SSIM assessment model. Here α = 0.9 and β = 0.7 (2) Then, the 'Luminance_Map' module is used to determine the value of luminance comparison. (3) Meanwhile, the 'Contrast_Map' module is the value of contrast comparison. (4) Finally, the 'Total_Map' is the combination of these three modules, it is used for evaluating the total image similarity. The parameters α and β in this model take values of 0.9 and 0.7, respectively. Refer to Section 3.1.3 for the correlation analysis of parameter values. 3 Experimental results 3.1 Experimental protocol 3.1.1 Database At present, there are seven main types of database commonly used for IQA, including LIVE database [[40]], CSIQ database [[41]], IVC database [[42]], WIQ database [[43]], A57 database [[44]], TID2008 database [[45]] and TID2013 database [[46]]. Table 1 describes the comparison of seven databases and their respective characteristics. Table 1. Comparison of seven databases and their respective features Database Original images Distorted images Degraded types Image type Observers TID2008 25 1700 17 colour 838 CSIQ 30 866 6 colour 25 LIVE 29 779 5 colour 161 IVC 10 235 4 colour 15 A57 3 54 6 grey 7 WIQ 7 80 5 grey 60 TID2013 25 3000 24 colour 971 The LIVE, TID2008 and TID2013 databases contain the most images and are the most widely used, so it is effective testing for IRF_SSIM assessment method by using these three databases. The three databases used in this experiment have been marked in bold in Table 1. The LIVE database is the standard database for IQA, containing 29 reference images and 779 compressed images. It includes five kinds of distortion: JPEG2000 compression (j2k), JPEG compression (jpg), White noise (wn), Gaussian blur (gb) and fast fading (ff). The differential mean opinion score (DMOS) value is required from nearly 25,000 data given by 161 observers, and the DMOS value range from 0 to 100. TID2008 includes 25 original images and 1700 deformed images. It contains 17 sorts of distortion: additive Gaussian noise, additive noise of the colour component stronger than the illumination component, spatial position-dependent noise, mask noise, high-frequency noise, impulse noise, quantisation noise, gb, image noise, JPEG compression, JPEG2000 compression, JPEG transmission error, JPEG2000 transmission error, non-eccentric noise, local block distortion of different intensities, intensity mean shift and contrast variation. The mean opinion score (MOS) value of the database is 256,428 data statistics obtained by the 838 observers, and the MOS value range is [0,9]. TID2013 is an enhanced version of TID2008 database. It is a colour distortion database that includes 25 complete images and 3000 test images. In addition to the existing 17 distortion types, 7 distortion types have been added, so there are a total of 24 types: changing colour saturation, multiple Gaussian noise, comfort noise, lossy compression, colour image quantisation, chromatic aberration and sparse sampling. The MOS result is acquired by 524,340 data statistics given by the 971 observers. The MOS score range from 0 to 9. 3.1.2 Assessment index The quality of the resulting image is measured by the assessment index and the human eye perception. In the experiment, we use the three coefficients Spearman rank-order correlation coefficient (SROCC), Pearson linear correlation coefficient (PLCC), root MSE (RMSE) to measure the correlation between objective test assessment criteria and subjective scores [[47]]. SROCC is employed to calculate the monotonicity of the evaluation results. This indicator only measures the rank order of the data points without considering the relative distance between the points. For the measurement of another two indicators, regression analysis is first performed, and then a non-linear mapping of test results and subjective values (MOS or DMOS) is established. PLCC evaluates the correlation between MOS values and test values after non-linear regression; RMSE is the MSE of objective fraction and MOS value after non-linear regression. Supposing that the array of subjective assessment scores is x, the array of objective assessment scores is y, and N is the size of x and y. First, sort x and y in descending order, and record x j and y j as the positions of x i , y i in the array after sorting in the original array, and x i ≠ y i . SROCC refers to the linear analysis of the rank size of two target arrays, and the scope of application is relatively wide. The calculation process of SROCC is as follows: SROCC = 1 − 6 ∑ x j − y j 2 N N 2 − 1 (14) The merit of the correlation between two linear variables is measured by PLCC index, which ranges from [-1,1] and has symmetry. PLCC can be calculated as follows: PLCC = cov x , y σ x σ y = E x − μ x y − μ y σ x σ y (15) RMSE represents the degree of dispersion between two object arrays. The smaller the RMSE value, the better the correlation. RMSE can be calculated as follows: RMSE = ∑ x i , y j 2 N − 1 (16) The higher the correlation between the results of the objective algorithm and the subjective assessment, the closer the values of the two correlation coefficients of PLCC and SROCC are to 1, and the smaller the value of RMSE, the more accurate the algorithm. 3.1.3 Parameters sensitivity analyses The proposed quality assessment model produces two parameters α and β. The parameter α mainly controls the influence of regularised features on image quality. In theory, the larger value of α is, the more obvious the structural information of the image, and the better the image quality. The parameter β reflects the effect of luminance and contrast on extracting areas of interest in the image, the best results gradually approach the larger parameter β during the quality assessment process. In order to verify the influence rules of parameters α and β on the quality evaluation, experiments were first performed on the LIVE database. We initialise the value of parameter α to 0.9, and then experiment with different values of parameter β to find the best value, where β ranges from 0.1 to 0.9. The experimental results of different values of parameter β are shown in Fig. 7, the best quality assessment results can be achieved when the parameter β = 0.7. Fig. 7Open in figure viewerPowerPoint IRF_SSIM model parameter analysis. Tune parameter β with α = 0.9. (a) Image in LIVE database, (b) α = 0 and β = 0.1, (c) α = 0.9 and β = 0.3, (d) α = 0.9 and β = 0.5, (e) α = 0.9 and β = 0.7, (f) α = 0.9 and β = 0.9 Second, we set the parameter β as 0.7 according to the above experiments, and then experimentally search for the optimal value of parameter α from 0.1 to 0.9. Fig. 8 shows the experimental results with different values of parameter α. When the evaluation score is optimal, the value of parameter α is 0.9. Fig. 8Open in figure viewerPowerPoint IRF_SSIM model parameter analysis. Tune parameter α with β = 0.7 (a) Image in LIVE database, (b) β = 0.7 and α = 0.1, (c) β = 0.7 and α = 0.3, (d) β = 0.7 and α = 0.5, (e) β = 0.7 and α = 0.7, (f) β = 0.7 and α = 0.9 The discrete form of variational regularisation features mainly includes 4 neighbours, 8 neighbours, and 16 neighbours. In order to verify the efficiency of the selected discrete method, experiments are performed on three image databases. The experimental results based on different discrete neighbourhoods are shown in Table 2, the best quality assessment results can be obtained when choosing the dispersion of eight neighbours. Table 2. Comparison of different discrete neighbours on three databases. Database Neighbours PLCC SROCC RMSE 4 0.9495 0.9278 7.2502 LIVE 8 0.9576 0.9403 6.6569 16 0.9491 0.9281 7.2790 4 0.6923 0.6546 0.9683 TID2008 8 0.9623 0.7259 0.8685 16 0.7266 0.6913 0.9220 4 0.8032 0.7910 0.7385 TID2013 8 0.8312 0.7556 0.6892 16 0.8161 0.7578 0.7165 Parametric analysis experiments were also performed on the TID2008 and TID2013 databases, and the results are similar to Figs. 6, 7 and Table 2. Consequently, the parameters of all experiments were set to α = 0.9 and β = 0.7, and eight neighbours were used in the discrete form of regularised features. 3.2 Performance evaluation Compared with eight profound types of measures, the performance of the IRF_SSIM assessment method can be better judged. This eight comparative methods include SSIM [[4]], PSNR [[19]], G_SSIM [[25]], VSNR [[48]], IFC [[49]], GSD [[50]], GW_PSNR and GW_SSIM [[25]]. The results obtained from the experiment of different IQA standards on LIVE, TID2008 and TID2013 databases can be compared in Fig. 9. Observing the histogram, the score of IRF_SSIM is significantly higher than that of other indicators, which proves the efficiency of this measure. Fig. 9Open in figure viewerPowerPoint Test results of different image assessment standards on the LIVE, TID2008 and TID2013 databases (a) PLCC and SROCC indices on LIVE database, (b) Three indices on TID2008 database, (c) RMSE index on LIVE database, (d) Three index on TID2013 database Fig. 10 shows the dispersion distribution and fitting curve of subjective MOS or DMOS and IRF_SSIM prediction scores in three databases. It can be seen from the fit curve that the IRF_SSIM value is highly close to DMOS or MOS, demonstrating the reliability and stability of this experiment. Fig. 10Open in figure viewerPowerPoint Scatter plots of subjective scores and experimental results on three databases (a) LIVE database, (b) TID2008 database, (c) TID2013 database 3.3 Evaluation on LIVE database To further examine the robustness of the IQA methods, five types of distortion for distortion-specific experiment can be tested on the LIVE image database. The specific type of distortion includes j2k, jpg, wn, gb and ff. Table 3 provides the experimental data for five types of distortion. Table 3. SROCC values on LIVE databases in several methods. IQA model j2k jpg wn gb ff SSIM 0.932 0.903 0.963 0.894 0.941 IFC 0.889 0.862 0.937 0.958 0.943 PSNR 0.890 0.841 0.985 0.782 0.890 GSD 0.911 0.931 0.879 0.964 0.953 VSNR 0.940 0.908 0.982 0.934 0.904