CN103873854B - The defining method of a kind of stereo-picture subjective assessment subject's quantity and experimental data - Google Patents
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Abstract
本发明公开了一种立体图像主观评价被试者数量及实验数据的确定方法,涉及立体图像质量主观评价领域,该方法包括:通过分析预处理数据,逆用置信区间公式确定主观评价被试者数量,从而缩短实验周期,节约成本;利用上述确定的合理数量的合格被试者进行主观评价实验,包括建立立体图像数据库、选择合理测试环境条件、根据标准选定被试者、实施被测试者训练与测试的流程,最终,根据图像质量主观评价标准规定和格鲁布斯检验法,淘汰异常被试者并合理筛选实验数据中的异常数据,避免了被试者和实验环境等不确定因素对实验结果准确性的影响。本方法在立体图像质量主观评价的基础上,从概率统计的角度出发,提高了实验数据结果的准确性。
The invention discloses a method for determining the number of subjects for subjective evaluation of stereoscopic images and experimental data, and relates to the field of subjective evaluation of stereoscopic image quality. Quantity, so as to shorten the experimental cycle and save costs; use the above-mentioned reasonable number of qualified subjects to conduct subjective evaluation experiments, including establishing a stereoscopic image database, selecting reasonable test environment conditions, selecting subjects according to standards, and implementing test subjects. In the process of training and testing, in the end, according to the subjective evaluation standard of image quality and the Grubbs test method, abnormal subjects are eliminated and abnormal data in the experimental data are reasonably screened to avoid uncertain factors such as subjects and experimental environment influence on the accuracy of experimental results. Based on the subjective evaluation of stereoscopic image quality, this method improves the accuracy of experimental data results from the perspective of probability and statistics.
Description
技术领域technical field
本发明涉及立体图像领域,尤其涉及一种立体图像主观评价被试者数量及实验数据的确定方法。The invention relates to the field of stereoscopic images, in particular to a method for determining the number of subjects and experimental data for subjective evaluation of stereoscopic images.
背景技术Background technique
目前,立体成像技术已被各个行业广泛应用。该技术包括立体图像的采集、压缩、传输、处理和显示,因此,如何利用立体图像质量评价方法衡量立体成像在这些过程中产生的图像损伤程度,已成为立体成像技术领域的研究热点之一。立体图像质量评价分为主观评价和客观评价两类,相对于客观评价方法,主观评价方法是直接利用人评判立体图像质量,能够更直观、更准确地反映立体图像的真实质量。主观质量评价必须经过诸多环节,主要包括立体图像数据库的建立、测试环境条件的选择、被试者的选定、训练与测试流程、实验数据的筛选与处理等。然而,实验环境和被试者状态的不确定性等因素,均会直接影响主观评价实验数据的准确性和精确性,同时,被试者的数量会直接影响实验测试时间和主观实验成本。因此,提出一套切实可行的被试者数量确定的方法和实验数据筛选的方法至关重要。At present, stereoscopic imaging technology has been widely used in various industries. This technology includes the acquisition, compression, transmission, processing and display of stereoscopic images. Therefore, how to use stereoscopic image quality evaluation methods to measure the degree of image damage caused by stereoscopic imaging in these processes has become one of the research hotspots in the field of stereoscopic imaging technology. Stereoscopic image quality evaluation is divided into two categories: subjective evaluation and objective evaluation. Compared with objective evaluation methods, subjective evaluation methods directly use people to judge the quality of stereoscopic images, which can more intuitively and accurately reflect the true quality of stereoscopic images. Subjective quality evaluation must go through many links, mainly including the establishment of a stereoscopic image database, the selection of test environment conditions, the selection of subjects, the training and test process, the screening and processing of experimental data, etc. However, factors such as the uncertainty of the experimental environment and the status of the subjects will directly affect the accuracy and precision of the subjective evaluation of the experimental data. At the same time, the number of subjects will directly affect the experimental testing time and subjective experimental costs. Therefore, it is very important to propose a feasible method for determining the number of subjects and screening methods for experimental data.
近年来,已有国内外学者开始对立体图像质量主观评价方案进行研究。国际电信联盟ITU在平面图像质量主观评价标准ITU-R BT.500-11的基础上提出了立体电视图像主观评价标准ITU-R BT.1438[1],对实验环境、测试源、被试者和测试流程进行了简单而笼统的介绍;文献[2]、[3]、[4]从观看条件、观看人员、测试方法等方面定义主观评价方案;文献[5]提出的主观评价方法中建立了包含经压缩和加噪降质的立体图像库;文献[6]从主观方面分析了影响立体感知质量的各种因素,并论述了现有的各个立体图像数据库的特点;文献[7]从建立立体图像数据库、被试者的选择、实验条件、被试者的训练及测试等方面建立了一个较完善的立体图像质量主观评价方案。与立体图像质量主观评价方案不同的是,文献[8]通过软件编程实现了一个人机交互的立体视频质量主观测试系统;文献[9]提出的立体视频质量主观评价方法中采用SPSS工具进行数据处理和比较。此外,立体图像质量主观评价方法往往用于研究与立体成像相关的各个因素和特性,文献[10]通过主观实验探讨了右视图质量和视差图质量对深度感知的影响;文献[11]通过主观实验探讨亮度与立体感知的关系。In recent years, domestic and foreign scholars have begun to study the subjective evaluation scheme of stereoscopic image quality. Based on the subjective evaluation standard ITU-R BT.500-11 of the planar image quality, the International Telecommunication Union ITU proposed the subjective evaluation standard ITU-R BT.1438 [1] for stereoscopic TV images. A simple and general introduction to the test process and the test process; literature [2], [3], [4] define the subjective evaluation scheme from the aspects of viewing conditions, viewing personnel, testing methods, etc.; the subjective evaluation method proposed in the literature [5] establishes The literature [6] analyzed various factors that affect the quality of stereo perception from the subjective aspect, and discussed the characteristics of the existing stereo image databases; literature [7] from Establishing a stereoscopic image database, selection of subjects, experimental conditions, training and testing of subjects, etc., established a relatively complete subjective evaluation scheme of stereoscopic image quality. Different from the stereoscopic image quality subjective evaluation scheme, literature [8] realizes a human-computer interaction stereoscopic video quality subjective testing system through software programming; the stereoscopic video quality subjective evaluation method proposed in literature [9] adopts SPSS tool for data process and compare. In addition, the subjective evaluation method of stereoscopic image quality is often used to study various factors and characteristics related to stereoscopic imaging. Literature [10] explores the influence of right view quality and disparity image quality on depth perception through subjective experiments; literature [11] uses subjective The experiment explores the relationship between brightness and stereo perception.
发明人在实现本发明的过程中发现,现有技术的主要缺点如下:The inventor finds in the process of realizing the present invention that the main shortcoming of the prior art is as follows:
目前,对于立体图像的主观评价方案尚无统一的标准,同时在建立或使用的立体图像质量主观评价方案中均未对被试者数量确定的方法和实验数据的筛选方法作具体规定,而被试者数量和实验数据的准确性直接决定了主观实验的复杂度、实验成本及实验结果的准确性,也是影响主观评价分值的重要因素。At present, there is no uniform standard for the subjective evaluation scheme of stereoscopic images. At the same time, in the established or used subjective evaluation schemes of stereoscopic image quality, there are no specific regulations on the method of determining the number of subjects and the screening method of experimental data. The number of subjects and the accuracy of experimental data directly determine the complexity, cost, and accuracy of experimental results of subjective experiments, and are also important factors affecting subjective evaluation scores.
发明内容Contents of the invention
本发明提供了一种立体图像主观评价被试者数量及实验数据的确定方法,本方法提高了与主观评价结果的正确性,详见下文描述:The present invention provides a method for determining the number of subjects and experimental data for subjective evaluation of three-dimensional images. This method improves the correctness of the subjective evaluation results. See the following description for details:
一种立体图像主观评价被试者数量及实验数据的确定方法,所述方法包括以下步骤:A method for determining the number of subjects and experimental data for subjective evaluation of stereo images, said method comprising the following steps:
(1)选取n名被试者进行立体图像质量预评价,得到主观评价分数,计算平均值和标准偏差;(1) Select n subjects to pre-evaluate the stereoscopic image quality, obtain subjective evaluation scores, and calculate the average value and standard deviation;
(2)根据所需精确度和置信度的要求,通过逆用置信区间公式,迭代运算得到主观实验实际需要的被试者数量N;(2) According to the required accuracy and confidence requirements, the number of subjects N actually required for the subjective experiment is obtained through iterative operations by inverting the confidence interval formula;
(3)选取N名被试者进行立体图像质量主观评价,计算每幅测试图像评价分数的平均值和标准差;(3) Select N subjects for subjective evaluation of stereoscopic image quality, and calculate the average and standard deviation of the evaluation scores for each test image;
(4)根据每幅测试图像评价分数是否满足正态分布,采用不同的评判分数异常的阈值pi和qi,计算得到评判异常数据的指标和通过比较,淘汰异常被试者;(4) According to whether the evaluation score of each test image satisfies the normal distribution, different thresholds p i and q i for judging abnormal scores are used to calculate the index for judging abnormal data and Eliminate abnormal subjects by comparison;
(5)采用格鲁布斯检验法筛选出每幅测试图像评价分数中的异常数据;(5) Use the Grubbs test method to screen out abnormal data in the evaluation scores of each test image;
(6)利用筛选后的不存在异常的被试者和数据,计算得出每幅测试图像的全部正常实验数据的平均值得到最终的主观评价结果。(6) Using the screened subjects and data without abnormalities, calculate the average value of all normal experimental data of each test image to obtain the final subjective evaluation result.
所述被试者数量确定具体为:The determination of the number of subjects is as follows:
N=(t2S2)/(r2mean2) (1)N=(t 2 S 2 )/(r 2 mean 2 ) (1)
其中,r表示可接受的相对百分偏差,mean、S分别表示少量被试者对单幅立体图像质量进行预评价的主观评价分数的平均值和标准偏差,t取初值1.96,代入公式(1)计算出被试者数量N的初值,再用对应于N初值的t值(查t分布表可得)代入公式(1)计算N值,不断迭代,直至计算出的N值不再发生变化,即被试者数量。Among them, r represents the acceptable relative percentage deviation, mean and S represent the average value and standard deviation of the subjective evaluation scores of a small number of subjects who pre-evaluate the quality of a single stereoscopic image, and the initial value of t is 1.96, which is substituted into the formula ( 1) Calculate the initial value of the number of subjects N, and then substitute the t value corresponding to the initial value of N (obtained from the t distribution table) into formula (1) to calculate the N value, and iterate continuously until the calculated N value does not exceed Another change occurs, namely the number of subjects.
所述采用格鲁布斯检验法筛选出每幅测试图像评价分数中的异常数据具体为:The abnormal data in the evaluation scores of each test image screened out by using the Grubbs test method is specifically:
首先,将每幅测试图像的评价分数以升序方式进行排列;根据公式(2)和(3)计算参数αi和γi,First, the evaluation scores of each test image are arranged in ascending order; parameters α i and γ i are calculated according to formulas (2) and (3),
αi=(ximax-ui)/Si (2)α i =(x imax -u i )/S i (2)
γi=(ui-ximin)/Si (3)γ i =(u i -x imin )/S i (3)
其中,ximax和ximin分别表示第i幅测试图像的全部评价分数中的最大值和最小值;ui和Si分别表示第i幅测试图像评价分数的平均值与标准差;最后,通过查临界值表获得对应被试者数量和置信度为95%的临界值G,若αi>G,则淘汰异常数据ximax,若γi>G,则淘汰异常数据ximin;最后,重复上述检验步骤直至实验数据中不再存在异常数据。Among them, x imax and x imin represent the maximum value and minimum value of all evaluation scores of the i-th test image respectively; u i and S i represent the average value and standard deviation of the i-th test image evaluation scores respectively; finally, pass Check the critical value table to obtain the critical value G corresponding to the number of subjects and a confidence level of 95%. If α i >G, then eliminate the abnormal data x imax , and if γ i >G, then eliminate the abnormal data x imin ; finally, repeat The above inspection steps are performed until there are no abnormal data in the experimental data.
本发明提供的技术方案的有益效果是:本方法完善了立体图像质量主观评价方案,与现有技术中相比,本方法着重研究了被试者数量确定和实验数据筛选的方法。实验结果表明,本方法切实可行,既保证了实验结果的可靠性,又节省了实验资源,提高了与主观评价结果的正确性。The beneficial effects of the technical solution provided by the invention are: the method perfects the subjective evaluation scheme of the stereoscopic image quality, and compared with the prior art, the method focuses on the method of determining the number of subjects and screening experimental data. The experimental results show that this method is feasible, which not only ensures the reliability of the experimental results, but also saves experimental resources and improves the correctness of the subjective evaluation results.
附图说明Description of drawings
图1为girl参考图像的左和右视点;Figure 1 is the left and right viewpoints of the girl reference image;
图2为family参考图像的左和右视点;Figure 2 is the left and right viewpoints of the family reference image;
图3为boy参考图像的左和右视点;Figure 3 is the left and right viewpoints of the boy reference image;
图4为flower参考图像的左和右视点;Figure 4 is the left and right viewpoints of the flower reference image;
图5为tree参考图像的左和右视点;Figure 5 is the left and right viewpoints of the tree reference image;
图6为river参考图像的左和右视点;Figure 6 is the left and right viewpoints of the river reference image;
图7为立体图像质量主观评价方案流程图;FIG. 7 is a flow chart of a subjective evaluation scheme for stereoscopic image quality;
图8为3D WINDOWS-19A0型计算机立体成像设备。Figure 8 is a 3D WINDOWS-19A0 computer stereoscopic imaging device.
具体实施方式Detailed ways
为使本发明的目的、技术方案和优点更加清楚,下面对本发明实施方式作进一步地详细描述。In order to make the purpose, technical solution and advantages of the present invention clearer, the implementation manners of the present invention will be further described in detail below.
为了提高与主观评价结果的正确性,本发明实施例提供了一种立体图像主观评价被试者数量及实验数据的确定方法,参考立体图像的左、右视点如图1~6所示,主观评价方案流程参见图7,详见下文描述:In order to improve the correctness of the subjective evaluation results, the embodiment of the present invention provides a method for determining the number of subjects and experimental data for the subjective evaluation of the stereoscopic image. The left and right viewpoints of the reference stereoscopic image are shown in Figures 1-6. See Figure 7 for the evaluation plan process, and see the description below for details:
101:选取n名被试者进行立体图像质量预评价,得到主观评价分数,计算平均值和标准偏差;101: Select n subjects to pre-evaluate the stereoscopic image quality, obtain subjective evaluation scores, and calculate the average value and standard deviation;
首先,利用较为完善的立体图像质量主观评价系统对选取的n名少量被试者进行预测试,包括建立立体图像数据库、确定观看条件、根据标准选定被试者、进行训练与测试[7],最终得到n名被试者对单幅立体图像质量进行预评价的主观评价分数{x1,x2,...xn},根据公式(1)和公式(2)分别计算它们的均值和标准偏差,First, use a relatively complete stereoscopic image quality subjective evaluation system to conduct a pre-test on a small number of selected subjects, including establishing a stereoscopic image database, determining viewing conditions, selecting subjects according to standards, and conducting training and testing [7] , and finally get the subjective evaluation scores {x 1 ,x 2 ,...x n } of n subjects who pre-evaluate the quality of a single stereo image, and calculate their mean values according to formula (1) and formula (2) and standard deviation,
102:根据所需精确度和置信度的要求,通过逆用置信区间公式,迭代运算得到主观实验实际需要的被试者数量N;102: According to the required accuracy and confidence requirements, the number of subjects N actually required for the subjective experiment is obtained through iterative operations by inverting the confidence interval formula;
根据公式(3)计算被试者数量N,Calculate the number of subjects N according to formula (3),
N=(t2S2)/(r2mean2) (3)N=(t 2 S 2 )/(r 2 mean 2 ) (3)
其中,r表示可接受的相对百分偏差,mean、S分别表示少量被试者对单幅立体图像质量进行预评价的主观评价分数的平均值和标准偏差;t首先取初值1.96,代入公式(3)计算出被试者数量N的初值,然后,再用对应于N初值的t值(查t分布表可得)代入公式(3)计算N值,不断迭代,最后,直至计算出的N值不再发生变化,即被试者数量。Among them, r represents the acceptable relative percentage deviation, mean and S represent the average value and standard deviation of the subjective evaluation scores of a small number of subjects who pre-evaluate the quality of a single stereoscopic image; t first takes the initial value of 1.96 and substitutes it into the formula (3) Calculate the initial value of the number of subjects N, and then use the t value corresponding to the initial value of N (obtained from the t distribution table) to be substituted into the formula (3) to calculate the N value, iteratively, and finally, until the calculation The N value out will no longer change, that is, the number of subjects.
103:选取N名被试者进行立体图像质量主观评价,计算每幅测试图像评价分数的平均值和标准差;103: Select N subjects to perform subjective evaluation of stereoscopic image quality, and calculate the average value and standard deviation of the evaluation scores of each test image;
首先,假定共使用I幅测试图像,根据上述计算设定参与主观实验的被试者N名,通过较为完善的立体图像质量主观评价系统再次进行测试,xi,j表示第j名被试者对第i幅测试图像的评价分数,计算每幅测试图像评价分数的平均值和标准差,如公式(4)和公式(5),First, assuming that a total of I test images are used, N subjects are set to participate in the subjective experiment according to the above calculation, and the test is carried out again through a relatively complete subjective evaluation system of stereoscopic image quality, x i,j represents the jth subject For the evaluation score of the i-th test image, calculate the average value and standard deviation of each test image evaluation score, such as formula (4) and formula (5),
其中,ui和Si分别表示第i幅测试图像评价分数的平均值与标准差。Among them, u i and S i represent the average value and standard deviation of the i-th test image evaluation scores, respectively.
104:根据每幅测试图像评价分数是否满足正态分布,采用不同的评判分数异常的阈值pi和qi,计算得到评判异常数据的指标和通过比较,淘汰异常被试者;104: According to whether the evaluation score of each test image satisfies the normal distribution, different thresholds p i and q i for judging abnormal scores are used to calculate the index for judging abnormal data and Eliminate abnormal subjects by comparison;
首先,分别根据公式(6)和(7)计算第i幅测试图像评价分数的二阶矩和四阶矩,得出第i幅测试图像评价分数的峰度系数,如公式(8),根据ITU-R BT.500-11的要求,若2≤β2i≤4,则第i幅测试图像的评价结果为正态分布;否则,为非正态分布。First, calculate the second-order moment and fourth-order moment of the evaluation score of the i-th test image according to formulas (6) and (7) respectively, and obtain the kurtosis coefficient of the evaluation score of the i-th test image, such as formula (8), according to According to the requirements of ITU-R BT.500-11, if 2≤β 2i ≤4, the evaluation result of the i-th test image is a normal distribution; otherwise, it is a non-normal distribution.
对于每幅测试图像,计算被试者评判分数是否异常的阈值pi和qi,若评价分数符合正态分布,则利用公式(9)和(10)计算;否则,利用公式(11)和(12)计算,For each test image, calculate the thresholds p i and q i for the subjects to judge whether the scores are abnormal. If the evaluation scores conform to the normal distribution, use formulas (9) and (10) to calculate; otherwise, use formulas (11) and (12) calculation,
pi=ui+2Si (9)p i =u i +2S i (9)
qi=ui-2Si (10)q i =u i -2S i (10)
然后,对第j名被试者对所有测试图像的评判分数进行分类,得到评判两类情况的计量参数Pj和Qj。即设初始值均为0,将第j名被试者的全部评价分数xi,j(i=1,2,...,I)分别与对应测试图像的pi、qi进行比较;如果第i幅测试图像的评价分数符合正态分布,则根据公式(9)和(10)判断,若xi,j≥pi,则Pj=Pj+1,若xi,j≤qi,则Qj=Qj+1;否则,根据公式(11)和(12)判断。Then, classify the judgment scores of all test images by the jth subject, and obtain the measurement parameters P j and Q j for judging two types of situations. That is, the initial values are all set to 0, and all evaluation scores x i,j (i=1,2,...,I) of the jth subject are compared with p i and q i of the corresponding test image; If the evaluation score of the i-th test image conforms to the normal distribution, judge according to formulas (9) and (10), if x i,j ≥ p i , then P j =P j +1, if x i,j ≤ q i , then Q j =Q j +1; otherwise, judge according to formulas (11) and (12).
最后,根据公式(13)和公式(14)计算评判异常数据的指标和若且则淘汰第j名被试者的所有评价数据,反之,保留评价数据。Finally, according to formula (13) and formula (14), the index for judging abnormal data is calculated and like and Then eliminate all the evaluation data of the jth subject, otherwise, keep the evaluation data.
105:采用格鲁布斯检验法筛选出每幅测试图像评价分数中的异常数据;105: Use the Grubbs test method to filter out the abnormal data in the evaluation score of each test image;
首先,将每幅测试图像的评价分数以升序方式进行排列,得出第i幅测试图像的全部评价分数中的最大值和最小值为ximax和ximin,根据公式(15)和(16)计算参数αi和γi,其中,ui和Si分别表示第i幅测试图像评价分数的平均值与标准差。First, the evaluation scores of each test image are arranged in ascending order, and the maximum and minimum values of all evaluation scores of the i-th test image are x imax and x imin , according to formulas (15) and (16) Calculate the parameters α i and γ i , where u i and S i represent the mean value and standard deviation of the evaluation scores of the i-th test image, respectively.
αi=(ximax-ui)/Si (15)α i =(x imax -u i )/S i (15)
γi=(ui-ximin)/Si (16)γ i =(u i -x imin )/S i (16)
最后,通过查临界值表获得对应被试者数量和置信度为95%的临界值G,若αi>G,则淘汰异常数据ximax,若γi>G,则淘汰异常数据ximin。之后,重复上述检验步骤直至实验数据中不再存在异常数据。Finally, the critical value G corresponding to the number of subjects and a confidence level of 95% is obtained by checking the critical value table. If α i >G, the abnormal data x imax will be eliminated, and if γ i >G, the abnormal data x imin will be eliminated. Afterwards, the above inspection steps are repeated until there is no abnormal data in the experimental data.
106:利用筛选后的不存在异常的被试者和数据,计算得出每幅测试图像的全部正常实验数据的平均值(即MOS值)得到最终的主观评价结果。106: Using the screened subjects and data without abnormalities, calculate the average value (ie MOS value) of all normal experimental data of each test image to obtain the final subjective evaluation result.
根据公式(17)计算每幅测试图像的全部正常实验数据的平均值(即MOS值)得到最终的主观评价结果。According to the formula (17), calculate the average value (ie, MOS value) of all normal experimental data of each test image to obtain the final subjective evaluation result.
其中,MOSi表示第i幅图像的主观评价值,K表示最终正常被试者的数量,xi,j表示第j名被试者对第i幅图像的评价分数。Among them, MOS i represents the subjective evaluation value of the i-th image, K represents the number of final normal subjects, and xi,j represents the evaluation score of the j-th subject on the i-th image.
为了验证本方法所提出的被试者数量确定算法和实验数据筛选算法,本方法对JPEG压缩失真及随机噪声、高斯噪声失真的图像进行了主观评价,下面简单介绍实验过程:In order to verify the algorithm for determining the number of subjects and the experimental data screening algorithm proposed by this method, this method subjectively evaluates images distorted by JPEG compression, random noise, and Gaussian noise. The following is a brief introduction to the experimental process:
本方法选取了48幅测试图像用于确定被试者数量的预测试,其中包括girl、family、boy、flower、tree和river六个场景,它们分别经85%、65%、45%、25%、15%、5%的JPEG压缩及添加随机噪声、高斯噪声;六幅参考立体图像左右视点图像如图1~6所示,实验数据由天津大学宽带无线通信与立体成像研究所提供。根据ITU-R BT.1438[1]标准规定,实验采用DSIS(Double-Stimulus Impairment Scale,双刺激损伤标度法),招募15名视觉功能和立体视觉均正常的被试者参加预测试,观看设备为3DWINDOWS-19A0(19英寸,1280×1024),如图8,实验室亮度为极弱,按照DSIS评分标准对失真图像的质量进行打分,每次被试者的训练和测试的总时间不能超过30分钟。This method selects 48 test images for the pre-test to determine the number of subjects, including six scenes of girl, family, boy, flower, tree and river, which are respectively tested by 85%, 65%, 45%, and 25%. , 15%, 5% JPEG compression and adding random noise and Gaussian noise; the left and right viewpoint images of six reference stereo images are shown in Figures 1 to 6, and the experimental data are provided by the Institute of Broadband Wireless Communication and Stereo Imaging of Tianjin University. According to the ITU-R BT.1438[1] standard, the experiment adopts DSIS (Double-Stimulus Impairment Scale, double-stimulus damage scale method), recruiting 15 subjects with normal visual function and stereo vision to participate in the pre-test, watching The equipment is 3DWINDOWS-19A0 (19 inches, 1280×1024), as shown in Figure 8, the brightness of the laboratory is extremely weak, and the quality of the distorted image is scored according to the DSIS scoring standard. The total time of training and testing for each subject cannot more than 30 minutes.
结果验证:Result verification:
本实施例将利用本专利介绍的被试者数量确定算法计算不同测试图像需要的被试者数量(具体结果见表1和表2),其中,设定置信度为95%,由于质量较差图像的评价分数波动性较大,因此将平均评价分数大于3的预测试图像的可接受百分偏差r设为5%,其它图像设为8%。In this embodiment, the algorithm for determining the number of subjects introduced in this patent will be used to calculate the number of subjects required for different test images (see Table 1 and Table 2 for specific results), where the confidence level is set to 95%, due to poor quality The evaluation scores of images fluctuate greatly, so the acceptable percentage deviation r is set to 5% for pre-test images with an average evaluation score greater than 3, and 8% for other images.
表1和表2分别展示了经JPEG压缩和添加噪声的测试场景的部分实验数据,经观察可知,被试者数量与场景内容、图像处理方式和图像损伤程度有关。通过被试者数量确定算法的计算,对于本专利所建立的立体图像数据库,选择30名被试者参与主观评价实验,既满足绝大多数测试图像对被试者数量的要求,保证了实验结果的准确性和可靠度,又不会因被试者数量过大导致实验资源的浪费。为进一步验证本方法的正确性以及30名被试者可以确保本实验结果的准确性,本方法增加被试者数量至35名对部分测试图像进行主观评价实验,发现实验结果与30名被试者所测结果非常接近,具体见表1和表2。Table 1 and Table 2 respectively show some experimental data of JPEG compressed and noise-added test scenes. It can be seen from observation that the number of subjects is related to scene content, image processing method and image damage degree. Through the calculation of the algorithm for determining the number of subjects, for the stereoscopic image database established in this patent, 30 subjects were selected to participate in the subjective evaluation experiment, which not only meets the requirements of most test images for the number of subjects, but also ensures the experimental results The accuracy and reliability of the experiment will not be wasted due to the large number of subjects. In order to further verify the correctness of this method and that 30 subjects can ensure the accuracy of the experimental results, this method increases the number of subjects to 35 to conduct subjective evaluation experiments on some test images, and found that the experimental results are consistent with those of 30 subjects. The measured results are very close, see Table 1 and Table 2 for details.
表1关于压缩图像的算法验证结果Table 1 Algorithm verification results on compressed images
表2关于加噪图像的算法验证结果Table 2 Algorithm verification results on noised images
通过本方法淘汰异常、可疑的被试者和实验数据,从而使实验结果更加准确、可靠。为了验证该实验数据筛选方法的正确性和有效性,本实施例将主观实验的最终评价结果MOS值与被淘汰被试者的部分评价分数进行比较,具体见表3。观察可知,被淘汰被试者的评价分数与主观实验的最终MOS值均相差1.5分以上,偏离了大多数被试者的评价分数范围,将影响主观评价结果的准确性,从而验证了本专利实验数据筛选方法的正确性。因此,本方法能够正确确定被试者数量以及筛选实验数据,保证了实验结果的可靠性,节约了实验资源,淘汰异常、可疑的实验值,从而保证实验结果的一致性。Abnormal and suspicious subjects and experimental data are eliminated by this method, so that the experimental results are more accurate and reliable. In order to verify the correctness and effectiveness of the experimental data screening method, this embodiment compares the MOS value of the final evaluation result of the subjective experiment with the partial evaluation scores of the eliminated subjects, see Table 3 for details. It can be seen from observation that the difference between the evaluation scores of the eliminated subjects and the final MOS value of the subjective experiment is more than 1.5 points, which deviates from the evaluation score range of most of the subjects and will affect the accuracy of the subjective evaluation results, thus verifying the patent The correctness of the experimental data screening method. Therefore, this method can correctly determine the number of subjects and screen experimental data, ensure the reliability of experimental results, save experimental resources, and eliminate abnormal and suspicious experimental values, thereby ensuring the consistency of experimental results.
表3关于数据筛选算法的验证结果Table 3 Verification results about the data screening algorithm
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本领域技术人员可以理解附图只是一个优选实施例的示意图,上述本发明实施例序号仅仅为了描述,不代表实施例的优劣。Those skilled in the art can understand that the accompanying drawing is only a schematic diagram of a preferred embodiment, and the serial numbers of the above-mentioned embodiments of the present invention are for description only, and do not represent the advantages and disadvantages of the embodiments.
以上所述仅为本发明的较佳实施例,并不用以限制本发明,凡在本发明的精神和原则之内,所作的任何修改、等同替换、改进等,均应包含在本发明的保护范围之内。The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection of the present invention. within range.
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