公开(公告)号：US20120268652A1

公开(公告)日：2012-10-25

申请号：US13500921

申请日：2009-10-10

申请人： Debing Liu ,  Zhibo Chen ,  Xaiodong Gu ,  Xu Feng

发明人： Debing Liu ,  Zhibo Chen ,  Xaiodong Gu ,  Xu Feng

IPC分类号： H04N7/01

CPC分类号： G06T7/0002 ,  G06T2207/10016 ,  G06T2207/20004 ,  G06T2207/20021 ,  G06T2207/30168 ,  H04N7/012 ,  H04N19/117 ,  H04N19/14 ,  H04N19/162 ,  H04N19/85

摘要： Blur is one of the most important features related to image quality. Accurately estimating the blur level of an image is a great help to accurately evaluate its quality. A method for calculating blur of video images comprises calculating a horizontal blur value, and determining characteristic parameters of the video images, including at last progressive/interlaced and spatial resolution level. If the video images are progressive, also a vertical blur value is calculated, and combined with the horizontal blur value. The next step is normalizing the horizontal blur value, or the combined horizontal and vertical blur value, according to predefined min and max values, wherein the min and max values are adapted to the spatial resolution level.

摘要翻译： 模糊是与图像质量相关的最重要的功能之一。 准确估计图像的模糊水平是准确评估其质量的极大帮助。 一种用于计算视频图像模糊的方法包括计算水平模糊值，以及确定视频图像的特征参数，包括最后渐进/隔行扫描和空间分辨率级别。 如果视频图像是渐进的，则还计算垂直模糊值，并与水平模糊值组合。 下一步是根据预定义的最小值和最大值归一化水平模糊值或组合的水平和垂直模糊值，其中最小值和最大值适应于空间分辨率水平。

公开(公告)号：US08823873B2

公开(公告)日：2014-09-02

申请号：US13500921

申请日：2009-10-10

申请人： Debing Liu ,  Zhibo Chen ,  Xaiodong Gu ,  Feng Xu

发明人： Debing Liu ,  Zhibo Chen ,  Xaiodong Gu ,  Feng Xu

IPC分类号： H04N7/01

CPC分类号： G06T7/0002 ,  G06T2207/10016 ,  G06T2207/20004 ,  G06T2207/20021 ,  G06T2207/30168 ,  H04N7/012 ,  H04N19/117 ,  H04N19/14 ,  H04N19/162 ,  H04N19/85

摘要： Blur is one of the most important features related to image quality. Accurately estimating the blur level of an image is a great help to accurately evaluate its quality. A method for calculating blur of video images comprises calculating a horizontal blur value, and determining characteristic parameters of the video images, including at last progressive/interlaced and spatial resolution level. If the video images are progressive, also a vertical blur value is calculated, and combined with the horizontal blur value. The next step is normalizing the horizontal blur value, or the combined horizontal and vertical blur value, according to predefined min and max values, wherein the min and max values are adapted to the spatial resolution level.

摘要翻译： 模糊是与图像质量相关的最重要的功能之一。 准确估计图像的模糊水平是准确评估其质量的极大帮助。 一种用于计算视频图像模糊的方法包括计算水平模糊值，以及确定视频图像的特征参数，包括最后渐进/隔行扫描和空间分辨率级别。 如果视频图像是渐进的，则还计算垂直模糊值，并与水平模糊值组合。 下一步是根据预定义的最小值和最大值归一化水平模糊值或组合的水平和垂直模糊值，其中最小值和最大值适应于空间分辨率水平。

公开(公告)号：US09076220B2

公开(公告)日：2015-07-07

申请号：US13642622

申请日：2010-04-29

申请人： Debing Liu ,  Zhibo Chen ,  Xiaodong Gu ,  Feng Xu

发明人： Debing Liu ,  Zhibo Chen ,  Xiaodong Gu ,  Feng Xu

IPC分类号： G06K9/34 ,  G06T7/00 ,  G06T7/40 ,  H04N19/159 ,  H04N19/176 ,  H04N19/117 ,  H04N19/86

CPC分类号： G06T7/0081 ,  G06T7/0002 ,  G06T7/11 ,  G06T7/181 ,  G06T7/41 ,  G06T2207/20021 ,  G06T2207/30168 ,  H04N19/117 ,  H04N19/159 ,  H04N19/176 ,  H04N19/86 ,  H04N19/865

摘要： A method for processing an image divided into blocks of pixels is described. The method includes detecting a largest sub-block whose pixels have an equal luminance value and identifying if the detected largest sub-block is or is not a natural texture. Thereafter, a weighted luminance difference is calculated between the detected largest sub-block and neighboring pixels and a block blockiness level is determined on the basis of the number of pixels within the detected largest sub-block, of the identification step and of the weighted luminance difference. The image is then processed on the basis of the block blockiness level.

摘要翻译： 描述用于处理被划分成像素块的图像的方法。 该方法包括检测其像素具有相等亮度值的最大子块，并且识别检测到的最大子块是否是自然纹理。 此后，在检测到的最大子块和相邻像素之间计算加权亮度差，并且基于检测到的最大子块中的像素数，确定步骤的加权亮度和 区别。 然后基于块块化级别处理图像。

公开(公告)号：US08824783B2

公开(公告)日：2014-09-02

申请号：US13695060

申请日：2010-04-30

申请人： Feng Xu ,  Zhibo Chen ,  Debing Liu ,  Xiaodong Gu

发明人： Feng Xu ,  Zhibo Chen ,  Debing Liu ,  Xiaodong Gu

IPC分类号： G06K9/62 ,  G06K9/46 ,  H04N17/00 ,  G06T7/00

CPC分类号： H04N17/004 ,  G06N99/005 ,  G06T7/0002 ,  G06T2207/10016 ,  G06T2207/20081 ,  G06T2207/30168

摘要： The invention is made in the technical field of video quality measurement. More precisely, the invention is related to mean observer score prediction using a trained semi-supervised learning regressor. That is, a method and apparatus for measuring video quality using a semi-supervised learning system for mean observer score prediction is proposed. Said semi-supervised learning system comprises at least one semi-supervised learning regressor and said method comprises training the learning system and retraining the trained learning system using a selection of test data wherein the test data is used for determining at least one mean observer score prediction using the trained learning system and the selection is indicated by a feedback received through a user interface upon presenting, in the user interface, said at least one mean observer score prediction. Doing so, prediction quality can be improved after re-training at least for the selection.

摘要翻译： 本发明是在视频质量测量技术领域进行的。 更准确地说，本发明涉及使用训练有素的半监督学习回归器的平均观察者评分预测。 也就是说，提出了一种使用半监督学习系统测量视频质量的方法和装置，用于平均观察员评分预测。 所述半监督学习系统包括至少一个半监督学习回归器，并且所述方法包括训练学习系统并且使用测试数据的选择重新训练训练学习系统，其中测试数据用于确定至少一个平均观察者评分预测 使用训练的学习系统和选择由在用户界面中呈现所述至少一个平均观察者分数预测时通过用户界面接收的反馈来指示。 做到这一点，至少在选择后再进行训练后，可以提高预测质量。

公开(公告)号：US08723960B2

公开(公告)日：2014-05-13

申请号：US13807875

申请日：2010-07-02

申请人： Xiaodong Gu ,  Debing Liu ,  Feng Xu ,  Zhibo Chen

发明人： Xiaodong Gu ,  Debing Liu ,  Feng Xu ,  Zhibo Chen

IPC分类号： H04N17/00

CPC分类号： H04N5/14 ,  G06K9/00758 ,  G06K9/6212 ,  H04N17/004

摘要： The purpose of an objective video quality evaluation is to automatically assess the quality of video sequences in agreement with human quality perception. The invention addresses the effects of the introduction of a temporal dimension by focusing on the temporal evolutions of spatial distortions, since it has been found that a spatial quality variation cannot be evaluated by simple subtraction of the spatial quality of neighboring frames. An improved method for estimating perceived video quality comprises steps of calculating a first similarity map between adjacent frames of a current sequence, calculating a second similarity map between the corresponding reference frames, and calculating (smg3) a third similarity map, which provides a numerical quality value.

摘要翻译： 客观视频质量评估的目的是自动评估视频序列的质量，符合人的素质感知。 本发明通过关注空间失真的时间演变来解决引入时间维度的效果，因为已经发现空间质量变化不能通过简单地减去相邻帧的空间质量来评估。 一种用于估计感知视频质量的改进方法包括以下步骤：计算当前序列的相邻帧之间的第一相似度图，计算相应参考帧之间的第二相似度映射，以及计算（smg3）第三相似度图，其提供数值质量 值。

公开(公告)号：US09924167B2

公开(公告)日：2018-03-20

申请号：US14358114

申请日：2011-11-28

申请人： Xiaodong Gu ,  Debing Liu ,  Zhibo Chen

发明人： Xiaodong Gu ,  Debing Liu ,  Zhibo Chen

IPC分类号： H04N19/103 ,  H04N19/154 ,  H04N19/192 ,  H04N19/48 ,  H04N19/89 ,  H04N17/00

CPC分类号： H04N19/192 ,  H04N17/004 ,  H04N19/103 ,  H04N19/154 ,  H04N19/48 ,  H04N19/89

摘要： A particular implementation determines objective distortion levels (d,) respectively for a plurality of artifact types. The objective distortion levels are aligned to ensure that the same distortion level of different types of artifacts corresponds to the same perceived distortion level. The aligned distortion levels (d,′) are sorted to obtain sorted distortion levels (d,″). The sorted distortion levels are then pooled together into an overall distortion level or an overall quality metric. The sorted distortion levels may be pooled using a weighted sum, wherein the weight is larger when the sorted distortion level is greater.

公开(公告)号：US08675075B2

公开(公告)日：2014-03-18

申请号：US13811657

申请日：2010-07-30

申请人： Xiaodong Gu ,  Debing Liu ,  Zhibo Chen

发明人： Xiaodong Gu ,  Debing Liu ,  Zhibo Chen

IPC分类号： H04N17/00 ,  H04N5/21

CPC分类号： H04N17/00 ,  H04N5/21 ,  H04N17/002 ,  H04N19/139 ,  H04N19/14 ,  H04N19/172 ,  H04N19/89

摘要： A method and apparatus for measuring the quality of a video sequence, which includes a plurality of frames, among which one or more consecutive frames are lost, wherein during the displaying of the video sequence, the one or more lost frames are substituted by an immediate preceding frame in the video sequence during a period from the displaying of the immediate preceding frame to that of an immediate subsequent frame of the one or more lost frames, the method includes measuring the quality of the video sequence as a function of a first parameter (DF) relating to the stability of the immediate preceding frame during the period, a second parameter (DD) relating to the continuity between the immediate preceding frame and the immediate subsequent frame, and a third parameter (DP) relating to the coherent 20 motions of the video sequence.

摘要翻译： 一种用于测量包括多个帧的视频序列的质量的方法和装置，其中一个或多个连续帧丢失，其中在显示视频序列期间，一个或多个丢失帧被立即替换 在从直接前一帧的显示到所述一个或多个丢失帧的即时后续帧的时间段期间，视频序列中的前一帧的视频序列的前一帧，该方法包括测量作为第一参数的函数的视频序列的质量 DF），与第一参数（DP）有关，该第二参数（DD）涉及在紧接着的前一帧与即时后续帧之间的连续性的第二参数（DD）以及与第二参数 视频序列。

公开(公告)号：US20130251045A1

公开(公告)日：2013-09-26

申请号：US13991664

申请日：2010-12-10

申请人： Xiaodong Gu ,  Debing Liu ,  Zhibo Chen

发明人： Xiaodong Gu ,  Debing Liu ,  Zhibo Chen

IPC分类号： H04N7/36

CPC分类号： H04N19/61 ,  H04N19/521 ,  H04N19/57

摘要： A method for determining a motion vector for a current video frame block comprises determining the motion vector using full search. Then, a number of further motion vectors is counted which is the number of motion vectors of neighbouring blocks which are similar to each other and the motion vector. Then it is ascertained that the number meets or exceeds a threshold and that the motion vector is not similar to at least one of the counted further motion vectors. A search region is determined using counted motion vectors and searched for a local best match of the current block. The motion vector is changed towards referencing the local best match. The search region only comprises candidates referenced by motion vector candidates similar to a yet further motion vector pointing to a centre of the further search region. Then, the motion vector resembles the motion presumed by the HVS.

摘要翻译： 用于确定当前视频帧块的运动矢量的方法包括使用全搜索来确定运动矢量。 然后，计数多个其他运动矢量，它们是彼此相似的相邻块的运动矢量的数量和运动矢量。 然后确定该数量满足或超过阈值，并且运动矢量与所计数的进一步的运动矢量中的至少一个不相似。 使用计数运动矢量确定搜索区域，并搜索当前块的局部最佳匹配。 运动矢量改变为参考局部最佳匹配。 搜索区域仅包括由与指向另外的搜索区域的中心的又一运动矢量类似的运动矢量候选引用的候选者。 然后，运动矢量类似于由HVS假设的运动。

公开(公告)号：US20130100350A1

公开(公告)日：2013-04-25

申请号：US13807875

申请日：2010-07-02

申请人： Xiaodong Gu ,  Debing Liu ,  Feng Xu ,  Zhibo Chen

发明人： Xiaodong Gu ,  Debing Liu ,  Feng Xu ,  Zhibo Chen

IPC分类号： H04N5/14

CPC分类号： H04N5/14 ,  G06K9/00758 ,  G06K9/6212 ,  H04N17/004

摘要： The purpose of an objective video quality evaluation is to automatically assess the quality of video sequences in agreement with human quality perception. The invention addresses the effects of the introduction of a temporal dimension by focusing on the temporal evolutions of spatial distortions, since it has been found that a spatial quality variation cannot be evaluated by simple subtraction of the spatial quality of neighbouring frames. An improved method for estimating perceived video quality comprises steps of calculating a first similarity map between adjacent frames of a current sequence, calculating a second similarity map between the corresponding reference frames, and calculating (smg3) a third similarity map, which provides a numerical quality value.

摘要翻译： 客观视频质量评估的目的是自动评估视频序列的质量，符合人的素质感知。 本发明通过关注空间失真的时间演变来解决引入时间维度的效果，因为已经发现空间质量变化不能通过简单地减去相邻帧的空间质量来评估。 一种用于估计感知视频质量的改进方法包括以下步骤：计算当前序列的相邻帧之间的第一相似度图，计算相应参考帧之间的第二相似度映射，以及计算（smg3）第三相似度图，其提供数值质量 值。

公开(公告)号：US20130039420A1

公开(公告)日：2013-02-14

申请号：US13642622

申请日：2010-04-29

申请人： Debing Liu ,  Zhibo Chen ,  Xiaodong Gu ,  Feng Xu

发明人： Debing Liu ,  Zhibo Chen ,  Xiaodong Gu ,  Feng Xu

IPC分类号： H04N7/32

CPC分类号： G06T7/0081 ,  G06T7/0002 ,  G06T7/11 ,  G06T7/181 ,  G06T7/41 ,  G06T2207/20021 ,  G06T2207/30168 ,  H04N19/117 ,  H04N19/159 ,  H04N19/176 ,  H04N19/86 ,  H04N19/865

摘要： The invention relates to a method for processing an image divided into blocks of pixels comprising the steps of: detecting, for each block, a largest sub-block whose pixels have an equal luminance value; identifying, for each block, if the detected largest sub-block is or is not a natural texture; calculating, for each block, a weighted luminance difference between the detected largest sub-block and neighboring pixels; determining, for each block, a block blockiness level on the basis of the number of pixels within the detected largest sub-block, of said identification step and of said weighted luminance difference; and processing the image on the basis of the block blockiness levels.

摘要翻译： 本发明涉及一种用于处理被划分成像素块的图像的方法，包括以下步骤：针对每个块检测其像素具有相等亮度值的最大子块; 识别每个块，如果检测到的最大子块是或不是自然纹理; 针对每个块计算所检测的最大子块和相邻像素之间的加权亮度差; 基于检测到的最大子块中的像素数，所述识别步骤和所述加权亮度差来确定每个块的块阻塞级别; 并且基于块阻塞级别处理图像。