Abstract: In a method for invitation behavior prediction, group behavior feature information of a first user of a group is obtained. In addition, group relationship feature information of a second user is obtained. Further, group architecture information of the group, the group behavior feature information of the first user, and the group relationship feature information of the second user are input to an invitation prediction model, to obtain a target member user and a candidate invitation user of the target member user. The invitation prediction model is obtained by training the invitation prediction model based on a plurality of sample groups in a training set, and group relationship feature information of associated users of member users in the plurality of sample groups. Invitation prediction information is sent to the target member user to prompt the target member user to add the candidate invitation user to the group.

Abstract: The present disclosure provides a method and an apparatus for precise positioning of a scholar based on mining of the scholar's scientific research achievement. The method includes: extracting text information in the scholar's scientific research achievement P to obtain key information, and constructing structural information; mining and constructing implicit information O with a geographic directivity in the scholar's scientific research achievement P according to the key information and the structural information; performing a structural arrangement on the structural information, and acquiring a final result R; and acquiring a mapping of A?R according to the final result R and the matrix U, acquiring and outputting the positioning information of the authors in the set A.

Abstract: The present disclosure provides a method and an apparatus for precise positioning of a scholar based on mining of the scholar's scientific research achievement. The method includes: extracting text information in the scholar's scientific research achievement P to obtain key information, and constructing structural information; mining and constructing implicit information O with a geographic directivity in the scholar's scientific research achievement P according to the key information and the structural information; performing a structural arrangement on the structural information, and acquiring a final result R; and acquiring a mapping of A?R according to the final result R and the matrix U, acquiring and outputting the positioning information of the authors in the set A.

Abstract: In a method for invitation behavior prediction, group behavior feature information of a first user of a group is obtained. In addition, group relationship feature information of a second user is obtained. Further, group architecture information of the group, the group behavior feature information of the first user, and the group relationship feature information of the second user are input to an invitation prediction model, to obtain a target member user and a candidate invitation user of the target member user. The invitation prediction model is obtained by training the invitation prediction model based on a plurality of sample groups in a training set, and group relationship feature information of associated users of member users in the plurality of sample groups. Invitation prediction information is sent to the target member user to prompt the target member user to add the candidate invitation user to the group.

Abstract: 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.

Abstract: The present invention generally relates to automatic detection of video artifacts. The present invention relates to automatically detect out the images with visible artifacts due to STB decoding error based on a generated artificial edge map. In particular, the application teaches a method to detect out the images with the visible artifacts and how to identify artificial edges from texture (natural) edges. The present system identifies that artificial edges are most significant in local area, while texture edges are often together with some other texture edges.

Abstract: An image is partitioned into a foreground area, a background area, and optionally a transitional area. The partitioning may be pre-defined, or it may be based on user inputs and configuration data. The partitioning may also be refined based on an initial partitioning. Blur measures are determined respectively for the partitioned areas. A blur measure for the whole image can then be determined from a weighted average of the blur measures for the partitioned areas. The blur measure for the image can be used in a video quality monitor.

Abstract: Various implementations relate to providing a pictorial summary, also referred to as a comic book or a narrative abstraction. In one particular implementation, one or more parameters from a configuration guide are accessed. The configuration guide includes one or more parameters for configuring a pictorial summary of a video. The video is accessed. The pictorial summary for the video is generated. The pictorial summary conforms to the one or more accessed parameters from the configuration guide.

Abstract: The present invention generally relates to automatic detection of video artifacts. The present invention relates to automatically detect out the images with visible artifacts due to STB decoding error based on a generated artificial edge map. In particular, the application teaches a method to detect out the images with the visible artifacts and how to identify artificial edges from texture (natural) edges. The present system identifies that artificial edges are most significant in local area, while texture edges are often together with some other texture edges.

Abstract: Various implementations relate to providing a pictorial summary, also referred to as a comic book or a narrative abstraction. In one particular implementation, a first portion in a video is accessed, and a second portion in the video is accessed. A weight for the first portion is determined, and a weight for the second portion is determined. A first number and a second number are determined. The first number identifies how many pictures from the first portion are to be used in a pictorial summary of the video. The first number is one or more, and is determined based on the weight for the first portion. The second number identifies how many pictures from the second portion are to be used in the pictorial summary of the video. The second number is one or more, and is determined based on the weight for the second portion.

Abstract: 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.

Abstract: An image is partitioned into a foreground area, a background area, and optionally a transitional area. The partitioning may be pre-defined, or it may be based on user inputs and configuration data. The partitioning may also be refined based on an initial partitioning. Blur measures are determined respectively for the partitioned areas. A blur measure for the whole image can then be determined from a weighted average of the blur measures for the partitioned areas. The blur measure for the image can be used in a video quality monitor.

Abstract: Various implementations address distortion and quality measurements. Certain implementations relate to distortions resulting in a freeze-with-skip and/or a freeze-without-skip. In one implementation, a pause term provides an indication of distortion associated with a pause arising from not displaying one or more consecutive pictures that are not to be displayed. A motion term provides an indication of distortion associated with a related change in motion. A combined indicator is based on an addition of the pause term and the motion term. In another particular implementation, a first indicator indicates distortion across one or more types of distortive effects that results from not displaying a first set of pictures. A second indicator indicates corresponding distortion with respect to a second set of pictures. The first indicator and the second indicator are combined in a non-linear manner.

Abstract: 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.

Abstract: Various implementations address distortion and quality measurements. Certain implementations relate to distortions resulting in a freeze-with-skip and/or a freeze-without-skip. In one implementation, a pause term provides an indication of distortion associated with a pause arising from not displaying one or more consecutive pictures that are not to be displayed. A motion term provides an indication of distortion associated with a related change in motion. A combined indicator is based on an addition of the pause term and the motion term. In another particular implementation, a first indicator indicates distortion across one or more types of distortive effects that results from not displaying a first set of pictures. A second indicator indicates corresponding distortion with respect to a second set of pictures. The first indicator and the second indicator are combined in a non-linear manner.

Abstract: Methods and apparatus for artifact detection are provided by the present principles that measure the level of artifacts, such as those caused by temporal concealment of errors due to packet loss, for conditional error concealment. The principles are based on an assumption that sharp edges of video are rarely aligned with macroblock boundaries so video discontinuities are checked throughout the video. The scheme solves the problem of error propagation when temporal concealment of artifacts is used and the high false alarm rates of prior methods. Artifact detection methods are provided for regions of an image, an entire image, or for a video sequence, with error concealment provided conditionally based on the artifact levels.

Abstract: 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.

Abstract: 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.

Abstract: A method for processing an image divided into blocks of pixels is disclosed. The method comprises 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; determining, for each block, a block blockiness level on the basis of the number of pixels within the detected largest sub-block and on the basis of the identification step; and processing the image on the basis of the block blockiness levels.

Abstract: 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.