Thomson Licensing

Tsinghua University

Other

Microsoft Technology Licensing, LLC

Tencent Technology (Shenzhen) Company Limited

Interdigital Ce Patent Holdings

Debing Liu

Zhibo Chen

Xiaodong Gu

Feng Xu

Jie Tang

Bo Gao

Shipeng Li

Jian Ding

Guo Bin Shen

Hao Ye

Yuwen He

Yujie Qian

Xaiodong Gu

Zhou Shao

Jiezhong Qiu

Yufei Zheng

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.

Invitation behavior prediction method and apparatus, and storage medium

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&#x2192;R according to the final result R and the matrix U, acquiring and outputting the positioning information of the authors in the set A.

Method and apparatus for precise positioning of scholar based on mining of scholar's scientific research achievement

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,&#x2033;). 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.

Video quality measurement considering multiple artifacts

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.

Method and apparatus to detect artificial edges in images

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.

Blur measurement

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.

Method of processing an image based on the determination of blockiness level

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.

Distortion/quality measurement

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.

Method and apparatus for measuring video quality using at least one semi-supervised learning regressor for mean observer score prediction

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.

Method and device for calculating blur in video images

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 (smg) a third similarity map, which provides a numerical quality value.

Method for measuring video quality using a reference, and apparatus for measuring video quality using a reference

A method for processing an image divided into blocks of pixels is disclosed. The method comprises the steps of:

Method of processing an image and corresponding device

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.

Method and apparatus for measuring video quality

Systems and methods are presented for accelerated image rendering. In one implementation, the systems and methods receive digital image data and derive intermediate data associated with the digital image data to accelerate image rendering.

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Beijing, China

Debing Liu