CODING VIDEO SYNTAX ELEMENTS USING A CONTEXT TREE

    公开(公告)号:WO2019013842A1

    公开(公告)日:2019-01-17

    申请号:PCT/US2018/022794

    申请日:2018-03-16

    Applicant: GOOGLE LLC

    Abstract: Video syntax elements are coded using a context tree. Context information used for coding previously-coded syntax elements is identified. A context tree is produced by separating the previously-coded syntax elements into data groups based on the context information. The context tree includes nodes representing the data groups. Separating the previously-coded syntax elements can include applying separation criteria against values of the context information to produce at least some of the nodes. Context information is then identified for another set of syntax elements to be coded. One of the nodes of the context tree is identified based on values of the context information associated with one of the other set of syntax elements. That syntax element is then coded according to a probability model associated with the identified node. The context tree can be used to encode or decode syntax elements.

    ACTIVE SUSPENSION SYSTEM
    22.
    发明申请

    公开(公告)号:WO2018208510A1

    公开(公告)日:2018-11-15

    申请号:PCT/US2018/029753

    申请日:2018-04-27

    Abstract: A suspension system (760) includes a top mount (774), a bottom mount (778), a rigid housing, an air spring (766), and a linear actuator (662). The air spring transfers force of a first load path between the top mount and the bottom mount. The air spring includes a pressurized cavity containing pressurized gas that transfers the force of the first load path. The linear actuator transfers force of a second load path between the top mount and the bottom mount in parallel to the first load path. The rigid housing defines at least part of the pressurized cavity and transfers the force of the second load path.

    IMAGE COMPRESSION BASED ON INFORMATION OF A DISTANCE TO A SENSOR

    公开(公告)号:WO2018191346A1

    公开(公告)日:2018-10-18

    申请号:PCT/US2018/027043

    申请日:2018-04-11

    Applicant: APPLE INC.

    Abstract: A method includes determining compression parameters for image portions based on a distance value for each of the image portions to a sensor such that compression rates applied by the compression parameters depend on the distance values, and encoding the image portions using the compression parameters. The range-based compression parameters cause the image portions corresponding to large distance values to be compressed using low compression rates and cause the image portions corresponding to small distance values to be compressed using high compression rates.

    TRANSFORM KERNEL SELECTION AND ENTROPY CODING

    公开(公告)号:WO2018160231A1

    公开(公告)日:2018-09-07

    申请号:PCT/US2017/059272

    申请日:2017-10-31

    Applicant: GOOGLE LLC

    Abstract: Transform kernel candidates including a vertical transform type associated with a vertical motion and a horizontal transform type associated with a horizontal motion can be encoded or decoded. During an encoding operation, a residual block of a current block is transformed according to a selected transform kernel candidate to produce a transform block. A probability model for encoding the selected transform kernel candidate is then identified based on neighbor transform blocks of the transform block. The selected transform kernel candidate is then encoded according to the probability model. During a decoding operation, the encoded transform kernel candidate is decoded using the probability model. The encoded transform block is then decoded by inverse transforming dequantized transform coefficients thereof according to the decoded transform kernel candidate.

    CLASSIFICATION AND STORAGE OF DOCUMENTS
    25.
    发明申请
    CLASSIFICATION AND STORAGE OF DOCUMENTS 审中-公开
    文件的分类和存储

    公开(公告)号:WO2016118215A1

    公开(公告)日:2016-07-28

    申请号:PCT/US2015/059535

    申请日:2015-11-06

    Applicant: GOOGLE INC.

    CPC classification number: G06F17/30598 G06F17/30011 G06F17/30707

    Abstract: A method includes defining a plurality of known document types, obtaining a collection of previously classified documents that are each associated with one of the known document types, and extracting features from each document from the collection of previously classified documents to define feature information. The method also includes obtaining a subject document that is associated with a user, extracting one or more features from the subject document, comparing the one or more features from the subject document to the feature information, associating the subject document with one of the known document types based on the comparison, and transmitting the document to a cloud storage system for storage in a dedicated storage location that is associated with the user and contains only documents of the respective known document type that is associated with the subject document.

    Abstract translation: 一种方法包括定义多个已知文档类型,获得各自与已知文档类型之一相关联的先前分类的文档的集合,以及从先前分类的文档的集合中提取来自每个文档的特征以定义特征信息。 该方法还包括获得与用户相关联的主题文档,从主题文档中提取一个或多个特征,将来自主题文档的一个或多个特征与特征信息进行比较,将主题文档与已知文档之一相关联 基于比较的类型,以及将文档发送到云存储系统,以存储在与用户相关联的专用存储位置中,并且仅包含与主题文档相关联的相应已知文档类型的文档。

    METHOD AND APPARATUS FOR VIDEO CODING USING REFERENCE MOTION VECTORS
    26.
    发明申请
    METHOD AND APPARATUS FOR VIDEO CODING USING REFERENCE MOTION VECTORS 审中-公开
    使用参考运动矢量进行视频编码的方法和装置

    公开(公告)号:WO2014058796A1

    公开(公告)日:2014-04-17

    申请号:PCT/US2013/063723

    申请日:2013-10-07

    Applicant: GOOGLE INC

    CPC classification number: H04N19/44 H04N19/51 H04N19/56

    Abstract: Techniques are described to use a reference motion vector to reduce the amount of bits needed to encode motion vectors for inter prediction. One method includes identifying a candidate motion vector used to inter predict each of a plurality of previously coded blocks to define a plurality of candidate motion vectors, identifying a set of reconstructed pixel values corresponding to a set of previously coded pixels for the current block, and generating, using each candidate motion vector, a corresponding set of predicted values for the set of previously coded pixel values within each reference frame of a plurality of reference frames. A respective error value based on a difference between the set of reconstructed pixel values and each set of predicted values is used to select a reference motion vector from the candidate motion vectors that is used to encode the motion vector for the current block.

    Abstract translation: 描述技术以使用参考运动矢量来减少编码用于帧间预测的运动矢量所需的位数量。 一种方法包括识别用于相互预测多个先前编码的块中的每一个的候选运动矢量,以定义多个候选运动矢量,识别与当前块的一组先前编码像素相对应的一组重建像素值,以及 使用每个候选运动矢量生成多个参考帧的每个参考帧内的先前编码的像素值集合的相应的一组预测值。 使用基于重构像素值集合和每组预测值之间的差的相应误差值从用于编码当前块的运动矢量的候选运动矢量中选择参考运动矢量。

    DYNAMIC PROFILE SWITCHING
    27.
    发明申请
    DYNAMIC PROFILE SWITCHING 审中-公开
    动态配置文件切换

    公开(公告)号:WO2013059514A1

    公开(公告)日:2013-04-25

    申请号:PCT/US2012/060901

    申请日:2012-10-18

    Applicant: GOOGLE INC.

    CPC classification number: G06F9/4451 G06F21/32 H04M1/72569 H04M2250/52

    Abstract: Methods and apparatuses are disclosed for dynamic switching of user profiles on computing devices. In one method, the computing device identifies a first user profile under which the computing device is operating. The first user profile is associated with a first user value indicative of a first user. The computing device receives an image from an image-sensing device, generates a current user value indicative of a current user based on the received image, and determines if the current user value corresponds to the first user value. If the current user value does not correspond to the first user value, the computing device configures at least some programs operating on the computing device using a second user profile that is selected based on the current user value. If the current user value does correspond to the first user value, the computing device continues to operate using the first user profile.

    Abstract translation: 公开了用于在计算设备上的用户简档的动态切换的方法和装置。 在一种方法中,计算设备识别计算设备在其下操作的第一用户简档。 第一用户简档与指示第一用户的第一用户值相关联。 计算装置从图像感测装置接收图像,基于接收到的图像生成表示当前用户的当前用户值,并且确定当前用户值是否对应于第一用户值。 如果当前用户值不对应于第一用户值,则计算设备使用基于当前用户值选择的第二用户简档来配置在计算设备上操作的至少一些程序。 如果当前用户值对应于第一用户值,则计算设备使用第一用户简档继续操作。

    ADAPTIVE WAVELET DENOISING
    29.
    发明申请

    公开(公告)号:WO2021236070A1

    公开(公告)日:2021-11-25

    申请号:PCT/US2020/033655

    申请日:2020-05-19

    Applicant: GOOGLE LLC

    Abstract: Image data is processed for noise reduction before encoding and subsequent decoding. For an input image in a spatial domain, two-dimensional (2-D) wavelet coefficients at multiple levels are generated. Each level includes multiple subbands, each associated with a respective subband type in a wavelet domain. For respective levels, a flat region of a subband is identified, which flat region includes blocks of the subband having a variance no higher than a first threshold variance. A flat block set for the subband type associated with the subband is identified, which includes blocks common to respective flat regions of the subband. A second threshold variance is determined using variances of the flat block set, and is then used for thresholding at least some of the 2-D wavelet coefficients to remove noise. After thresholding, a denoised image is generated in the spatial domain using the levels.

    DEBANDING USING A NOVEL BANDING METRIC
    30.
    发明申请

    公开(公告)号:WO2021236061A1

    公开(公告)日:2021-11-25

    申请号:PCT/US2020/033545

    申请日:2020-05-19

    Applicant: GOOGLE LLC

    Abstract: A method includes training a first model to measure the banding artefacts (1302), training a second model to deband the image (1304), and generating a debanded image for the image using the second model (1306). Training the first model (1302) can include selecting a first set of first training images, generating a banding edge map for a first training image, where the map includes weights that emphasize banding edges and de-emphasize true edges in the first training image, and using the map and a luminance plane of the first training image as input to the first model. Training the second model (1304) can include selecting a second set of second training images, generating a debanded training image for a second training image, generating a banding score for the debanded training image using the first model, and using the banding score in a loss function used in the training the second model..

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