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1. 发明授权

US08983223B2 Low-complexity bilateral filter (BF) implementation in a data processing device 有权
标题翻译：在数据处理设备中实现低复杂度双边滤波（BF）
公开(公告)号：US08983223B2
公开(公告)日：2015-03-17
申请号：US13948210
申请日：2013-07-23
申请人： NVIDIA Corporation
发明人： Niranjan Avadhanam , Prashant Sohani
IPC分类号： G06K9/40 , G06T5/00
CPC分类号： G06T5/002 , G06T5/003 , G06T5/20 , G06T2207/20028 , G06T2207/20192
摘要： A method includes implementing, through a processor communicatively coupled to a memory and/or a hardware block, a Bilateral Filter (BF) including a spatial filter component and a range filter component, and implementing the spatial filter component with a low-complexity function to allow for focus on the range filter component. The method also includes determining, through the processor, filter tap value(s) related to the range filter component as a function of radiometric distance between a pixel of a video frame and/or an image and other pixels thereof based on a pre-computed corpus of data related to execution of an application in accordance with a filtering requirement of the pixel by the application. Further, the method includes constraining, through the processor, the filter tap value(s) to a form i×base based on the BF implementation. i is an integer and base is a floating point base.
摘要翻译：一种方法包括通过通信地耦合到存储器和/或硬件块的处理器实现包括空间滤波器组件和范围滤波器组件的双边滤波器（BF），并且实现具有低复杂度功能的空间滤波器组件允许对范围过滤器组件进行对焦。该方法还包括基于预先计算的方法，通过处理器确定与范围滤波器分量相关的滤波器抽头值作为视频帧的像素和/或图像的其他像素之间的辐射距离的函数根据应用程序对像素的过滤要求与应用执行相关的数据语料库。此外，该方法包括基于BF实现，通过处理器将滤波器抽头值限制为形式i×base。我是一个整数，基数是一个浮点数。

2. 发明申请

US20150030257A1 LOW-COMPLEXITY BILATERAL FILTER (BF) IMPLEMENTATION IN A DATA PROCESSING DEVICE 有权
标题翻译：数据处理设备中的低复杂双通道滤波器（BF）实现
公开(公告)号：US20150030257A1
公开(公告)日：2015-01-29
申请号：US13948210
申请日：2013-07-23
申请人： NVIDIA Corporation
发明人： Niranjan Avadhanam , Prashant Sohani
IPC分类号： G06T5/00
CPC分类号： G06T5/002 , G06T5/003 , G06T5/20 , G06T2207/20028 , G06T2207/20192
摘要： A method includes implementing, through a processor communicatively coupled to a memory and/or a hardware block, a Bilateral Filter (BF) including a spatial filter component and a range filter component, and implementing the spatial filter component with a low-complexity function to allow for focus on the range filter component. The method also includes determining, through the processor, filter tap value(s) related to the range filter component as a function of radiometric distance between a pixel of a video frame and/or an image and other pixels thereof based on a pre-computed corpus of data related to execution of an application in accordance with a filtering requirement of the pixel by the application. Further, the method includes constraining, through the processor, the filter tap value(s) to a form i×base based on the BF implementation. i is an integer and base is a floating point base.
摘要翻译：一种方法包括通过通信地耦合到存储器和/或硬件块的处理器实现包括空间滤波器组件和范围滤波器组件的双边滤波器（BF），并且实现具有低复杂度功能的空间滤波器组件允许对范围过滤器组件进行对焦。该方法还包括基于预先计算的方法，通过处理器确定与范围滤波器分量相关的滤波器抽头值作为视频帧的像素和/或图像的其他像素之间的辐射距离的函数根据应用程序对像素的过滤要求与应用执行相关的数据语料库。此外，该方法包括基于BF实现，通过处理器将滤波器抽头值限制为形式i×base。我是一个整数，基数是一个浮点数。

3. 发明申请

US20230048189A1 MACHINE LEARNING OF ENCODING PARAMETERS FOR A NETWORK USING A VIDEO ENCODER 有权
公开(公告)号：US20230048189A1
公开(公告)日：2023-02-16
申请号：US17402953
申请日：2021-08-16
申请人： NVIDIA Corporation
发明人： Ravi kumar Boddeti , Vinayak Pore , Hassane Samir Azar , Prashant Sohani
IPC分类号： H04N19/166 , G06N20/00 , H04N19/65 , H04N19/127 , H04N19/142
摘要： In various examples, machine learning of encoding parameter values for a network is performed using a video encoder. Feedback associated with streaming video encoded by a video encoder over a network may be applied to an MLM(s). Using such feedback, the MLM(s) may predict a value(s) of an encoding parameter(s). The video encoder may then use the value to encode subsequent video data for the streaming. By using the video encoder in training, the MLM(s) may learn based on actual encoded parameter values of the video encoder. The MLM(s) may be trained via reinforcement learning based on video encoded by the video encoder. A rewards metric(s) may be used to train the MLM(s) using data generated or applied to the physical network in which the MLM(s) is to be deployed and/or a simulation thereof. Penalty metric(s) (e.g., the quantity of dropped frames) may also be used to train the MLM(s).

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