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1. 发明授权

US11350105B2 Selection of video quality metrics and models to optimize bitrate savings in video encoding applications 有权
公开(公告)号：US11350105B2
公开(公告)日：2022-05-31
申请号：US17144924
申请日：2021-01-08
申请人： Euclid Discoveries, LLC
发明人： Dane P. Kottke , Katherine H. Cornog , John J. Guo , Myo Tun , Jeyun Lee , Nigel Lee
IPC分类号： H04N19/146 , H04N19/124 , H04N19/172 , H04N19/50 , H04N19/192 , H04N19/179 , H04N19/115 , H04N19/154 , G06T7/00
摘要： Videos may be characterized by objective metrics that quantify video quality. Embodiments are directed to target bitrate prediction methods in which one or more objective metrics may serve as inputs into a model that predicts a mean opinion score (MOS), a measure of perceptual quality, as a function of metric values. The model may be derived by generating training data through conducting subjective tests on a set of video encodings, obtaining MOS data from the subjective tests, and correlating the MOS data with metric measurements on the training data. The MOS predictions may be extended to predict the target (encoding) bitrate that achieves a desired MOS value. The target bitrate prediction methods may be applied to segments of a video. The methods may be made computationally faster by applying temporal subsampling. The methods may also be extended for adaptive bitrate (ABR) applications by applying scaling factors to predicted bitrates at one frame size to determine predicted bitrates at different frame sizes. A dynamic scaling algorithm may be used to determine predicted bitrates at different frame sizes.

2. 发明授权

US10091507B2 Perceptual optimization for model-based video encoding 有权
公开(公告)号：US10091507B2
公开(公告)日：2018-10-02
申请号：US14845067
申请日：2015-09-03
申请人： Euclid Discoveries, LLC
发明人： Nigel Lee , Sangseok Park , Myo Tun , Dane P. Kottke , Jeyun Lee , Christopher Weed
IPC分类号： H04N19/124 , H04N19/139 , H04N19/176 , H04N19/14 , H04N19/527
摘要： Perceptual statistics may be used to compute importance maps that indicate which regions of a video frame are important to the human visual system. Importance maps may be applied to the video encoding process to enhance the quality of encoded bitstreams. The temporal contrast sensitivity function (TCSF) may be computed from the encoder's motion vectors. Motion vector quality metrics may be used to construct a true motion vector map (TMVM) that can be used to refine the TCSF. Spatial complexity maps (SCMs) can be calculated from metrics such as block variance, block luminance, SSIM, and edge strength, and the SCMs can be combined with the TCSF to obtain a unified importance map. Importance maps may be used to improve encoding by modifying the criterion for selecting optimum encoding solutions or by modifying the quantization for each target block to be encoded.

3. 发明申请

US20130114703A1 Context Based Video Encoding and Decoding 审中-公开
标题翻译：基于上下文的视频编码和解码
公开(公告)号：US20130114703A1
公开(公告)日：2013-05-09
申请号：US13725980
申请日：2012-12-21
申请人： Euclid Discoveries, LLC
发明人： Darin DeForest , Nigel Lee , Renato Pizzorni , Charles P. Pace
IPC分类号： H04N7/26 , H04N7/32
CPC分类号： H04N19/20 , H04N19/503
摘要： A model-based compression codec applies higher-level modeling to produce better predictions than can be found through conventional block-based motion estimation and compensation. Computer-vision-based feature and object detection algorithms identify regions of interest throughout the video datacube. The detected features and objects are modeled with a compact set of parameters, and similar feature/object instances are associated across frames. Associated features/objects are formed into tracks and related to specific blocks of video data to be encoded. The tracking information is used to produce model-based predictions for those blocks of data, enabling more efficient navigation of the prediction search space than is typically achievable through conventional motion estimation methods. A hybrid framework enables modeling of data at multiple fidelities and selects the appropriate level of modeling for each portion of video data.
摘要翻译：基于模型的压缩编解码器应用较高级别的建模以产生比通过传统的基于块的运动估计和补偿可以发现的更好的预测。基于计算机视觉的特征和对象检测算法识别整个视频数据库中的感兴趣区域。检测到的特征和对象用一组紧凑的参数建模，并且相似的特征/对象实例在帧之间相关联。相关特征/对象被形成轨道并与要编码的视频数据的特定块有关。跟踪信息用于为这些数据块产生基于模型的预测，使得能够比通常通过常规运动估计方法可实现的预测搜索空间更有效地导航。混合框架可以对多个保真度的数据进行建模，并为视频数据的每个部分选择适当的建模级别。

4. 发明申请

US20210203951A1 Selection of Video Quality Metrics and Models to Optimize Bitrate Savings in Video Encoding Applications 有权
公开(公告)号：US20210203951A1
公开(公告)日：2021-07-01
申请号：US17144924
申请日：2021-01-08
申请人： Euclid Discoveries, LLC
发明人： Dane P. Kottke , Katherine H. Cornog , John J. Guo , Myo Tun , Jeyun Lee , Nigel Lee
IPC分类号： H04N19/146 , H04N19/124 , H04N19/172 , H04N19/50 , G06T7/00 , H04N19/192 , H04N19/179 , H04N19/115 , H04N19/154
摘要： Videos may be characterized by objective metrics that quantify video quality. Embodiments are directed to target bitrate prediction methods in which one or more objective metrics may serve as inputs into a model that predicts a mean opinion score (MOS), a measure of perceptual quality, as a function of metric values. The model may be derived by generating training data through conducting subjective tests on a set of video encodings, obtaining MOS data from the subjective tests, and correlating the MOS data with metric measurements on the training data. The MOS predictions may be extended to predict the target (encoding) bitrate that achieves a desired MOS value. The target bitrate prediction methods may be applied to segments of a video. The methods may be made computationally faster by applying temporal subsampling. The methods may also be extended for adaptive bitrate (ABR) applications by applying scaling factors to predicted bitrates at one frame size to determine predicted bitrates at different frame sizes. A dynamic scaling algorithm may be used to determine predicted bitrates at different frame sizes.

5. 发明申请

US20150124874A1 VIDEO COMPRESSION REPOSITORY AND MODEL REUSE 有权
标题翻译：视频压缩报告和模式重用
公开(公告)号：US20150124874A1
公开(公告)日：2015-05-07
申请号：US14527477
申请日：2014-10-29
申请人： EUCLID DISCOVERIES, LLC
发明人： Charles P. Pace , Darin DeForest , Nigel Lee , Renato Pizzorni , Richard Wingard
IPC分类号： H04N19/17 , H04N19/136
CPC分类号： H04N19/54 , H04N19/167
摘要： Systems and methods of improving video encoding/decoding efficiency may be provided. A feature-based processing stream is applied to video data having a series of video frames. Computer-vision-based feature and object detection algorithms identify regions of interest throughout the video datacube. The detected features and objects are modeled with a compact set of parameters, and similar feature/object instances are associated across frames. Associated features/objects are formed into tracks, and each track is given a representative, characteristic feature. Similar characteristic features are clustered and then stored in a model library, for reuse in the compression of other videos. A model-based compression framework makes use of the preserved model data by detecting features in a new video to be encoded, relating those features to specific blocks of data, and accessing similar model information from the model library. The formation of model libraries can be specialized to include personal, “smart” model libraries, differential libraries, and predictive libraries. Predictive model libraries can be modified to handle a variety of demand scenarios.
摘要翻译：可以提供改善视频编码/解码效率的系统和方法。基于特征的处理流被应用于具有一系列视频帧的视频数据。基于计算机视觉的特征和对象检测算法识别整个视频数据库中的感兴趣区域。检测到的特征和对象用一组紧凑的参数建模，并且相似的特征/对象实例在帧之间相关联。相关特征/对象被形成轨道，并且每个轨道被赋予代表性的特征。类似的特征特征被聚类，然后存储在模型库中，以便在压缩其他视频中重用。基于模型的压缩框架通过检测要编码的新视频中的特征来使用保留的模型数据，将这些特征与特定的数据块相关联，以及从模型库访问相似的模型信息。模型库的形成可以专门包括个人，“智能”模型库，差异库和预测库。可以修改预测模型库来处理各种需求情况。

6. 发明申请

US20170070745A1 Perceptual Optimization for Model-Based Video Encoding 审中-公开
标题翻译：基于模型的视频编码的感知优化
公开(公告)号：US20170070745A1
公开(公告)日：2017-03-09
申请号：US15356142
申请日：2016-11-18
申请人： Euclid Discoveries, LLC
发明人： Nigel Lee , Sangseok Park , Myo Tun , Dane P. Kottke , Jeyun Lee , Christopher Weed
IPC分类号： H04N19/513 , H04N19/124 , H04N19/147 , H04N19/61 , H04N19/117 , H04N19/14 , H04N19/159 , H04N19/13
CPC分类号： H04N19/513 , H04N19/117 , H04N19/124 , H04N19/13 , H04N19/139 , H04N19/14 , H04N19/147 , H04N19/159 , H04N19/167 , H04N19/172 , H04N19/176 , H04N19/182 , H04N19/184 , H04N19/527 , H04N19/56 , H04N19/567 , H04N19/61
摘要： Perceptual statistics are used to compute importance maps that indicate which regions of a video frame are important to the human visual system. Importance maps may be generated from encoders that produce motion vectors and employ motion estimation for inter-prediction. The temporal contrast sensitivity function (TCSF) may be computed from the encoder's motion vectors. Quality metrics may be used to construct a true motion vector map (TMVM), which refines the TCSF. Spatial complexity maps (SCMs) can be calculated from simple metrics (e.g. block variance, block luminance, SSIM, and edge detection). Importance maps with TCSF, TMVM, and SCM may be used to modify the standard rate-distortion optimization criterion for selecting the optimum encoding solution. Importance maps may modify encoder quantization. The spatial information for the importance maps may be provided by a lookup table based on block variance, where negative and positive spatial QP offsets for block variances are provided.
摘要翻译：感知统计用于计算重要性图，其指示视频帧的哪些区域对于人类视觉系统是重要的。可以从产生运动矢量的编码器产生重要性图，并且对帧间预测采用运动估计。可以从编码器的运动矢量计算时间对比度灵敏度函数（TCSF）。可以使用质量度量来构建真正的运动矢量图（TMVM），其精简TCSF。可以从简单度量（例如块方差，块亮度，SSIM和边缘检测）计算空间复杂度映射（SCM）。使用TCSF，TMVM和SCM的重要性映射可用于修改用于选择最佳编码解决方案的标准速率失真优化标准。重要性地图可能会修改编码器量化。重要性图的空间信息可以由基于块方差的查找表提供，其中提供了用于块方差的负和正空间QP偏移。

7. 发明授权

US09532069B2 Video compression repository and model reuse 有权
标题翻译：视频压缩库和模型重用
公开(公告)号：US09532069B2
公开(公告)日：2016-12-27
申请号：US14527477
申请日：2014-10-29
申请人： EUCLID DISCOVERIES, LLC
发明人： Charles P. Pace , Darin DeForest , Nigel Lee , Renato Pizzorni , Richard Wingard
IPC分类号： H04N19/54 , H04N19/167
CPC分类号： H04N19/54 , H04N19/167
摘要： Systems and methods of improving video encoding/decoding efficiency may be provided. A feature-based processing stream is applied to video data having a series of video frames. Computer-vision-based feature and object detection algorithms identify regions of interest throughout the video datacube. The detected features and objects are modeled with a compact set of parameters, and similar feature/object instances are associated across frames. Associated features/objects are formed into tracks, and each track is given a representative, characteristic feature. Similar characteristic features are clustered and then stored in a model library, for reuse in the compression of other videos. A model-based compression framework makes use of the preserved model data by detecting features in a new video to be encoded, relating those features to specific blocks of data, and accessing similar model information from the model library. The formation of model libraries can be specialized to include personal, “smart” model libraries, differential libraries, and predictive libraries. Predictive model libraries can be modified to handle a variety of demand scenarios.
摘要翻译：可以提供改善视频编码/解码效率的系统和方法。基于特征的处理流被应用于具有一系列视频帧的视频数据。基于计算机视觉的特征和对象检测算法识别整个视频数据库中的感兴趣区域。检测到的特征和对象用一组紧凑的参数建模，并且相似的特征/对象实例在帧之间相关联。相关特征/对象被形成轨道，并且每个轨道被给予代表性的特征。类似的特征特征被聚类，然后存储在模型库中，以便在压缩其他视频中重用。基于模型的压缩框架通过检测要编码的新视频中的特征来使用保留的模型数据，将这些特征与特定的数据块相关联，以及从模型库访问相似的模型信息。模型库的形成可以专门包括个人，“智能”模型库，差异库和预测库。可以修改预测模型库来处理各种需求情况。

8. 发明授权

US08902971B2 Video compression repository and model reuse 有权
标题翻译：视频压缩库和模型重用
公开(公告)号：US08902971B2
公开(公告)日：2014-12-02
申请号：US13772230
申请日：2013-02-20
申请人： Euclid Discoveries, LLC
发明人： Charles P. Pace , Darin DeForest , Nigel Lee , Renato Pizzorni , Richard Wingard
IPC分类号： H04N7/26 , H04N19/119 , G06T9/00
CPC分类号： H04N19/149 , G06T9/001
摘要： Systems and methods of improving video encoding/decoding efficiency may be provided. A feature-based processing stream is applied to video data having a series of video frames. Computer-vision-based feature and object detection algorithms identify regions of interest throughout the video datacube. The detected features and objects are modeled with a compact set of parameters, and similar feature/object instances are associated across frames. Associated features/objects are formed into tracks, and each track is given a representative, characteristic feature. Similar characteristic features are clustered and then stored in a model library, for reuse in the compression of other videos. A model-based compression framework makes use of the preserved model data by detecting features in a new video to be encoded, relating those features to specific blocks of data, and accessing similar model information from the model library. The formation of model libraries can be specialized to include personal, “smart” model libraries, differential libraries, and predictive libraries. Predictive model libraries can be modified to handle a variety of demand scenarios.
摘要翻译：可以提供改善视频编码/解码效率的系统和方法。基于特征的处理流被应用于具有一系列视频帧的视频数据。基于计算机视觉的特征和对象检测算法识别整个视频数据库中的感兴趣区域。检测到的特征和对象用一组紧凑的参数建模，并且相似的特征/对象实例在帧之间相关联。相关特征/对象被形成轨道，并且每个轨道被给予代表性的特征。类似的特征特征被聚类，然后存储在模型库中，以便在压缩其他视频中重用。基于模型的压缩框架通过检测要编码的新视频中的特征来使用保留的模型数据，将这些特征与特定的数据块相关联，以及从模型库访问相似的模型信息。模型库的形成可以专门包括个人，“智能”模型库，差异库和预测库。可以修改预测模型库来处理各种需求情况。

9. 发明申请

US20130230099A1 STANDARDS-COMPLIANT MODEL-BASED VIDEO ENCODING AND DECODING 有权
标题翻译：基于标准的基于模型的视频编码和解码
公开(公告)号：US20130230099A1
公开(公告)日：2013-09-05
申请号：US13797644
申请日：2013-03-12
申请人： EUCLID DISCOVERIES, LLC
发明人： Darin DeForest , Charles P. Pace , Nigel Lee , Renato Pizzorni
IPC分类号： H04N7/26
CPC分类号： H04N19/50 , H04N19/23 , H04N19/51 , H04N19/543 , H04N19/85
摘要： A model-based compression codec applies higher-level modeling to produce better predictions than can be found through conventional block-based motion estimation and compensation. Computer-vision-based feature and object detection algorithms identify regions of interest throughout the video datacube. The detected features and objects are modeled with a compact set of parameters, and similar feature/object instances are associated across frames. Associated features/objects are formed into tracks and related to specific blocks of video data to be encoded. The tracking information is used to produce model-based predictions for those blocks of data, enabling more efficient navigation of the prediction search space than is typically achievable through conventional motion estimation methods. A hybrid framework enables modeling of data at multiple fidelities and selects the appropriate level of modeling for each portion of video data. A compliant-stream version of the model-based compression codec uses the modeling information indirectly to improve compression while producing bitstreams that can be interpreted by standard decoders.
摘要翻译：基于模型的压缩编解码器应用较高级别的建模以产生比通过传统的基于块的运动估计和补偿可以发现的更好的预测。基于计算机视觉的特征和对象检测算法识别整个视频数据库中的感兴趣区域。检测到的特征和对象用一组紧凑的参数建模，并且相似的特征/对象实例在帧之间相关联。相关特征/对象被形成轨道并与要编码的视频数据的特定块有关。跟踪信息用于为这些数据块产生基于模型的预测，使得能够比通常通过常规运动估计方法可实现的预测搜索空间更有效地导航。混合框架可以对多个保真度的数据进行建模，并为视频数据的每个部分选择适当的建模级别。基于模型的压缩编解码器的兼容流版本间接地使用建模信息来提高压缩，同时产生可由标准解码器解释的比特流。

10. 发明申请

US20130170541A1 Video Compression Repository and Model Reuse 有权
标题翻译：视频压缩库和模型重用
公开(公告)号：US20130170541A1
公开(公告)日：2013-07-04
申请号：US13772230
申请日：2013-02-20
申请人： Euclid Discoveries, LLC
发明人： Charles P. Pace , Darin DeForest , Nigel Lee , Renato Pizzorni , Richard Wingard
IPC分类号： H04N7/26
CPC分类号： H04N19/149 , G06T9/001
摘要： Systems and methods of improving video encoding/decoding efficiency may be provided. A feature-based processing stream is applied to video data having a series of video frames. Computer-vision-based feature and object detection algorithms identify regions of interest throughout the video datacube. The detected features and objects are modeled with a compact set of parameters, and similar feature/object instances are associated across frames. Associated features/objects are formed into tracks, and each track is given a representative, characteristic feature. Similar characteristic features are clustered and then stored in a model library, for reuse in the compression of other videos. A model-based compression framework makes use of the preserved model data by detecting features in a new video to be encoded, relating those features to specific blocks of data, and accessing similar model information from the model library. The formation of model libraries can be specialized to include personal, “smart” model libraries, differential libraries, and predictive libraries. Predictive model libraries can be modified to handle a variety of demand scenarios.
摘要翻译：可以提供改善视频编码/解码效率的系统和方法。基于特征的处理流被应用于具有一系列视频帧的视频数据。基于计算机视觉的特征和对象检测算法识别整个视频数据库中的感兴趣区域。检测到的特征和对象用一组紧凑的参数建模，并且相似的特征/对象实例在帧之间相关联。相关特征/对象被形成轨道，并且每个轨道被给予代表性的特征。类似的特征特征被聚类，然后存储在模型库中，以便在压缩其他视频中重用。基于模型的压缩框架通过检测要编码的新视频中的特征来使用保留的模型数据，将这些特征与特定的数据块相关联，以及从模型库访问相似的模型信息。模型库的形成可以专门包括个人，“智能”模型库，差异库和预测库。可以修改预测模型库来处理各种需求情况。

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