Patents Assigned to Euclid Discoveries, LLC
  • Patent number: 9743078
    Abstract: 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.
    Type: Grant
    Filed: March 12, 2013
    Date of Patent: August 22, 2017
    Assignee: Euclid Discoveries, LLC
    Inventors: Darin DeForest, Charles P. Pace, Nigel Lee, Renato Pizzorni
  • Patent number: 9621917
    Abstract: Continuous block tracking (CBT) tracks macroblock locations over reference frames to produce better inter-predictions than conventional block-based motion estimation/compression. CBT includes frame-to-frame tracking, estimating motion from a frame to a previous frame, and continuous tracking, related frame-to-frame motion vectors to block tracks. Frame-to-frame tracking may include block based or hierarchical motion estimations. CBT combined with enhanced predictive zonal search may create unified motion estimation. Accumulated CBT results may form trajectories for trajectory-based CBT predictions. Metrics measuring continuous track and motion vector quality can assess relative priority of CBT prediction against non-tracker-based predictions and to modify encoding choices. Continuous tracks can be analyzed for goodness-of-fit to translational motion models, with outliers removed from encoding. Translational motion models can be extended to entire frames in adaptive picture type selection.
    Type: Grant
    Filed: November 4, 2014
    Date of Patent: April 11, 2017
    Assignee: EUCLID DISCOVERIES, LLC
    Inventors: Dane P. Kottke, John J. Guo, Jeyun Lee, Sangseok Park, Christopher Weed, Justin Kwan, Nigel Lee
  • Patent number: 9578345
    Abstract: 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.
    Type: Grant
    Filed: December 21, 2012
    Date of Patent: February 21, 2017
    Assignee: Euclid Discoveries, LLC
    Inventors: Darin DeForest, Charles P. Pace, Nigel Lee, Renato Pizzorni
  • Patent number: 9532069
    Abstract: 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.
    Type: Grant
    Filed: October 29, 2014
    Date of Patent: December 27, 2016
    Assignee: Euclid Discoveries, LLC
    Inventors: Charles P. Pace, Darin DeForest, Nigel Lee, Renato Pizzorni, Richard Wingard
  • Patent number: 9106977
    Abstract: Personal object based archival systems and methods are provided for processing and compressing video. By analyzing features unique to a user, such as face, family, and pet attributes associated with the user, an invariant model can be determined to create object model adapters personal to each user. These personalized video object models can be created using geometric and appearance modeling techniques, and they can be stored in an object model library. The object models can be reused for processing other video streams. The object models can be shared in a peer-to-peer network among many users, or the object models can be stored in an object model library on a server. When the compressed (encoded) video is reconstructed, the video object models can be accessed and used to produce quality video with nearly lossless compression.
    Type: Grant
    Filed: December 30, 2011
    Date of Patent: August 11, 2015
    Assignee: Euclid Discoveries, LLC
    Inventor: Charles P. Pace
  • Patent number: 8964835
    Abstract: Systems and methods of processing video data are provided. Video data having a series of video frames is received and processed. One or more instances of a candidate feature are detected in the video frames. The previously decoded video frames are processed to identify potential matches of the candidate feature. When a substantial amount of portions of previously decoded video frames include instances of the candidate feature, the instances of the candidate feature are aggregated into a set. The candidate feature set is used to create a feature-based model. The feature-based model includes a model of deformation variation and a model of appearance variation of instances of the candidate feature. The feature-based model compression efficiency is compared with the conventional video compression efficiency.
    Type: Grant
    Filed: December 30, 2011
    Date of Patent: February 24, 2015
    Assignee: Euclid Discoveries, LLC
    Inventor: Charles P. Pace
  • Patent number: 8942283
    Abstract: Systems and methods of processing video data are provided. Video data having a series of video frames is received and processed. One or more instances of a candidate feature are detected in the video frames. The previously decoded video frames are processed to identify potential matches of the candidate feature. When a substantial amount of portions of previously decoded video frames include instances of the candidate feature, the instances of the candidate feature are aggregated into a set. The candidate feature set is used to create a feature-based model. The feature-based model includes a model of deformation variation and a model of appearance variation of instances of the candidate feature. The feature-based model compression efficiency is compared with the conventional video compression efficiency.
    Type: Grant
    Filed: October 6, 2009
    Date of Patent: January 27, 2015
    Assignee: Euclid Discoveries, LLC
    Inventor: Charles P. Pace
  • Patent number: 8908766
    Abstract: A method and apparatus for image data compression includes detecting a portion of an image signal that uses a disproportionate amount of bandwidth compared to other portions of the image signal. The detected portion of the image signal result in determined components of interest. Relative to certain variance, the method and apparatus normalize the determined components of interest to generate an intermediate form of the components of interest. The intermediate form represents the components of interest reduced in complexity by the certain variance and enables a compressed form of the image signal where the determined components of interest maintain saliency. In one embodiment, the video signal is a sequence of video frames.
    Type: Grant
    Filed: January 4, 2008
    Date of Patent: December 9, 2014
    Assignee: Euclid Discoveries, LLC
    Inventor: Charles P. Pace
  • Patent number: 8902971
    Abstract: 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.
    Type: Grant
    Filed: February 20, 2013
    Date of Patent: December 2, 2014
    Assignee: Euclid Discoveries, LLC
    Inventors: Charles P. Pace, Darin DeForest, Nigel Lee, Renato Pizzorni, Richard Wingard
  • Patent number: 8842154
    Abstract: Systems and methods for processing video are provided. Video compression schemes are provided to reduce the number of bits required to store and transmit digital media in video conferencing or videoblogging applications. A photorealistic avatar representation of a video conference participant is created. The avatar representation can be based on portions of a video stream that depict the conference participant. A face detector is used to identify, track and classify the face. Object models including density, structure, deformation, appearance and illumination models are created based on the detected face. An object based video compression algorithm, which uses machine learning face detection techniques, creates the photorealistic avatar representation from parameters derived from the density, structure, deformation, appearance and illumination models.
    Type: Grant
    Filed: July 3, 2012
    Date of Patent: September 23, 2014
    Assignee: Euclid Discoveries, LLC
    Inventor: Charles P. Pace
  • Patent number: 8553782
    Abstract: Personal object based archival systems and methods are provided for processing and compressing video. By analyzing features unique to a user, such as face, family, and pet attributes associated with the user, an invariant model can be determined to create object model adapters personal to each user. These personalized video object models can be created using geometric and appearance modeling techniques, and they can be stored in an object model library. The object models can be reused for processing other video streams. The object models can be shared in a peer-to-peer network among many users, or the object models can be stored in an object model library on a server. When the compressed (encoded) video is reconstructed, the video object models can be accessed and used to produce quality video with nearly lossless compression.
    Type: Grant
    Filed: January 4, 2008
    Date of Patent: October 8, 2013
    Assignee: Euclid Discoveries, LLC
    Inventor: Charles P. Pace
  • Publication number: 20130230099
    Abstract: 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.
    Type: Application
    Filed: March 12, 2013
    Publication date: September 5, 2013
    Applicant: Euclid Discoveries, LLC
    Inventors: Darin DeForest, Charles P. Pace, Nigel Lee, Renato Pizzorni
  • Publication number: 20130170541
    Abstract: 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.
    Type: Application
    Filed: February 20, 2013
    Publication date: July 4, 2013
    Applicant: EUCLID DISCOVERIES, LLC
    Inventor: Euclid Discoveries, LLC
  • Publication number: 20130114703
    Abstract: 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.
    Type: Application
    Filed: December 21, 2012
    Publication date: May 9, 2013
    Applicant: Euclid Discoveries, LLC
    Inventor: Euclid Discoveries, LLC
  • Publication number: 20130107948
    Abstract: 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.
    Type: Application
    Filed: December 21, 2012
    Publication date: May 2, 2013
    Applicant: Euclid Discoveries, LLC
    Inventor: Euclid Discoveries, LLC
  • Publication number: 20130083854
    Abstract: A data compression method and apparatus that includes detecting a portion of a signal comprising a sequence of video frames that uses a disproportionate amount of bandwidth compared to other portions of the signal. The detected portion of the signal result in determined components of interest. Relative to certain variance, these components of interest are normalized to generate an intermediate form, which represents the components of interest reduced in complexity by the certain variance and enables a compressed form of the signal that maintains saliency. The detecting includes any of: (i) analyzing image gradients across frames where image gradient is a first derivative model and gradient flow is a second derivative, (ii) integrating finite differences of pels temporally/spatially to form a derivative model, (iii) analyzing an illumination field across frames, and (iv) predictive analysis, to determine bandwidth consumption, which is used to determine the components of interest.
    Type: Application
    Filed: November 21, 2012
    Publication date: April 4, 2013
    Applicant: EUCLID DISCOVERIES, LLC
    Inventor: Euclid Discoveries, LLC
  • Patent number: 8243118
    Abstract: Systems and methods for processing video are provided. Video compression schemes are provided to reduce the number of bits required to store and transmit digital media in video conferencing or videoblogging applications. A photorealistic avatar representation of a video conference participant is created. The avatar representation can be based on portions of a video stream that depict the conference participant. A face detector is used to identify, track and classify the face. Object models including density, structure, deformation, appearance and illumination models are created based on the detected face. An object based video compression algorithm, which uses machine learning face detection techniques, creates the photorealistic avatar representation from parameters derived from the density, structure, deformation, appearance and illumination models.
    Type: Grant
    Filed: January 4, 2008
    Date of Patent: August 14, 2012
    Assignee: Euclid Discoveries, LLC
    Inventor: Charles P. Pace
  • Publication number: 20120163446
    Abstract: Personal object based archival systems and methods are provided for processing and compressing video. By analyzing features unique to a user, such as face, family, and pet attributes associated with the user, an invariant model can be determined to create object model adapters personal to each user. These personalized video object models can be created using geometric and appearance modeling techniques, and they can be stored in an object model library. The object models can be reused for processing other video streams. The object models can be shared in a peer-to-peer network among many users, or the object models can be stored in an object model library on a server. When the compressed (encoded) video is reconstructed, the video object models can be accessed and used to produce quality video with nearly lossless compression.
    Type: Application
    Filed: December 30, 2011
    Publication date: June 28, 2012
    Applicant: Euclid Discoveries, LLC
    Inventor: Charles P. Pace
  • Publication number: 20120155536
    Abstract: Systems and methods of processing video data are provided. Video data having a series of video frames is received and processed. One or more instances of a candidate feature are detected in the video frames. The previously decoded video frames are processed to identify potential matches of the candidate feature. When a substantial amount of portions of previously decoded video frames include instances of the candidate feature, the instances of the candidate feature are aggregated into a set. The candidate feature set is used to create a feature-based model. The feature-based model includes a model of deformation variation and a model of appearance variation of instances of the candidate feature. The feature-based model compression efficiency is compared with the conventional video compression efficiency.
    Type: Application
    Filed: December 30, 2011
    Publication date: June 21, 2012
    Applicant: Euclid Discoveries, LLC
    Inventor: Charles P. Pace
  • Publication number: 20100086062
    Abstract: Personal object based archival systems and methods are provided for processing and compressing video. By analyzing features unique to a user, such as face, family, and pet attributes associated with the user, an invariant model can be determined to create object model adapters personal to each user. These personalized video object models can be created using geometric and appearance modeling techniques, and they can be stored in an object model library. The object models can be reused for processing other video streams. The object models can be shared in a peer-to-peer network among many users, or the object models can be stored in an object model library on a server. When the compressed (encoded) video is reconstructed, the video object models can be accessed and used to produce quality video with nearly lossless compression.
    Type: Application
    Filed: January 4, 2008
    Publication date: April 8, 2010
    Applicant: EUCLID DISCOVERIES, LLC
    Inventor: Charles P. Pace