Patents by Inventor Abdelaziz DJELOUAH
Abdelaziz DJELOUAH has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20230377093Abstract: Techniques are disclosed for resampling images. In some embodiments, a resampling model includes (1) one or more feature extraction layers that extract features from an input image and a degradation map; (2) one or more resampling layers that generate warped features from the extracted features and a warp grid; and (3) one or more prediction layers that generate, from the warped features, an output image or resampling kernels that can be applied to the input image to generate an output image. In some embodiments, the resampling model can be trained by applying degradation maps to output images in a training data set to generate corresponding input images, and training the resampling model using the input images and the corresponding output images.Type: ApplicationFiled: May 19, 2023Publication date: November 23, 2023Inventors: Abdelaziz DJELOUAH, Michael Yves BERNASCONI, Farnood SALEHI, Christopher Richard SCHROERS
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Publication number: 20230379475Abstract: A system includes a machine learning (ML) model-based video downsampler configured to receive an input video sequence having a first display resolution, and to map the input video sequence to a lower resolution video sequence having a second display resolution lower than the first display resolution. The system also includes a neural network-based (NN-based) proxy video codec configured to transform the lower resolution video sequence into a decoded proxy bitstream. In addition, the system includes an upsampler configured to produce an output video sequence using the decoded proxy bitstream.Type: ApplicationFiled: August 4, 2023Publication date: November 23, 2023Inventors: Christopher Richard Schroers, Roberto Gerson de Albuquerque Azevedo, Nicholas David Gregory, Yuanyi Xue, Scott Labrozzi, Abdelaziz Djelouah
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Patent number: 11765360Abstract: A system includes a machine learning (ML) model-based video downsampler configured to receive an input video sequence having a first display resolution, and to map the input video sequence to a lower resolution video sequence having a second display resolution lower than the first display resolution. The system also includes a neural network-based (NN-based) proxy video codec configured to transform the lower resolution video sequence into a decoded proxy bitstream. In addition, the system includes an upsampler configured to produce an output video sequence using the decoded proxy bitstream.Type: GrantFiled: October 13, 2021Date of Patent: September 19, 2023Assignees: Disney Enterprises, Inc., ETH ZurichInventors: Christopher Richard Schroers, Roberto Gerson de Albuquerque Azevedo, Nicholas David Gregory, Yuanyi Xue, Scott Labrozzi, Abdelaziz Djelouah
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Publication number: 20230274138Abstract: A system includes a computing platform having a hardware processor and a memory storing a software code and a neural network (NN) having multiple layers including a last activation layer and a loss layer. The hardware processor executes the software code to identify different combinations of layers for testing the NN, each combination including candidate function(s) for the last activation layer and candidate function(s) for the loss layer. For each different combination, the software code configures the NN based on the combination, inputs, into the configured NN, a training dataset including multiple data objects, receives, from the configured NN, a classification of the data objects, and generates a performance assessment for the combination based on the classification. The software code determines a preferred combination of layers for the NN including selected candidate functions for the last activation layer and the loss layer, based on a comparison of the performance assessments.Type: ApplicationFiled: May 4, 2023Publication date: August 31, 2023Inventors: Hayko Jochen Wilhelm Riemenschneider, Leonhard Markus Helminger, Christopher Richard Schroers, Abdelaziz Djelouah
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Publication number: 20230267706Abstract: One embodiment of the present invention sets forth a technique for performing remastering of video content. The technique includes determining a first input frame corresponding to a first frame included in a first video and a first target frame corresponding to a second frame included in a second video based on one or more alignments between the first frame and the second frame. The technique also includes executing a machine learning model to convert the first input frame into a first output frame. The technique further includes training the machine learning model based on one or more losses associated with the first output frame and the first target frame.Type: ApplicationFiled: February 21, 2023Publication date: August 24, 2023Inventors: Abdelaziz DJELOUAH, Shinobu HATTORI, Christopher Richard SCHROERS, Andrew John WAHLQUIST
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Patent number: 11669723Abstract: A system includes a computing platform having a hardware processor and a memory storing a software code and a neural network (NN) having multiple layers including a last activation layer and a loss layer. The hardware processor executes the software code to identify different combinations of layers for testing the NN, each combination including candidate function(s) for the last activation layer and candidate function(s) for the loss layer. For each different combination, the software code configures the NN based on the combination, inputs, into the configured NN, a training dataset including multiple data objects, receives, from the configured NN, a classification of the data objects, and generates a performance assessment for the combination based on the classification. The software code determines a preferred combination of layers for the NN including selected candidate functions for the last activation layer and the loss layer, based on a comparison of the performance assessments.Type: GrantFiled: September 16, 2022Date of Patent: June 6, 2023Assignees: Disney Enterprises, Inc., ETH Zürich (EIDGENÖSSISCHE TECHNISCHE HOCHSCHULE ZÜRICH)Inventors: Hayko Jochen Wilhelm Riemenschneider, Leonhard Markus Helminger, Christopher Richard Schroers, Abdelaziz Djelouah
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Publication number: 20230153952Abstract: Restoration methods and systems are disclosed for video remastering. Techniques disclosed include receiving a video sequence. For each frame of the video sequence, techniques disclosed include encoding, by a degradation encoder, a video content associated with the frame into a latent vector. The latent vector is a representation of the degradation present in the video content; the degradation present in the video content includes one or more degradation types. Based on the latent vector and the video content, techniques disclosed further include generating, by a backbone network, one or more feature maps, and, then, restoring the frame based on the one or more feature maps.Type: ApplicationFiled: February 11, 2022Publication date: May 18, 2023Applicant: DISNEY ENTERPRISES, INC.Inventors: Abdelaziz Djelouah, Givi Meishvili, Christopher Richard Schroers, Shinobu Hattori
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Publication number: 20230116696Abstract: A system includes a machine learning (ML) model-based video downsampler configured to receive an input video sequence having a first display resolution, and to map the input video sequence to a lower resolution video sequence having a second display resolution lower than the first display resolution. The system also includes a neural network-based (NN-based) proxy video codec configured to transform the lower resolution video sequence into a decoded proxy bitstream. In addition, the system includes an upsampler configured to produce an output video sequence using the decoded proxy bitstream.Type: ApplicationFiled: October 13, 2021Publication date: April 13, 2023Inventors: Christopher Richard Schroers, Roberto Gerson de Albuquerque Azevedo, Nicholas David Gregory, Yuanyi Xue, Scott Labrozzi, Abdelaziz Djelouah
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Publication number: 20230077379Abstract: Systems and methods are disclosed for compressing a target video. A computer-implemented method may use a computer system that include one or more physical computer processors and non-transient electronic storage. The computer-implemented method may include: obtaining the target video, extracting one or more frames from the target video, and generating an estimated optical flow based on a displacement of pixels between the one or more frames. The one or more frames may include one or more of a key frame and a target frame.Type: ApplicationFiled: October 24, 2022Publication date: March 16, 2023Inventors: Christopher SCHROERS, Simone SCHAUB, Erika DOGGETT, Jared MCPHILLEN, Scott LABROZZI, Abdelaziz DJELOUAH
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Patent number: 11599804Abstract: A system includes a computing platform having a hardware processor, and a system memory storing a software code and a content labeling predictive model. The hardware processor is configured to execute the software code to scan a database to identify content assets stored in the database, parse metadata stored in the database to identify labels associated with the content assets, and generate a graph by creating multiple first links linking each of the content assets to its corresponding label or labels. The hardware processor is configured to further execute the software code to train, using the graph, the content labeling predictive model, to identify, using the trained content labeling predictive model, multiple second links among the content assets and the labels, and to annotate the content assets based on the second links.Type: GrantFiled: April 17, 2020Date of Patent: March 7, 2023Assignees: Disney Enterprises, Inc., ETH ZurichInventors: Hayko Jochen Wilhelm Riemenschneider, Leonhard Markus Helminger, Abdelaziz Djelouah, Christopher Richard Schroers
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Publication number: 20230065392Abstract: According to one implementation, a system for performing re-noising and neural network (NN) based image enhancement includes a computing platform having a processing hardware and a system memory storing a software code, a noise synthesizer, and an image restoration NN. The processing hardware is configured to execute the software code to receive a denoised image component and a noise component extracted from a degraded image, to generate, using the noise synthesizer and the noise component, synthesized noise corresponding to the noise component, and to interpolate, using the noise component and the synthesized noise, an output image noise. The processing hardware is further configured to execute the software code to enhance, using the image restoration NN, the denoised image component to provide an output image component, and to re-noise the output image component, using the output image noise, to produce an enhanced output image corresponding to the degraded image.Type: ApplicationFiled: August 27, 2021Publication date: March 2, 2023Inventors: Abdelaziz Djelouah, Shinobu Hattori, Christopher Richard Schroers
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Patent number: 11570397Abstract: One embodiment of the present invention sets forth a technique for performing deinterlacing. The technique includes separating a first interlaced video frame into a first sequence of fields ordered by time, the first sequence of fields including a first field. The technique also includes generating, by applying a deinterlacing network to a first field in the first sequence, a second field that is missing from the first sequence of fields and is complementary to the first field. The technique further includes constructing a progressive video frame based on the first field and the second field.Type: GrantFiled: July 10, 2020Date of Patent: January 31, 2023Assignees: DISNEY ENTERPRISES, INC., ETH ZÜRICH, (EIDGENÖSSISCHE TECHNISCHE HOCHSCHULE ZÜRICH)Inventors: Michael Bernasconi, Daniel Konrad Dorda, Abdelaziz Djelouah, Shinobu Hattori, Christopher Richard Schroers
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Publication number: 20230009121Abstract: A system includes a computing platform having a hardware processor and a memory storing a software code and a neural network (NN) having multiple layers including a last activation layer and a loss layer. The hardware processor executes the software code to identify different combinations of layers for testing the NN, each combination including candidate function(s) for the last activation layer and candidate function(s) for the loss layer. For each different combination, the software code configures the NN based on the combination, inputs, into the configured NN, a training dataset including multiple data objects, receives, from the configured NN, a classification of the data objects, and generates a performance assessment for the combination based on the classification. The software code determines a preferred combination of layers for the NN including selected candidate functions for the last activation layer and the loss layer, based on a comparison of the performance assessments.Type: ApplicationFiled: September 16, 2022Publication date: January 12, 2023Inventors: Hayko Jochen Wilhelm Riemenschneider, Leonhard Markus Helminger, Christopher Richard Schroers, Abdelaziz Djelouah
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Publication number: 20220383573Abstract: One embodiment of the present invention sets forth a technique for performing frame interpolation. The technique includes generating (i) a first set of feature maps based on a first set of rendering features associated with a first key frame, (ii) a second set of feature maps based on a second set of rendering features associated with a second key frame, and (iii) a third set of feature maps based on a third set of rendering features associated with a target frame. The technique also includes applying one or more neural networks to the first, second, and third set of feature maps to generate a set of mappings from a first set of pixels in the first key frame to a second set of pixels in the target frame. The technique further includes generating the target frame based on the set of mappings.Type: ApplicationFiled: May 19, 2021Publication date: December 1, 2022Inventors: Christopher Richard SCHROERS, Karlis Martins BRIEDIS, Abdelaziz DJELOUAH, Ian MCGONIGAL, Mark MEYER, Marios PAPAS, Markus PLACK
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Publication number: 20220337852Abstract: A system includes a machine learning (ML) model-based video encoder configured to receive an uncompressed video sequence including multiple video frames, determine, from among the multiple video frames, a first video frame subset and a second video frame subset, encode the first video frame subset to produce a first compressed video frame subset, and identify a first decompression data for the first compressed video frame subset. The ML model-based video encoder is further configured to encode the second video frame subset to produce a second compressed video frame subset, and identify a second decompression data for the second compressed video frame subset. The first decompression data is specific to decoding the first compressed video frame subset but not the second compressed video frame subset, and the second decompression data is specific to decoding the second compressed video frame subset but not the first compressed video frame subset.Type: ApplicationFiled: March 25, 2022Publication date: October 20, 2022Inventors: Abdelaziz Djelouah, Leonhard Markus Helminger, Roberto Gerson de Albuquerque Azevedo, Christopher Richard Schroers, Scott Labrozzi, Yuanyi Xue
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Patent number: 11475280Abstract: A system includes a computing platform having a hardware processor and a memory storing a software code and a neural network (NN) having multiple layers including a last activation layer and a loss layer. The hardware processor executes the software code to identify different combinations of layers for testing the NN, each combination including candidate function(s) for the last activation layer and candidate function(s) for the loss layer. For each different combination, the software code configures the NN based on the combination, inputs, into the configured NN, a training dataset including multiple data objects, receives, from the configured NN, a classification of the data objects, and generates a performance assessment for the combination based on the classification. The software code determines a preferred combination of layers for the NN including selected candidate functions for the last activation layer and the loss layer, based on a comparison of the performance assessments.Type: GrantFiled: March 3, 2020Date of Patent: October 18, 2022Assignees: Disney Enterprises, Inc., ETH ZurichInventors: Hayko Jochen Wilhelm Riemenschneider, Leonhard Markus Helminger, Christopher Richard Schroers, Abdelaziz Djelouah
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Patent number: 11475543Abstract: According to one implementation, an image enhancement system includes a computing platform including a hardware processor and a system memory storing a software code configured to provide a normalizing flow based generative model trained using an objective function. The hardware processor executes the software code to receive an input image, transform the input image to a latent space representation of the input image using the normalizing flow based generative model, and perform an optimization of the latent space representation of the input image to identify an enhanced latent space representation of the input image. The software code then uses the normalizing flow based generative model to reverse transform the enhanced latent space representation of the input image to an enhanced image corresponding to the input image.Type: GrantFiled: July 1, 2020Date of Patent: October 18, 2022Assignees: Disney Enterprises, Inc., ETH ZurichInventors: Abdelaziz Djelouah, Leonhard Markus Helminger, Michael Bernasconi, Christopher Richard Schroers
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Publication number: 20220329876Abstract: A system processing hard e executes a machine learning (ML) model-based video compression encoder to receive uncompressed video content and corresponding motion compensated video content, compare the uncompressed and motion compensated video content to identify an image space residual, transform the image space residual to a latent space representation of the uncompressed video content, and transform, using a trained image compression ML model, the motion compensated video content to a latent space representation of the motion compensated video content.Type: ApplicationFiled: March 25, 2022Publication date: October 13, 2022Inventors: Abdelaziz Djelouah, Leonhard Markus Helminger, Roberto Gerson de Albuquerque Azevedo, Scott Labrozzi, Christopher Richard Schroers, Yuanyi Xue
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Publication number: 20220014708Abstract: One embodiment of the present invention sets forth a technique for performing deinterlacing. The technique includes separating a first interlaced video frame into a first sequence of fields ordered by time, the first sequence of fields including a first field. The technique also includes generating, by applying a deinterlacing network to a first field in the first sequence, a second field that is missing from the first sequence of fields and is complementary to the first field. The technique further includes constructing a progressive video frame based on the first field and the second field.Type: ApplicationFiled: July 10, 2020Publication date: January 13, 2022Inventors: Michael Bernasconi, Daniel Dorda, Abdelaziz Djelouah, Sally Hattori, Christopher SCHROERS
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Publication number: 20220005161Abstract: According to one implementation, an image enhancement system includes a computing platform including a hardware processor and a system memory storing a software code configured to provide a normalizing flow based generative model trained using an objective function. The hardware processor executes the software code to receive an input image, transform the input image to a latent space representation of the input image using the normalizing flow based generative model, and perform an optimization of the latent space representation of the input image to identify an enhanced latent space representation of the input image. The software code then uses the normalizing flow based generative model to reverse transform the enhanced latent space representation of the input image to an enhanced image corresponding to the input image.Type: ApplicationFiled: July 1, 2020Publication date: January 6, 2022Inventors: Abdelaziz Djelouah, Leonhard Markus Helminger, Michael Bernasconi, Christopher Richard Schroers