Abstract: A system includes processing hardware and a memory storing software code. The processing hardware executes the software code to receive automation data for media content having a default playback experience, analyze, using the automation data, at least one parameter of the media content, and generate, based on the analyzing, one or more automation instruction(s) for at least one portion(s) of the media content. The automation instruction(s) include at least one of: one or more bounding timestamps of the media content portion(s), an increased or reduced playback speed for the media content portion(s) relative to the default playback experience, or a variable playback speed for the media content portion(s). The software code is further executed to outputs the automation instruction(s) to a media delivery platform configured to distribute and control the quality of the media content or to a media player configured to automate playback of the media content.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments provide for improved stream generation. A first encoded segment is generated by encoding a first segment, of a plurality of segments in a media asset, using a first bitrate of a plurality of bitrates specified in an encoding ladder. A second encoded segment is generated by encoding the first segment using a second bitrate, where the second bitrate is lower than the first bitrate. Upon receiving a request for the first segment at the first bitrate, the second encoded segment is output based at least in part on determining that a first quality of the second encoded segment is within a tolerance of a second quality of the first encoded segment.

Abstract: 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.

Abstract: Embodiments provide for improved stream generation. A target average bitrate (TAB) segment is generated by encoding a first segment, of a plurality of segments in a video, using a first maximum average bitrate (MAB) of a plurality of MABs specified in an encoding ladder. An intermediate average bitrate (IAB) segment is generated by encoding the first segment using a first intermediate bitrate, wherein the first intermediate bitrate is lower than the first MAB. Upon receiving a request for the first segment at the first MAB, the IAB segment is output based at least in part on determining that a first quality score of the IAB segment is within a predefined tolerance of a second quality score of the TAB segment.

Abstract: 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.

Abstract: 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.

Abstract: A system includes a computing platform having processing hardware, and a system memory storing software code and one or more machine learning (ML) model(s) trained using contrastive learning based on a similarity metric. The processing hardware is configured to execute the software code to receive input data including a plurality of content segments, map, using the ML model(s), each of the plurality of content segments to a respective embedding in a continuous vector space to provide a plurality of mapped embeddings, and perform one of a classification or a regression of the content segments using the plurality of mapped embeddings. The processing hardware is also configured to execute the software code to discover, based on the classification or the regression, at least one new label for characterizing the plurality of content segments.

Abstract: Embodiments provide for improved stream generation. A target average bitrate (TAB) segment is generated by encoding a first segment, of a plurality of segments in a video, using a first maximum average bitrate (MAB) of a plurality of MABs specified in an encoding ladder. An intermediate average bitrate (IAB) segment is generated by encoding the first segment using a first intermediate bitrate, wherein the first intermediate bitrate is lower than the first MAB. Upon receiving a request for the first segment at the first MAB, the IAB segment is output based at least in part on determining that a first quality score of the IAB segment is within a predefined tolerance of a second quality score of the TAB segment.

Abstract: Embodiments provide for improved stream generation are provided. A video comprising a plurality of segments and an encoding ladder specifying a plurality of maximum average bitrates (MABs) are received. A plurality of intermediate bitrates interspersed among the plurality of MABs are selected. A target average bitrate (TAB) segment is generated by encoding a first segment using a first MAB, and a first intermediate average bitrate (IAB) segment is generated by encoding the first segment using a first intermediate bitrate. Quality scores are generated for the first TAB segment and the first IAB segment. A first output segment is selected for the first segment at the first MAB based on the quality scores, where the first output segment is either the first TAB segment or the first IAB segment. Upon receiving a request for the first segment at the first MAB, the first output segment is outputted.

Abstract: A data processing system includes a computing platform having a hardware processor and a memory storing a data compression software code. The hardware processor executes the data compression software code to receive a series of compression input data and encode a first compression input data of the series to a latent space representation of the first compression input data. The data compression software code further decodes the latent space representation to produce an input space representation of the first compression input data corresponding to the latent space representation, and generates f refined latent values for re-encoding the first compression input data based on a comparison of the first compression input data with its input space representation. The data compression software code then re-encodes the first compression input data using the refined latent values to produce a first compressed data corresponding to the first compression input data.

Abstract: Systems and methods are disclosed for generating a latent space residual. A computer-implemented method may use a computer system that includes non-transient electronic storage, a graphical user interface, and one or more physical computer processors. The computer-implemented method may include: obtaining a target frame, obtaining a reconstructed frame, encoding the target frame into a latent space to generate a latent space target frame, encoding the reconstructed frame into the latent space to generate a latent space reconstructed frame, and generating a latent space residual based on the latent space target frame and the latent space reconstructed frame.

Abstract: According to one implementation, an image compression system includes a computing platform having a hardware processor and a system memory storing a software code. 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, and quantize the latent space representation of the input image to produce multiple quantized latents. The hardware processor further executes the software code to encode the quantized latents using a probability density function of the latent space representation of the input image, to generate a bitstream, and convert the bitstream into an output image corresponding to the input image. The probability density function of the latent space representation of the input image is obtained based on a normalizing flow mapping of one of the input image or the latent space representation of the input image.

Abstract: Systems and methods are disclosed for reconstructing a frame. A computer-implemented method may use a computer system that includes non-transient electronic storage, a graphical user interface, and one or more physical computer processors. The computer-implemented method may include: obtaining one or more reference frames from non-transient electronic storage, generating one or more displacement maps based on the one or more reference frames and a target frame with the physical computer processor, generating one or more warped frames based on the one or more reference frames and the one or more displacement maps with the physical computer processor, obtaining a conditioned reconstruction model from the non-transient electronic storage, and generating one or more blending coefficients and one or more reconstructed displacement maps by applying the one or more displacement maps, the one or more warped frames, and a target frame to the conditioned reconstruction model with the physical computer processor.

Abstract: Embodiments herein describe dividing a video into chunks with varying lengths based on the content within those frames. In contrast, dividing the video at a fix interval is prone to generating chunks starting at the middle of hard to encode areas, which can lead to a loss of encoder rate-control efficiency and produce visual quality gaps at the beginning of such chunks. The embodiments herein can identify a set of boundaries for dividing the video into chunks having similar lengths and with little to no impact on visual quality. In one embodiment, the boundaries of the chunks are placed at locations (or frames) that are far from the complex (or hard to encode) areas of the video. To do so, the system evaluates the video using various complexity metrics to identify the complex areas that require more bits to encode relative to less complex areas.

Abstract: Systems and methods are disclosed for generating a latent space residual. A computer-implemented method may use a computer system that includes non-transient electronic storage, a graphical user interface, and one or more physical computer processors. The computer-implemented method may include: obtaining a target frame, obtaining a reconstructed frame, encoding the target frame into a latent space to generate a latent space target frame, encoding the reconstructed frame into the latent space to generate a latent space reconstructed frame, and generating a latent space residual based on the latent space target frame and the latent space reconstructed frame.

Abstract: Systems and methods are disclosed for reconstructing a frame. A computer-implemented method may use a computer system that includes non-transient electronic storage, a graphical user interface, and one or more physical computer processors. The computer-implemented method may include: obtaining one or more reference frames from non-transient electronic storage, generating one or more displacement maps based on the one or more reference frames and a target frame with the physical computer processor, generating one or more warped frames based on the one or more reference frames and the one or more displacement maps with the physical computer processor, obtaining a conditioned reconstruction model from the non-transient electronic storage, and generating one or more blending coefficients and one or more reconstructed displacement maps by applying the one or more displacement maps, the one or more warped frames, and a target frame to the conditioned reconstruction model with the physical computer processor.

Abstract: A device, system, and method cast a ray projection from a perspective view. The method includes determining a first mask for a first object from a first frame captured by a first camera. The method includes determining a second mask for the first object from a second frame captured by a second camera. The method includes generating a 3D mask by associating the first mask and the second mask. The method includes determining a location of the 3D mask. The method includes generating the ray projection of the 3D mask from a perspective of a second object.

Abstract: A data processing system includes a computing platform having a hardware processor and a memory storing a data compression software code. The hardware processor executes the data compression software code to receive a series of compression input data and encode a first compression input data of the series to a latent space representation of the first compression input data. The data compression software code further decodes the latent space representation to produce an input space representation of the first compression input data corresponding to the latent space representation, and generates f refined latent values for re-encoding the first compression input data based on a comparison of the first compression input data with its input space representation. The data compression software code then re-encodes the first compression input data using the refined latent values to produce a first compressed data corresponding to the first compression input data.