Abstract: In some embodiments, a method trains a first parameter of a differentiable proxy codec to encode source content based on a first loss between first compressed source content and second compressed source content that is output by a target codec. A pre-processor pre-processes a source image to output a pre-processed source image, the pre-processing being based on a second parameter. The differentiable proxy codec encodes the pre-processed source image into a compressed pre-processed source image based on the first parameter. The method determines a second loss between the source image and the compressed pre-processed source image and determines an adjustment to the first parameter based on the second loss. The adjustment is used to adjust the second parameter of the pre-processor based on the second loss.

Abstract: In some embodiments, a method generates a first representation of a first relationship between bitrate and quality based on first features of a first portion of a video. The first representation is analyzed to determine a first list of potential bitrates for the first portion of video. The method analyzes potential bitrates and quality associated with the respective potential bitrates to refine the first list of potential bitrates to a second list of bitrates. The second list of bitrates includes a different list of bitrates than the first list of potential bitrates. The method outputs the second list of bitrates for encoding the first portion of video.

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: In some embodiments, a method analyzes flagged locations from a plurality of locations in an encoding of a video to form a cluster of locations. Draft micro-chunk boundaries for the cluster are determined based on searching for a first start location and a first end location in the encoding. The method searches in a first search range before the first start location and a second search range after the first end location for a second start location in the first search range and a second end location in the second search range. The second start location and the second end location form a micro-chunk. An encoding parameter set is determined for the micro-chunk formed by the second start location and the second end location based on content characteristics of the micro-chunk. The method uses the encoding parameter set to encode the micro-chunk for insertion in the encoding of the video.

Abstract: In some embodiments, a grain analysis system is configured for analyzing a first video frame and outputting respective first film grain information for film grain that is included in the first video frame or configured for analyzing a second video frame and outputting second film grain information. At least one of a grain removal system and a grain synthesis system is included. The grain removal system is configured for removing the film grain from the first video frame using the first film grain information to generate a third video frame corresponding to the first video frame with film grain removed. The grain analysis system is separate from the grain removal system. The grain synthesis system is configured for synthesizing film grain for the third video frame using the first film grain information or the second film grain information. The grain analysis system is separate from the grain synthesis system.

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: In some embodiments, a method generates a first representation of a first relationship between bitrate and quality based on first features of a first portion of a video. Also, the method generates a second representation of a second relationship between bitrate and quality based on second features of a second portion of a video. The first representation is analyzed to determine a first list of bitrates for the first portion of video and the second representation is analyzed to determine a second list of bitrates for the second portion of video. The first list of bitrates is different from the second list of bitrates. The method outputs the first list of bitrates for use encoding the first portion of video and the second list of bitrates for use encoding the second portion of video.

Abstract: A system processing hardware 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: In some embodiments, a method trains a first parameter of a differentiable proxy codec to encode source content based on a first loss between first compressed source content and second compressed source content that is output by a target codec. A pre-processor pre-processes a source image to output a pre-processed source image, the pre-processing being based on a second parameter. The differentiable proxy codec encodes the pre-processed source image into a compressed pre-processed source image based on the first parameter. The method determines a second loss between the source image and the compressed pre-processed source image and determines an adjustment to the first parameter based on the second loss. The adjustment is used to adjust the second parameter of the pre-processor based on the second loss.

Abstract: A system includes a hardware processor and a memory storing a video/audio (V/A) synchronizer including video and audio encoders. The hardware processor executes the V/A synchronizer to receive raw video and audio extracted from media content, partition the raw video into video frame patches, partition the raw audio into audio samples, pre-process the video frame patches and the audio samples for encoding. The hardware processor further executes the V/A synchronizer to encode, using the video encoder, the pre-processed video frame patches to provide pre-processed and encoded video frame patches used to provide a latent representation of the raw video, encode, using the audio encoder, the pre-processed audio samples to provide pre-processed and encoded audio samples used to provide a latent representation of the raw audio, and synchronize, using the latent representations of the raw video and the raw audio, the raw audio with the raw video.

Abstract: In some embodiments, a method sends information for a sample of content, a first question, and a second question for output on an interface. The first question receives, from a subject, a first response for a sample level rating for an artifact that is perceived to be visible in the sample and the second question receives, from the subject, a second response for regions in the sample that are perceived to contain the artifact. The method receives the first response for the sample level rating and the second response for regions that are perceived to contain the artifact. First responses are combined from multiple subjects to generate an opinion score for the sample and second responses are combined to generate region scores for regions. The method generates training data from the opinion score and the region scores to train a process to perform an action based on the artifacts.

Abstract: A system includes a computing platform having processing hardware, and a memory storing software code. The software code is executed to receive content having a sequence of content segments, and marker data identifying a location within the sequence, identify, using the content and the marker data, segment boundaries of a content segment containing the location, determine, using the location and the segment boundaries, whether the location is situated within a predetermined interval of one of the segment boundaries, and re-encode a subsection of the sequence to produce a new segment boundary at the location. When the location is not situated within the predetermined interval, the subsection of the sequence includes the content segment containing the location. When the location is situated within the predetermined interval, the subsection of the sequence includes the content segment containing the location and a content segment adjoining the content segment containing the location.

Abstract: A system processing hardware 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: In some embodiments, a method generates a first representation of a first relationship between bitrate and quality based on first features of a first portion of a video. Also, the method generates a second representation of a second relationship between bitrate and quality based on second features of a second portion of a video. The first representation is analyzed to determine a first list of bitrates for the first portion of video and the second representation is analyzed to determine a second list of bitrates for the second portion of video. The first list of bitrates is different from the second list of bitrates. The method outputs the first list of bitrates for use encoding the first portion of video and the second list of bitrates for use encoding the second portion of video.

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: 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 method receives a video. The method analyzes information for a pixel of a frame in the video to determine a first value and a second value for the pixel. The first value is based on an image structure formed by the pixel in the frame and the second value is based on interframe motion of the image structure at the pixel. A third value is determined for an amount of judder based on the first value and the second value. The method outputs the third value to evaluate the video.

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: Systems and methods for predicting a target set of pixels are disclosed. In one embodiment, a method may include obtaining target content. The target content may include a target set of pixels to be predicted. The method may also include convolving the target set of pixels to generate an estimated set of pixels. The method may include matching a second set of pixels in the target content to the target set of pixels. The second set of pixels may be within a distance from the target set of pixels. The method may include refining the estimated set of pixels to generate a refined set of pixels using a second set of pixels in the target content.

Abstract: A system includes a computing platform having processing hardware, and a memory storing software code. The software code is executed to receive digital content indexed to a timeline, receive insertion data identifying a timecode of the timeline, and encode the digital content using the insertion data to provide segmented content having a segment boundary at the timecode, and first and second segments adjoining the segment boundary, wherein the first segment precedes, and the second segment succeeds, the segment boundary. The software code also re-processes the first and second segments to apply a fade-out within or to the first segment and a fade-in within or to the second segment, wherein re-processing the first and second segments provides encoded segments having the segment boundary configured as an insertion point for supplemental content.