Abstract: An apparatus for encoding an image block includes a processor that presents, to a machine-learning model, the image block, obtains the partition decision for encoding the image block from the model, and encodes the image block using the partition decision. The model is trained to output a partition decision for encoding the image block by using training data for a plurality of training blocks as input, the training data including for a training block, partition decisions for encoding the training block, and, for each partition decision, a rate-distortion value resulting from encoding the training block using the partition decision. The model is trained using a loss function combining a partition loss function based upon a relationship between the partition decisions and respective predicted partitions, and a rate-distortion cost loss function based upon a relationship between the rate-distortion values and respective predicted rate-distortion values.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating and decoding watermarks. An image and a data item is received. The encoder generates a first watermark and then a second watermark is generated using multiple first watermarks. The second watermark is used to watermark the image by overlaying the second watermark over the image. To decode the watermark, presence of a watermark is determined on a portion of an image. A distortion model determines distortions in the image and modifies the portion of the image based on the predicted distortions. The modified portion is decoded using the decoder to obtain a predicted first data item that is further used to validate the watermark based on the first data item.

Abstract: Systems and methods of enforcing policies in a computer environment for content distribution using pointwise mutual information (PMI) based clustering are provided. The system can maintain a network of nodes representing a plurality of assets. Upon detecting that an asset is associated with a policy label, the system can identify attributes of the asset and compute a PMI score indicating whether nodes of the network sharing the attributes belong to a single content source. Upon determining that the PMI score exceeds a predefined threshold value, the system can identify a cluster of nodes including the nodes sharing the attributes. The system can tag the cluster, for example, as being associated with a content source that is associated with the policy label.

Abstract: Decoding a current block includes decoding a subset of quantized transform coefficients of a quantized transform block using a first scan order. A second scan order is determined based on the subset of the quantized transform coefficients. Remaining quantized transform coefficients of the quantized transform block are decoded based on the second scan order. A context model for decoding an intra-prediction mode is determined based on at least the subset of the quantized transform coefficients. The intra-prediction mode is decoded based on the context model. The current block is obtained based on the quantized transform coefficients and the intra-prediction mode.

Abstract: Systems and methods of enforcing policies in a computer environment for content distribution using pointwise mutual information (PMI) based clustering are provided. The system can maintain a network of nodes representing a plurality of assets. Upon detecting that an asset is associated with a policy label, the system can identify attributes of the asset and compute a PMI score indicating whether nodes of the network sharing the attributes belong to a single content source. Upon determining that the PMI score exceeds a predefined threshold value, the system can identify a cluster of nodes including the nodes sharing the attributes. The system can tag the cluster, for example, as being associated with a content source that is associated with the policy label.

Abstract: Methods of encoding and decoding for video data are described for encoding or decoding multi-level significance maps. Distinct context sets may be used for encoding the significant-coefficient flags in different regions of the transform unit. In a fixed case, the regions are defined by coefficient group borders. In one example, the upper-left coefficient group is a first region and the other coefficient groups are a second region. In a dynamic case, the regions are defined by coefficient group borders, but the encoder and decoder dynamically determine in which region each coefficient group belongs. Coefficient groups may be assigned to one region or another based on, for example, whether their respective significant-coefficient-group flags were inferred or not.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for jointly training an encoder that generates a watermark and a decoder that decodes a data item encoded within the watermark. The training comprises obtaining a plurality of training images and data items. For each training image, a first watermark is generated using an encoder and a subsequent second watermark is generated by tiling two or more first watermarks. The training image is watermarked using the second watermark to generate a first error value and distortions are added to the watermarked image. A distortion detector predicts the distortions based on which the distorted image is modified. The modified image is decoded by the decoder to generate a predicted data item and a second error value. The training parameters of the encoder and decoder are adjusted based on the first and the second error value.

Abstract: Methods of encoding and decoding for video data are described for encoding or decoding coefficients for a transform unit. In particular, the sign bits for the non-zero coefficients are encoded using sign bit hiding. Two or more sets of coefficients are defined for the transform unit and a sign bit may be hidden for each set, subject to satisfaction of a threshold test. The sets may correspond to coefficient groups that are otherwise used in multi-level significance map encoding and decoding.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for detecting and decoding a visually imperceptible or perceptible watermark. A watermark detection apparatus determines whether the particular image includes a visually imperceptible or perceptible watermark using detector a machine learning model. If the watermark detection apparatus detects a watermark, the particular image is routed to a watermark decoder. If the watermark detection apparatus cannot detect a watermark in the particular image, the particular image is filtered from further processing. The watermark decoder decodes the visually imperceptible or perceptible watermark detected in the particular image. After decoding, an item depicted in the particular image is validated based data extracted from the decoded visually imperceptible or perceptible watermark.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a visually imperceptible or a visually perceptible watermark and outputting a result based on the determination. A watermark decoder receives an input image. The watermark decoder applies a decoder machine learning model to decode a watermarks at different levels of zoom. The water mark decoder determines whether a watermark was decoded to obtain a decoded watermark. The watermark decoder outputs a result based on the determination whether the watermark was decoded through application of the decoder machine learning model to the input image that includes outputting a zoomed output decoded through application of the decoder machine learning model to the input image.

Abstract: Methods of encoding and decoding for video data are described for encoding or decoding multi-level significance maps. Distinct context sets may be used for encoding the significant-coefficient flags in different regions of the transform unit. In a fixed case, the regions are defined by coefficient group borders. In one example, the upper-left coefficient group is a first region and the other coefficient groups are a second region. In a dynamic case, the regions are defined by coefficient group borders, but the encoder and decoder dynamically determine in which region each coefficient group belongs. Coefficient groups may be assigned to one region or another based on, for example, whether their respective significant-coefficient-group flags were inferred or not.

Abstract: Methods of encoding and decoding for video data are describe in which significance maps are encoded and decoded using non-spatially-uniform partitioning of the map into parts, wherein the bit positions within each part are associated with a given context. Example partition sets and processes for selecting from amongst predetermined partition sets and communicating the selection to the decoder are described.

Abstract: Methods of encoding and decoding for video data are described for encoding or decoding coefficients for a transform unit. In particular, the sign bits for the non-zero coefficients are encoded using sign bit hiding. Two or more sets of coefficients are defined for the transform unit and a sign bit may be hidden for each set, subject to satisfaction of a threshold test. The sets may correspond to coefficient groups that are otherwise used in multi-level significance map encoding and decoding.

Abstract: Decoding a current block includes decoding a subset of quantized transform coefficients of a quantized transform block using a first scan order. A second scan order is determined based on the subset of the quantized transform coefficients. Remaining quantized transform coefficients of the quantized transform block are decoded based on the second scan order. A context model for decoding an intra-prediction mode is determined based on at least the subset of the quantized transform coefficients. The intra-prediction mode is decoded based on the context model. The current block is obtained based on the quantized transform coefficients and the intra-prediction mode.

Abstract: Methods of encoding and decoding for video data are describe in which significance maps are encoded and decoded using non-spatially-uniform partitioning of the map into parts, wherein the bit positions within each part are associated with a given context. Example partition sets and processes for selecting from amongst predetermined partition sets and communicating the selection to the decoder are described.

Abstract: Systems and methods of enforcing policies in a computer environment for content distribution using pointwise mutual information (PMI) based clustering are provided. The system can maintain a network of nodes representing a plurality of assets. Upon detecting that an asset is associated with a policy label, the system can identify attributes of the asset and compute a PMI score indicating whether nodes of the network sharing the attributes belong to a single content source. Upon determining that the PMI score exceeds a predefined threshold value, the system can identify a cluster of nodes including the nodes sharing the attributes. The system can tag the cluster, for example, as being associated with a content source that is associated with the policy label.

Abstract: A method at a network element for monitoring user plane traffic for a user equipment, the method including configuring a set of characteristics and a range of values for each of the set of characteristics for user plane traffic between the user equipment and the network element; monitoring user plane traffic for the user equipment at the network element, the monitoring determining whether at least one characteristic of the user plane traffic falls outside of the configured range of a values, resulting in a characteristic violation; and if the at least one characteristic of the user plane traffic falls outside the configured range of a values, performing an action resulting from the characteristic violation.

Abstract: Entropy coding of blocks of image data may include partitioning a block of video data into a plurality of groups of elements; decoding, using an entropy decoder, data from an encoded bitstream to obtain elements of a first group from the plurality of groups of elements; determining a category based on the elements of the first group; based on the category, selecting a context for an element from a second group from the plurality of groups of elements; and decoding, using the entropy decoder using the context, data from the encoded bitstream to obtain the element of the second group from the plurality of groups of elements.

Abstract: Decoding a transform block includes decoding a first group of coefficients of the transform block using a first scan order. The first group includes of first coefficients of a first row along a first edge of the transform block and second coefficients of a first column that is along a second edge of the transform block. The first group is used to determine a second scan order for decoding a second group of coefficients of the transform block. The second group includes remaining coefficients of the transform block and does not include any coefficient of the first group. The second group is decoded using the second scan order.

Abstract: Methods of encoding and decoding for video data are described in which multi-level significance maps are used in the encoding and decoding processes. The significant-coefficient flags that form the significance map are grouped into contiguous groups, and a significant-coefficient-group flag signifies for each group whether that group contains no non-zero significant-coefficient flags. If there are no non-zero significant-coefficient flags in the group, then the significant-coefficient-group flag is set to zero. The set of significant-coefficient-group flags is encoded in the bitstream. Any significant-coefficient flags that fall within a group that has a significant-coefficient-group flag that is non-zero are encoded in the bitstream, whereas significant-coefficient flags that fall within a group that has a significant-coefficient-group flag that is zero are not encoded in the bitstream.