An image processing method is provided. The method includes acquiring a video. The method includes using an object detection engine to detect a person in the video. The object detection engine is integrated with an image signal processing pipeline. The method includes transmitting the video over a network. The method includes determining that the detected person has moved less than a pre-set distance. The method includes, responsive to the determining, pausing transmission of the video. An embedded image processor including an object detection engine is also provided.

An image processing method is provided. The method includes acquiring a video. The method includes using an object detection engine to detect a person in the video. The object detection engine is integrated with an image signal processing pipeline. The method includes transmitting the video over a network. The method includes determining that the detected person has moved less than a pre-set distance. The method includes, responsive to the determining, pausing transmission of the video. An embedded image processor including an object detection engine is also provided.

A transmission device according to an embodiment includes one or more processors. The processors divide a plurality of pieces of transmission data to be transmitted into first data and second data. The processors transmit the first data to a server device configured to distribute the transmission data to a reception device. The processors store the second data in storage. The processors receive, from the reception device or the server device, a request for transmission of the second data. The processors transmit the second data to the server device in accordance with the request for transmission.

Provided are a video decoding method and apparatus, in which, during video encoding and decoding processes: in which a first bin of a sub-block merge index indicating a candidate motion vector of a sub-block merge mode is obtained, the first bin being arithmetic-encoded using a context model, a second arithmetic-encoded in a bypass mode is obtained based on a first value obtained by arithmetic-decoding the first bin by using the context model, a second value is obtained by arithmetic-decoding the second bin in the bypass mode, and prediction on a current block is performed in the sub-block merge mode, based on the first value and the second value.

A prediction method for an image block comprising a first prediction sub-block and a second prediction sub-block, the prediction method comprising: parsing a first index from a bitstream, wherein the first index is used to obtain prediction information of the first prediction sub-block; parsing a second index from the bitstream; comparing the first index with the second index; adjusting the second index in the event that the second index is equal to or greater than the first index; and obtaining prediction information of the second prediction sub-block according to the adjusted second index.

A prediction method for an image block comprising a first prediction sub-block and a second prediction sub-block, the prediction method comprising: parsing a first index from a bitstream, wherein the first index is used to obtain prediction information of the first prediction sub-block; parsing a second index from the bitstream; comparing the first index with the second index; adjusting the second index in the event that the second index is equal to or greater than the first index; and obtaining prediction information of the second prediction sub-block according to the adjusted second index.

A method for in-loop sample offset filtering in a video decoder is disclosed. The method includes obtaining at least one statistical property associated with reconstructed samples of at least a first color component in a current reconstructed data block of a video stream, selecting a target sample offset filter among a plurality of sample offset filters based on the at least one statistical property, the target sample offset filter comprising a nonlinear mapping between sample delta measures and sample offset values, and filtering a current sample in a second color component of the current reconstructed data block using the target sample offset filter and reference samples in a third color component of the current reconstructed data block to generate a filtered reconstructed sample of the current sample.

A method for in-loop sample offset filtering in a video decoder is disclosed. The method includes obtaining at least one statistical property associated with reconstructed samples of at least a first color component in a current reconstructed data block of a video stream, selecting a target sample offset filter among a plurality of sample offset filters based on the at least one statistical property, the target sample offset filter comprising a nonlinear mapping between sample delta measures and sample offset values, and filtering a current sample in a second color component of the current reconstructed data block using the target sample offset filter and reference samples in a third color component of the current reconstructed data block to generate a filtered reconstructed sample of the current sample.

Various embodiments of the present disclosure provide a segment-wise prediction machine learning framework. In one example, an embodiment provides for generating, using a segment-wise prediction machine learning framework, and based at least in part on a document segment for an input segment and a respective predictive code for the input segment, a segment-wise prediction score for the input segment. The segment-wise prediction machine learning framework may comprise a text embedding machine learning model and may be configured to generate a segment-wise prediction score for the input segment based at least in part on a document embedding for the input segment and a code embedding for the respective predictive code for the input segment. Additionally, the text embedding machine learning model may be trained as part of a code prediction machine learning model that is configured to generate, for a particular input document data object, a selected code subset.

Various embodiments of the present disclosure provide a segment-wise prediction machine learning framework. In one example, an embodiment provides for generating, using a segment-wise prediction machine learning framework, and based at least in part on a document segment for an input segment and a respective predictive code for the input segment, a segment-wise prediction score for the input segment. The segment-wise prediction machine learning framework may comprise a text embedding machine learning model and may be configured to generate a segment-wise prediction score for the input segment based at least in part on a document embedding for the input segment and a code embedding for the respective predictive code for the input segment. Additionally, the text embedding machine learning model may be trained as part of a code prediction machine learning model that is configured to generate, for a particular input document data object, a selected code subset.