A method for determining an initial bitrate for a communication includes receiving a communication request to establish a digital communication between a first user device and a second user device associated with a plurality of features including a geographical identifier identifying a geographical location associated with the first user device, a first network type connection associated with the first user device, a second network type connection associated with the second user device, and an average bitrate for a previous digital communication of the first user device. The method includes determining, using an initial bitrate predictor model configured to receive the plurality of features as feature inputs, an initial bitrate for the digital communication between the first user device and the second user device, and establishing the digital communication between the first user device and the second user device at the determined initial bitrate.

A method for determining an initial bitrate for a communication includes receiving a communication request to establish a digital communication between a first user device and a second user device associated with a plurality of features including a geographical identifier identifying a geographical location associated with the first user device, a first network type connection associated with the first user device, a second network type connection associated with the second user device, and an average bitrate for a previous digital communication of the first user device. The method includes determining, using an initial bitrate predictor model configured to receive the plurality of features as feature inputs, an initial bitrate for the digital communication between the first user device and the second user device, and establishing the digital communication between the first user device and the second user device at the determined initial bitrate.

Methods and apparatuses are disclosed herein for parameter estimation for metrology. An example method at least includes optimizing, using a parameter estimation network, a parameter set to fit a feature in an image based on one or more models of the feature, the parameter set defining the one or more models, and providing metrology data of the feature in the image based on the optimized parameter set.

Methods and apparatuses are disclosed herein for parameter estimation for metrology. An example method at least includes optimizing, using a parameter estimation network, a parameter set to fit a feature in an image based on one or more models of the feature, the parameter set defining the one or more models, and providing metrology data of the feature in the image based on the optimized parameter set.

This disclosure describes systems, methods, and devices related to bit-rate-based variable accuracy level encoding. A device may generate a list of encodes based on pairs of resolutions and quantization parameters (QP) associated with one or more video segments received from a source. The device may generate an estimated bit rate associated with the one or more video segments based on an analysis of the one or more video segments. The device may utilize an accuracy level of encoding for an encoder based on the estimated bit rate. The device may encode the one or more video segments based on the accuracy level of encoding.

A picture coding method includes: performing a first derivation process for deriving a first merging candidate which includes a candidate set of a prediction direction, a motion vector, and a reference picture index for use in coding of a current block;

performing a second derivation process for deriving a second merging candidate; selecting a merging candidate to be used in the coding of the current block from among the first and second merging candidates; and attaching an index for identifying the selected merging candidate to the bitstream; wherein the first derivation process is performed so that a total number of the first merging candidates does not exceed a predetermined number, and the second derivation process is performed when the total number of the first merging candidates is less than a predetermined maximum number of merging candidates.

This disclosure describes systems, methods, and devices related to bit-rate-based hybrid encoding. A device may generate a list of encodes based on pairs of resolution and quantization parameters (QP) pairs associated with one or more video segments received from a source. The device may generate an estimated bit rate associated with the one or more video segments based on an analysis of the one or more video segments. The device may compare the estimated bit rate to a threshold. The device may switch between a software encoder and a hardware encoder based on the comparison of the estimated bit rate to the threshold. The device may encode each of the one or more video segments for transmission using the hardware encoder or the software encoder.

In example embodiments, an apparatus, a method, and a computer program product are provided. The apparatus comprises at least one processor; and at least one non-transitory memory including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to perform determine a processing order of building blocks to encode or decode a media item; and determine a number of processing steps required to encode or decode the media item; wherein the processing order of building blocks and the number of processing steps are determined based on a content of the media item by using a guided probability model based on a neural network.

In example embodiments, an apparatus, a method, and a computer program product are provided. The apparatus comprises at least one processor; and at least one non-transitory memory including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to perform determine a processing order of building blocks to encode or decode a media item; and determine a number of processing steps required to encode or decode the media item; wherein the processing order of building blocks and the number of processing steps are determined based on a content of the media item by using a guided probability model based on a neural network.

Systems and methods for reducing latency through motion estimation and compensation techniques are disclosed. The systems and methods include a client device that uses transmitted lookup tables from a remote server to match user input to motion vectors, and tag and sum those motion vectors. When a remote server transmits encoded video frames to the client, the client decodes those video frames and applies the summed motion vectors to the decoded frames to estimate motion in those frames. In certain embodiments, the systems and methods generate motion vectors at a server based on predetermined criteria and transmit the generated motion vectors and one or more invalidators to a client, which caches those motion vectors and invalidators. The server instructs the client to receive input from a user, and use that input to match to cached motion vectors or invalidators. Based on that comparison, the client then applies the matched motion vectors or invalidators to effect motion compensation in a graphic interface. In other embodiments, the systems and methods cache repetitive motion vectors at a server, which transmits a previously generated motion vector library to a client. The client stores the motion vector library, and monitors for user input data. The server instructs the client to calculate a motion estimate from the input data and instructs the client to update the stored motion vector library based on the input data, so that the client applies the stored motion vector library to initiate motion in a graphic interface prior to receiving actual motion vector data from the server. In this manner, latency in video data streams is reduced.