Systems and methods are presented for reliable transmission of time-sensitive data. In particular, various embodiments provide for the generation of compressed sequential data, where individual instances of a sequence represent differentials from prior instances in that sequence. In order to reduce an amount of data that needs to be transmitted, instances of data (such as individual video frames) can be provided using a prior video frame as a reference, sending only data for those pixel locations where the pixel value differs from the reference frame. A reference frame can include a previously-received and successfully-decoded frame, in order to minimize the impact of dropped, incomplete, or corrupted frames. In order to further reduce data transmission requirements, a reference frame can be selected which is determined to be optimal for the current frame, such as may represent a least amount of data to be transmitted for a given frame.

A method at a client device for mitigating motion judder in frames of an image due to display data for a particular frame being unavailable at a required time at the client device. The method involves receiving (S35) the display data for a current frame n, and generating (S3Y3) the current frame n from the received display data. Motion vectors for some elements of the image in the current frame n are obtained (S3Y1). If it is determined that display data for the next frame n+1 is not available, the next frame n+1 is generated (S3N4) from either the current frame n or a previous frame n−m, where m=1, 2, 3, etc, adjusted based on an extrapolation (S3N3) of the motion vectors for the elements of the image in either the current frame n or the previous frame n−m.

At least a method and an apparatus are presented for efficiently encoding or decoding video. For example, one or more chroma residual scaling parameters are determined based one or more luma mapping parameters and based on a corrective value of the one or more chroma residual scaling parameters. The video is encoded or decoded based on the determined one or more chroma residual scaling parameters.

At least a method and an apparatus are presented for efficiently encoding or decoding video. For example, one or more chroma residual scaling parameters are determined based one or more luma mapping parameters and based on a corrective value of the one or more chroma residual scaling parameters. The video is encoded or decoded based on the determined one or more chroma residual scaling parameters.

This application provides a wireless communication method and a communication apparatus. The method includes a terminal device receives a data frame from a second device by using a first device, and notifies the first device when the terminal device determines that a communication data block from the first device is not successfully received. After determining a protocol data packet corresponding to the communication data block, the first device notifies the second device that the first protocol data packet is not successfully transmitted. After determining a data unit that is in the data frame and that corresponds to the communication data block, the terminal device notifies the second device through second-step feedback.

A method of signing prediction-coded video data, comprising: obtaining a coded video sequence including at least one I-frame (I), which contains independently decodable image data, and at least one predicted frame (P1, P2, P3, P4), which contains image data decodable by reference to at least one other frame; generating a fingerprint (H.sub.I) of each I-frame; generating a fingerprint (H.sub.P) of each predicted frame by hashing a combination of data derived from the predicted frame and data derived from an I-frame to which the predicted frame refers directly or indirectly, wherein the fingerprint of the predicted frame is independent of any further predicted frame to which the predicted frame refers directly or indirectly; and providing a signature of the video sequence including the generated fingerprints.

Techniques are disclosed for coding video data in which frames from a video source are partitioned into a plurality of tiles of common size, and the tiles are coded as a virtual video sequence according to motion-compensated prediction, each tile treated as having respective temporal location of the virtual video sequence. The coding scheme permits relative allocation of coding resources to tiles that are likely to have greater significance in a video coding session, which may lead to certain tiles that have low complexity or low motion content to be skipped during coding of the tiles for select source frames. Moreover, coding of the tiles may be ordered to achieve low coding latencies during a coding session.

Techniques are disclosed for coding video data in which frames from a video source are partitioned into a plurality of tiles of common size, and the tiles are coded as a virtual video sequence according to motion-compensated prediction, each tile treated as having respective temporal location of the virtual video sequence. The coding scheme permits relative allocation of coding resources to tiles that are likely to have greater significance in a video coding session, which may lead to certain tiles that have low complexity or low motion content to be skipped during coding of the tiles for select source frames. Moreover, coding of the tiles may be ordered to achieve low coding latencies during a coding session.

A method includes maintaining a set of parameters or weights derived through online learning for a neural net; transmitting an update of the parameters or weights to a decoder; deriving a first prediction block based on an output of the neural net using the parameters or weights; deriving a first encoded prediction error block through encoding a difference of the first prediction block and a first input block; encoding the first encoded prediction error block into a bitstream; deriving a reconstructed prediction error block based on the first encoded prediction error block; deriving a second prediction block based on an output of the neural net using the parameters or weights and the reconstructed prediction error block; deriving a second encoded prediction error block through encoding a difference of the second prediction block and a second input block; and encoding the second encoded prediction error block into a bitstream.

A method includes maintaining a set of parameters or weights derived through online learning for a neural net; transmitting an update of the parameters or weights to a decoder; deriving a first prediction block based on an output of the neural net using the parameters or weights; deriving a first encoded prediction error block through encoding a difference of the first prediction block and a first input block; encoding the first encoded prediction error block into a bitstream; deriving a reconstructed prediction error block based on the first encoded prediction error block; deriving a second prediction block based on an output of the neural net using the parameters or weights and the reconstructed prediction error block; deriving a second encoded prediction error block through encoding a difference of the second prediction block and a second input block; and encoding the second encoded prediction error block into a bitstream.