A method of video decoding performed in a video decoder is provided. In the method, a bit stream including coded bits of bins of syntax elements is received. The syntax elements correspond to residues of a region of a transform skipped block in a coded picture. Context modeling is performed to determine a context model for each of a number of the bins of syntax elements of the region. The number of the bins of syntax elements that are context coded does not exceed a maximum number of context coded bins set for the region. The coded bits of the number of the bins of syntax elements are decoded based on the determined context models.

Embodiments of the present disclosure provide a method for transcoding a video. An input attribute of a video is obtained and a target attribute is obtained. A segment transcoding speed of the video is determined based on the input attribute and the target attribute. The segment transcoding speed indicates a transcoding speed of a video segment. The number of video segments of the video is determined based on a preset target transcoding speed and the segment transcoding speed. The video is segmented based on a video length of the video and the number of video segments to obtain the video segments. The video segments are transcoded based on the segment transcoding speed.

Embodiments of the present disclosure provide a method for transcoding a video. An input attribute of a video is obtained and a target attribute is obtained. A segment transcoding speed of the video is determined based on the input attribute and the target attribute. The segment transcoding speed indicates a transcoding speed of a video segment. The number of video segments of the video is determined based on a preset target transcoding speed and the segment transcoding speed. The video is segmented based on a video length of the video and the number of video segments to obtain the video segments. The video segments are transcoded based on the segment transcoding speed.

A data compression apparatus includes: a compression unit that performs a compression process on output data that is sequentially outputted as time series data by a monitoring apparatus for monitoring a monitoring target range; an output unit that outputs the output data on which the compression process is performed, to a data processing apparatus that performs an object detection process for detecting an object that exists in the monitoring target range by using the output data; and a setting unit that sets a compression ratio used in the compression unit, on the basis of an accuracy information indicating a relationship between a compression ratio at which the output data is compressed and a detection accuracy of the object by the object detection process performed by using the output data compressed at the compression ratio.

Video streams uploaded to a video hosting platform are transcoded using quality-normalized transcoding parameters dynamically selected using a learning model. Video frames of a video stream are processed using the learning model to determine bitrate and quality score pairs for some or all possible transcoding resolutions. The listing of bitrate and quality score pairs determined for each resolution is processed to determine a set of transcoding parameters for transcoding the video stream into each resolution. The bitrate and quality score pairs of a given listing may be processed using one or more predefined thresholds, which may, in some cases, refer to a weighted distribution of resolutions according to watch times of videos of the video hosting platform. The video stream is then transcoded into the various resolutions using the set of transcoding parameters selected for each resolution.

Video streams uploaded to a video hosting platform are transcoded using quality-normalized transcoding parameters dynamically selected using a learning model. Video frames of a video stream are processed using the learning model to determine bitrate and quality score pairs for some or all possible transcoding resolutions. The listing of bitrate and quality score pairs determined for each resolution is processed to determine a set of transcoding parameters for transcoding the video stream into each resolution. The bitrate and quality score pairs of a given listing may be processed using one or more predefined thresholds, which may, in some cases, refer to a weighted distribution of resolutions according to watch times of videos of the video hosting platform. The video stream is then transcoded into the various resolutions using the set of transcoding parameters selected for each resolution.

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.

Provided is a method for encoding video data. The method includes: acquiring video data; configuring an encoding parameter of an encoder; determining a first time interval between two adjacent video frames in a group of pictures based on the encoding parameter; and adjusting a number of video frames in the group of pictures based on the first time interval by encoding the video data based on the encoding parameter. A device and a storage medium are also provided.

A method of decompression includes decompressing a compressed image according to a power curve to generate a partially decompressed image, wherein the compressed image is decompressed from a second bit depth that is lower than a first bit-depth at which the image was generated.