Provided are a method and apparatus for encoding a video, and a storage medium, which relate to the field of cloud computing, in particular to the video encoding technology. The method includes: acquiring an actual encoding data amount and an encoding frame number of an encoder in a historical time period; calculating a desired encoding data amount in the historical time period according to a code rate of the encoder and the encoding frame number; and comparing the actual encoding data amount and the desired encoding data amount, and adjusting a quantization parameter of the encoder according to a comparison result to instruct the encoder to continue encoding using the adjusted quantization parameter.

Provided are a method and apparatus for encoding a video, and a storage medium, which relate to the field of cloud computing, in particular to the video encoding technology. The method includes: acquiring an actual encoding data amount and an encoding frame number of an encoder in a historical time period; calculating a desired encoding data amount in the historical time period according to a code rate of the encoder and the encoding frame number; and comparing the actual encoding data amount and the desired encoding data amount, and adjusting a quantization parameter of the encoder according to a comparison result to instruct the encoder to continue encoding using the adjusted quantization parameter.

A disclosed system may include a hardware distortion data pipeline that may include (1) a quantization module that generates a quantized data set, (2) an inverse quantization module that generates, from the quantized data set, an inverse quantized data set by executing an inverse quantization of the quantized data set, and (3) an inverse transformation module that generates an inversely transformed data set by executing an inverse transformation of the inverse quantized data set. The system may also include a hardware determination pipeline that determines a distortion metric based on the inversely transformed data set and the residual frame data set, and a hardware token rate pipeline that determines, based on the quantized data set, a token rate for an encoding of the residual frame data set via a video encoding pipeline. Various other methods, systems, and computer-readable media are also disclosed.

A disclosed system may include a hardware distortion data pipeline that may include (1) a quantization module that generates a quantized data set, (2) an inverse quantization module that generates, from the quantized data set, an inverse quantized data set by executing an inverse quantization of the quantized data set, and (3) an inverse transformation module that generates an inversely transformed data set by executing an inverse transformation of the inverse quantized data set. The system may also include a hardware determination pipeline that determines a distortion metric based on the inversely transformed data set and the residual frame data set, and a hardware token rate pipeline that determines, based on the quantized data set, a token rate for an encoding of the residual frame data set via a video encoding pipeline. Various other methods, systems, and computer-readable media are also disclosed.

An apparatus can include a prediction mode decoding module configured to derive a luma intra prediction mode and a chroma intra prediction mode; a prediction size determining module configured to determine a size of a luma transform unit and a size of a chroma transform unit using transform size information; a reference pixel generating module configured to generate referential pixels if at least one reference pixel is unavailable; a reference pixel filtering module configured to adaptively filter the reference pixels of a current luma block based on the luma intra prediction mode and the size of the luma transform unit, and not to filter the reference pixels of a current chroma block; a prediction block generating module configured to generate prediction blocks of the current luma block and the current chroma block; a residual bock generating module configured to generate a luma residual block and a chroma residual block; and an adder.

An apparatus can include a prediction mode decoding module configured to derive a luma intra prediction mode and a chroma intra prediction mode; a prediction size determining module configured to determine a size of a luma transform unit and a size of a chroma transform unit using transform size information; a reference pixel generating module configured to generate referential pixels if at least one reference pixel is unavailable; a reference pixel filtering module configured to adaptively filter the reference pixels of a current luma block based on the luma intra prediction mode and the size of the luma transform unit, and not to filter the reference pixels of a current chroma block; a prediction block generating module configured to generate prediction blocks of the current luma block and the current chroma block; a residual bock generating module configured to generate a luma residual block and a chroma residual block; and an adder.

Described is picture segmentation through columns and slices in video encoding and decoding. A video picture is divided into a plurality of columns, each column covering only a part of the video picture in a horizontal dimension. All coded tree blocks (“CTBs”) belonging to a slice may belong to one or more columns. The columns may be used to break the same or different prediction or in-loop filtering mechanisms of the video coding, and the CTB scan order used for encoding and/or decoding may be local to a column. Column widths may be indicated in a parameter set and/or may be adjusted at the slice level. At the decoder, column width may be parsed from the bitstream, and slice decoding may occur in one or more columns.

Described is picture segmentation through columns and slices in video encoding and decoding. A video picture is divided into a plurality of columns, each column covering only a part of the video picture in a horizontal dimension. All coded tree blocks (“CTBs”) belonging to a slice may belong to one or more columns. The columns may be used to break the same or different prediction or in-loop filtering mechanisms of the video coding, and the CTB scan order used for encoding and/or decoding may be local to a column. Column widths may be indicated in a parameter set and/or may be adjusted at the slice level. At the decoder, column width may be parsed from the bitstream, and slice decoding may occur in one or more columns.

A method performed by a decoder for decoding a bitstream comprising a picture parameter set, PPS, and a first set of slices. The method includes obtaining the picture parameter set. The method also includes decoding a syntax element included in the picture parameter set to obtain an indicator value. The decoder is configured such that if the indicator value is set to a first value then the decoder determines that a picture header included in the bitstream comprises a parameter value corresponding to a particular parameter, otherwise the decoder determines that each slice included in the first set of slices comprises a parameter value corresponding to the particular parameter. If the picture header comprises the parameter value corresponding to the particular parameter, then this parameter value is used to decode slice data of each slice included in the first set of slices.

A system for decoding a video bitstream includes receiving a reference picture set associated with a frame including a set of reference picture identifiers. The reference picture set identifies one or more reference pictures to be used for inter-prediction of the frame based upon its associated least significant bits of a picture order count based upon the reference picture identifiers. The one or more reference pictures is a second or greater previous frame to the frame having the matching reference picture identifier.