H04N19/127

VIDEO DECODER WITH HARDWARE SHARED BETWEEN DIFFERENT PROCESSING CIRCUITS AND ASSOCIATED VIDEO DECODING METHOD
20230069089 · 2023-03-02 · ·

A video decoder has a plurality of processing circuits, including a first processing circuit and a second processing circuit. The first processing circuit applies a first decoding process to a current coding block according to reconstructed neighbor samples, and has a local neighbor buffer for buffering the reconstructed neighbor samples used by the first decoding process. The second processing circuit applies a second decoding process to the current coding block according to at least a portion of the reconstructed neighbor samples retrieved from the local neighbor buffer, wherein the second decoding process is different from the first decoding process.

VIDEO DECODER WITH HARDWARE SHARED BETWEEN DIFFERENT PROCESSING CIRCUITS AND ASSOCIATED VIDEO DECODING METHOD
20230069089 · 2023-03-02 · ·

A video decoder has a plurality of processing circuits, including a first processing circuit and a second processing circuit. The first processing circuit applies a first decoding process to a current coding block according to reconstructed neighbor samples, and has a local neighbor buffer for buffering the reconstructed neighbor samples used by the first decoding process. The second processing circuit applies a second decoding process to the current coding block according to at least a portion of the reconstructed neighbor samples retrieved from the local neighbor buffer, wherein the second decoding process is different from the first decoding process.

PROCESSING MEDIA USING NEURAL NETWORKS
20230111773 · 2023-04-13 ·

An encoder may determine a plurality of coding units associated with a frame of a media file and a plurality of prediction units associated with the frame of the media file. The encoder may determine, based on the plurality of coding units associated with the frame and the plurality of prediction units associated with the frame, and based on a training of the encoder using one or more neural networks, that a particular region of the frame can be encoded using one or more encoding characteristics that are different than the encoding characteristics of one or more other particular regions of the frame. The encoder may allocate one or more encoding resources to the particular region of the frame based on the one or more encoding characteristics of the particular region of the frame in order to reduce the overall media bitrate.

PROCESSING MEDIA USING NEURAL NETWORKS
20230111773 · 2023-04-13 ·

An encoder may determine a plurality of coding units associated with a frame of a media file and a plurality of prediction units associated with the frame of the media file. The encoder may determine, based on the plurality of coding units associated with the frame and the plurality of prediction units associated with the frame, and based on a training of the encoder using one or more neural networks, that a particular region of the frame can be encoded using one or more encoding characteristics that are different than the encoding characteristics of one or more other particular regions of the frame. The encoder may allocate one or more encoding resources to the particular region of the frame based on the one or more encoding characteristics of the particular region of the frame in order to reduce the overall media bitrate.

HIGH SPEED DATA COMPRESSION METHODS AND SYSTEMS
20230114644 · 2023-04-13 ·

In one aspect, a method of fast data compression operates on input data comprising plural J-bit bytes (e.g., 16-bit bytes). The method computes a first difference value between one pair of the input J-bit bytes, and determines that this first difference value can be represented by K bits, where K<J. The method further computes a second difference value between a second pair of the input J-bit bytes, and determines that this second difference value can be represented by M bits, where M<K. These K- and M-bit difference values are included in a composite output data string that also includes four data tags. One tag indicates the first difference value is represented by K bits. Another indicates the second difference value is represented by M bits. The final two tags indicate the polarities of the first and second difference values. A great variety of other features and arrangements are also detailed.

HIGH SPEED DATA COMPRESSION METHODS AND SYSTEMS
20230114644 · 2023-04-13 ·

In one aspect, a method of fast data compression operates on input data comprising plural J-bit bytes (e.g., 16-bit bytes). The method computes a first difference value between one pair of the input J-bit bytes, and determines that this first difference value can be represented by K bits, where K<J. The method further computes a second difference value between a second pair of the input J-bit bytes, and determines that this second difference value can be represented by M bits, where M<K. These K- and M-bit difference values are included in a composite output data string that also includes four data tags. One tag indicates the first difference value is represented by K bits. Another indicates the second difference value is represented by M bits. The final two tags indicate the polarities of the first and second difference values. A great variety of other features and arrangements are also detailed.

Processing media using neural networks

An encoder may determine a plurality of coding units associated with a frame of a media file and a plurality of prediction units associated with the frame of the media file. The encoder may determine, based on the plurality of coding units associated with the frame and the plurality of prediction units associated with the frame, and based on a training of the encoder using one or more neural networks, that a particular region of the frame can be encoded using one or more encoding characteristics that are different than the encoding characteristics of one or more other particular regions of the frame. The encoder may allocate one or more encoding resources to the particular region of the frame based on the one or more encoding characteristics of the particular region of the frame in order to reduce the overall media bitrate.

Processing media using neural networks

An encoder may determine a plurality of coding units associated with a frame of a media file and a plurality of prediction units associated with the frame of the media file. The encoder may determine, based on the plurality of coding units associated with the frame and the plurality of prediction units associated with the frame, and based on a training of the encoder using one or more neural networks, that a particular region of the frame can be encoded using one or more encoding characteristics that are different than the encoding characteristics of one or more other particular regions of the frame. The encoder may allocate one or more encoding resources to the particular region of the frame based on the one or more encoding characteristics of the particular region of the frame in order to reduce the overall media bitrate.

BLOCK DIMENSION SETTINGS OF TRANSFORM SKIP MODE
20230074729 · 2023-03-09 ·

Devices, systems, and methods for lossless coding for visual media coding are described. An exemplary method for video processing includes determining, based on a current video block of a video satisfying a dimension constraint, that coding modes are enabled for representing the current video block in a bitstream representation, where the dimension constraint states that a same set of allowed dimensions for the current video block is disabled for the coding modes, and where, for an encoding operation, the coding modes represent the current video block in the bitstream representation without using a transform operation, or where, for a decoding operation, the coding modes are used to obtain the current video block without using an inverse transform operation; and performing a conversion between the current video block and the bitstream representation of the video based on one of the coding modes.

BLOCK DIMENSION SETTINGS OF TRANSFORM SKIP MODE
20230074729 · 2023-03-09 ·

Devices, systems, and methods for lossless coding for visual media coding are described. An exemplary method for video processing includes determining, based on a current video block of a video satisfying a dimension constraint, that coding modes are enabled for representing the current video block in a bitstream representation, where the dimension constraint states that a same set of allowed dimensions for the current video block is disabled for the coding modes, and where, for an encoding operation, the coding modes represent the current video block in the bitstream representation without using a transform operation, or where, for a decoding operation, the coding modes are used to obtain the current video block without using an inverse transform operation; and performing a conversion between the current video block and the bitstream representation of the video based on one of the coding modes.