Patent classifications
H04N19/89
Guaranteed Data Compression
A method of converting 10-bit pixel data (e.g. 10:10:10:2 data) into 8-bit pixel data involves converting the 10-bit values to 7-bits or 8-bits and generating error values for each of the converted values. Two of the 8-bit output channels comprise a combination of a converted 7-bit value and one of the bits from the fourth input channel. A third 8-bit output channel comprises the converted 8-bit value and the fourth 8-bit output channel comprises the error values. In various examples, the bits of the error values may be interleaved when they are packed into the fourth output channel.
Machine learning techniques for component-based image preprocessing
In various embodiments, a training application trains a machine learning model to preprocess images. In operation, the training application computes a chroma sampling factor based on a downscaling factor and a chroma subsampling ratio. The training application executes a machine learning model that is associated with the chroma sampling factor on data that corresponds to both an image and a first chroma component to generate preprocessed data corresponding to the first chroma component. Based on the preprocessed data, the training application updates at least one parameter of the machine learning model to generate a trained machine learning model that is associated with the first chroma component.
Machine learning techniques for component-based image preprocessing
In various embodiments, a training application trains a machine learning model to preprocess images. In operation, the training application computes a chroma sampling factor based on a downscaling factor and a chroma subsampling ratio. The training application executes a machine learning model that is associated with the chroma sampling factor on data that corresponds to both an image and a first chroma component to generate preprocessed data corresponding to the first chroma component. Based on the preprocessed data, the training application updates at least one parameter of the machine learning model to generate a trained machine learning model that is associated with the first chroma component.
SYSTEM AND METHOD FOR CORRECTING NETWORK LOSS OF DATA
A reference-order AL-FEC system for recovering network video data packet loss during real-time video communication includes a packetizer, a reference-order AL-FEC encoder, a reference-order AL-FEC decoder and a depacketizer. The packetizer constructs source symbols from source packets of a current frame. The encoder generates a repair symbol from the source symbols of the current frame and other reference frames based on the reference-order, not time-order, between the frames within an encoding window. The encoder also generates a repair packet based on the repair symbol. The decoder recovers a lost source symbol based on the source symbols of the frames of the encoding window and the repair symbol by decoding the repair packet. The decoding is achieved by solving a linear system of the repair symbol.
SYSTEM AND METHOD FOR CORRECTING NETWORK LOSS OF DATA
A reference-order AL-FEC system for recovering network video data packet loss during real-time video communication includes a packetizer, a reference-order AL-FEC encoder, a reference-order AL-FEC decoder and a depacketizer. The packetizer constructs source symbols from source packets of a current frame. The encoder generates a repair symbol from the source symbols of the current frame and other reference frames based on the reference-order, not time-order, between the frames within an encoding window. The encoder also generates a repair packet based on the repair symbol. The decoder recovers a lost source symbol based on the source symbols of the frames of the encoding window and the repair symbol by decoding the repair packet. The decoding is achieved by solving a linear system of the repair symbol.
System and method for correcting network loss of data
A reference-order AL-FEC system for recovering network video data packet loss during real-time video communication includes a packetizer, a reference-order AL-FEC encoder, a reference-order AL-FEC decoder and a depacketizer. The packetizer constructs source symbols from source packets of a current frame. The encoder generates a repair symbol from the source symbols of the current frame and other reference frames based on the reference-order, not time-order, between the frames within an encoding window. The encoder also generates a repair packet based on the repair symbol. The decoder recovers a lost source symbol based on the source symbols of the frames of the encoding window and the repair symbol by decoding the repair packet. The decoding is achieved by solving a linear system of the repair symbol.
System and method for correcting network loss of data
A reference-order AL-FEC system for recovering network video data packet loss during real-time video communication includes a packetizer, a reference-order AL-FEC encoder, a reference-order AL-FEC decoder and a depacketizer. The packetizer constructs source symbols from source packets of a current frame. The encoder generates a repair symbol from the source symbols of the current frame and other reference frames based on the reference-order, not time-order, between the frames within an encoding window. The encoder also generates a repair packet based on the repair symbol. The decoder recovers a lost source symbol based on the source symbols of the frames of the encoding window and the repair symbol by decoding the repair packet. The decoding is achieved by solving a linear system of the repair symbol.
DIRECTED INTERPOLATION AND DATA POST-PROCESSING
An encoding device evaluates a plurality of processing and/or post-processing algorithms and/or methods to be applied to a video stream, and signals a selected method, algorithm, class or category of methods/algorithms either in an encoded bitstream or as side information related to the encoded bitstream. A decoding device or post-processor utilizes the signaled algorithm or selects an algorithm/method based on the signaled method or algorithm. The selection is based, for example, on availability of the algorithm/method at the decoder/post-processor and/or cost of implementation. The video stream may comprise, for example, downsampled multiplexed stereoscopic images and the selected algorithm may include any of upconversion and/or error correction techniques that contribute to a restoration of the downsampled images.
Determination of Block Vector Predictor Candidate List
Encoding and/or decoding a block of a video frame may be based on a previously decoded reference block in the same frame or in a different frame. The reference block may be indicated by a block vector (BV). The BV may be encoded as a difference between a block vector predictor (BVP) and the BV. A list of BVP candidates may be generated and/or augmented based on a decoded region of a video frame and/or dimensions of the block. For example, zero-valued candidate BVPs, in the list, may be replaced with other candidate BVPs generated based on a decoded region of a video frame and/or dimensions of the block.
Determination of Block Vector Predictor Candidate List
Encoding and/or decoding a block of a video frame may be based on a previously decoded reference block in the same frame or in a different frame. The reference block may be indicated by a block vector (BV). The BV may be encoded as a difference between a block vector predictor (BVP) and the BV. A list of BVP candidates may be generated and/or augmented based on a decoded region of a video frame and/or dimensions of the block. For example, zero-valued candidate BVPs, in the list, may be replaced with other candidate BVPs generated based on a decoded region of a video frame and/or dimensions of the block.