A method of processing image data for transmittal to a display device involves receiving a frame of image data, the frame being divided into tile groups composed of tiles of pixels, each having a number of colour component values of a first colour space. Each tile includes a number of colour component planes of the first colour space having the colour component values for the pixels forming the tile. Each tile group is processed in an execution unit, formed by arithmetic logic units (ALUs) and a local shared memory, where each ALU includes dedicated register space for use solely by the ALU, and each tile of each tile group is processed by a number of the ALUs of the execution unit. Each ALU performs a reversible colour transformation (S1) on the colour component values from the first colour space to a second colour space and discards the remaining colour component values and then performs a discrete wavelet transformation (S2) on the colour component values of one colour component plane of the second colour space to produce wavelet coefficients, which are quantized (S3) and entropy encoded (S4) into variable length codes. The variable length codes for all the tiles of the tile group are assembled together for transmittal to a display device. Each ALU stores the data at each stage of the processing in its dedicated register space but not in the local shared memory of the execution unit.

A method includes obtaining, using at least one image sensor of an electronic device, a first image frame and multiple second image frames of a scene. Each of the second image frames has an exposure time different from an exposure time of the first image frame. The method also includes encoding, using at least one processor, each of the first image frame and the second image frames using a convolutional neural network to generate a corresponding feature map. The method further includes aligning, using the at least one processor, encoded features of the feature map corresponding to the first image frame with encoded features of the feature maps corresponding to the second image frames.

A method includes obtaining, using at least one image sensor of an electronic device, a first image frame and multiple second image frames of a scene. Each of the second image frames has an exposure time different from an exposure time of the first image frame. The method also includes encoding, using at least one processor, each of the first image frame and the second image frames using a convolutional neural network to generate a corresponding feature map. The method further includes aligning, using the at least one processor, encoded features of the feature map corresponding to the first image frame with encoded features of the feature maps corresponding to the second image frames.

A computing device performs a method of decoding video data by receiving bitstream encoding a chroma block, a corresponding luma block, neighboring luma samples, and neighboring chroma samples; decoding the luma block, the plurality of neighboring luma samples, and the plurality of neighboring chroma samples; selecting a group of reference luma samples and a group of reference chroma samples; computing a threshold luma value from the plurality of reconstructed neighboring luma samples, and a threshold chroma value from the plurality of reconstructed neighboring chroma samples; determining a maximum luma value and a minimum luma value from the group of the reference luma samples; generating multi-model linear model (MMLM) including a first linear model between the minimum luma value and the threshold luma value, and a second linear model between the threshold luma value and the maximum luma value; and reconstructing the chroma block from the luma block using MMLM.

A computing device performs a method of decoding video data by receiving bitstream encoding a chroma block, a corresponding luma block, neighboring luma samples, and neighboring chroma samples; decoding the luma block, the plurality of neighboring luma samples, and the plurality of neighboring chroma samples; selecting a group of reference luma samples and a group of reference chroma samples; computing a threshold luma value from the plurality of reconstructed neighboring luma samples, and a threshold chroma value from the plurality of reconstructed neighboring chroma samples; determining a maximum luma value and a minimum luma value from the group of the reference luma samples; generating multi-model linear model (MMLM) including a first linear model between the minimum luma value and the threshold luma value, and a second linear model between the threshold luma value and the maximum luma value; and reconstructing the chroma block from the luma block using MMLM.

A method for processing point cloud data according to embodiments may comprise: encoding point cloud data; and transmitting the encoded point cloud data. The method for processing point cloud data according to embodiments may comprise: receiving point cloud data; and decoding the received point cloud data.

A method for processing point cloud data according to embodiments may comprise: encoding point cloud data; and transmitting the encoded point cloud data. The method for processing point cloud data according to embodiments may comprise: receiving point cloud data; and decoding the received point cloud data.

Provided is an encoder including: circuitry; and memory coupled to the circuitry. In operation, the circuitry: performs a mapping process of Luma Mapping with Chroma Scaling (LMCS) for transforming a first pixel value space applied to a luma display image signal into a second pixel value space applied to a luma encoding process signal, using line segments forming a transform curve, each of which corresponds to a different one of sections obtained by partitioning the first pixel value space; and encodes an image, and in the performing of the LMCS, the circuitry determines the transform curve so that among boundary values in the second pixel value space, a first value obtained by dividing a boundary value by a base width defined according to a bit depth of the image is not equal to a second value obtained by dividing another boundary value by the base width.

According to an example embodiment, a video decoding apparatus may be provided. The video decoding apparatus may include a central processing unit (CPU) configured to parse first header data included in an input bit-stream and generate a first register set based on the parsed first header data; and a decoder configured to decode the input bit-stream based on input parameters obtained through the first register set, wherein CPU is configured to parse second header data included in a second bit-stream of the input bit-stream of a second frame subsequent to the first frame while the decoder decodes a first bit-stream corresponding to the first frame.

According to an example embodiment, a video decoding apparatus may be provided. The video decoding apparatus may include a central processing unit (CPU) configured to parse first header data included in an input bit-stream and generate a first register set based on the parsed first header data; and a decoder configured to decode the input bit-stream based on input parameters obtained through the first register set, wherein CPU is configured to parse second header data included in a second bit-stream of the input bit-stream of a second frame subsequent to the first frame while the decoder decodes a first bit-stream corresponding to the first frame.