Described herein is a data processing system having a multisample antialiasing compressor coupled to a texture unit and shader execution array. In one embodiment, the data processing system includes a memory device to store a multisample render target, the multisample render target to store color data for a set of sample locations of each pixel in a set of pixels; and general-purpose graphics processor comprising a multisample antialiasing compressor to apply multisample antialiasing compression to color data generated for the set of sample locations of a first pixel in the set of pixels and a multisample render cache to store color data generated for the set of sample locations of the first pixel in the set of pixels, wherein color data evicted from the multisample render cache is to be stored to the multisample render target.

A mechanism is described for facilitating smart compression/decompression schemes at computing devices. A method of embodiments, as described herein, includes unifying a first compression scheme relating to three-dimensional (3D) content and a second compression scheme relating to media content into a unified compression scheme to perform compression of one or more of the 3D content and the media content relating to a processor including a graphics processor.

A mechanism is described for facilitating smart compression/decompression schemes at computing devices. A method of embodiments, as described herein, includes unifying a first compression scheme relating to three-dimensional (3D) content and a second compression scheme relating to media content into a unified compression scheme to perform compression of one or more of the 3D content and the media content relating to a processor including a graphics processor.

A method for encoding a digital hologram represented by values associated respectively with pixels in a plane defining the digital hologram includes forming matrix blocks associated respectively with regions composed of contiguous pixels, each matrix block containing elements determined as a function of the values of the pixels in the region associated with the respective matrix block, applying to each of the matrix blocks a space-frequency transformation to obtain, for each matrix block, a set of coefficients respectively corresponding to different two-dimensional spatial frequencies within the respective matrix block, constructing two-dimensional structures each including coefficients from sets of coefficients and associated with two-dimensional spatial frequencies meeting a criterion that is dependent on the two-respective dimensional structure, and encoding the constructed two-dimensional structures.

This disclosure relates to image processing method and apparatus. The method includes: processing, with a first generator in an image processing model, a first sample image in a first sample set to obtain a first predicted image; processing, with the first generator, a second sample image in a second sample set to obtain a second predicted image; and training the image processing model according to a difference between the target avatar in the first sample image and the first predicted avatar and a difference between a first type attribute of the sample avatar in the second sample image and a first type attribute of the second predicted avatar.

This disclosure relates to image processing method and apparatus. The method includes: processing, with a first generator in an image processing model, a first sample image in a first sample set to obtain a first predicted image; processing, with the first generator, a second sample image in a second sample set to obtain a second predicted image; and training the image processing model according to a difference between the target avatar in the first sample image and the first predicted avatar and a difference between a first type attribute of the sample avatar in the second sample image and a first type attribute of the second predicted avatar.

A system comprises an encoder configured to compress attribute information and/or spatial information for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. The encoder is configured to convert a point cloud into an image based representation. The encoder packs patch images into an image frame and fills empty spaces in the image frame with a padding. The encoder is also configured to determine quantized minimum depths and/or maximum depths patch images in the image frames, wherein depth information is signaled relative to the quantized minimum depth.

A three-dimensional data encoding method includes: dividing three-dimensional points included in point cloud data into processing units each of which includes one or more of the three-dimensional points; and encoding attribute information of a current three-dimensional point included in a current processing unit, by reference to an encoded processing unit, to generate a bitstream.

A three-dimensional data encoding method includes: dividing three-dimensional points included in point cloud data into processing units each of which includes one or more of the three-dimensional points; and encoding attribute information of a current three-dimensional point included in a current processing unit, by reference to an encoded processing unit, to generate a bitstream.

An inter prediction method allows a variable coefficient deep learning model to adaptively learn characteristics of a video; transmits a variable coefficient deep learning model parameter generated from the learning from an image encoding device to an image decoding device; and refers to a virtual reference frame generated by the variable coefficient deep learning model.