Methods for converting an n-bit number into an m-bit number for situations where n>m and also for situations where n<m, where n and m are integers. The methods use truncation or bit replication followed by the calculation of an adjustment value which is applied to the replicated number.

Methods for converting an n-bit number into an m-bit number for situations where n>m and also for situations where n<m, where n and m are integers. The methods use truncation or bit replication followed by the calculation of an adjustment value which is applied to the replicated number.

A method for decoding image content from an encoded bitstream including a plurality of blocks includes: dividing a block including one or more components of the image content into N single samples and M sample groups corresponding to one of the components, where N and M are greater than or equal to one; decoding each of the N single samples using a symbol variable length code to generate one or more decoded single samples; decoding each of the M sample groups using a common prefix entropy code to generate one or more decoded sample groups, each of the M sample groups including a variable length prefix and one or more fixed length suffixes representing a plurality of samples; concatenating the decoded single samples and the decoded sample groups into a block of residuals; and reconstructing image content based on previously reconstructed neighboring blocks and the block of residuals.

A method for decoding image content from an encoded bitstream including a plurality of blocks includes: dividing a block including one or more components of the image content into N single samples and M sample groups corresponding to one of the components, where N and M are greater than or equal to one; decoding each of the N single samples using a symbol variable length code to generate one or more decoded single samples; decoding each of the M sample groups using a common prefix entropy code to generate one or more decoded sample groups, each of the M sample groups including a variable length prefix and one or more fixed length suffixes representing a plurality of samples; concatenating the decoded single samples and the decoded sample groups into a block of residuals; and reconstructing image content based on previously reconstructed neighboring blocks and the block of residuals.

Embodiments described herein provide bootstrapping language-images pretraining for unified vision-language understanding and generation (BLIP), a unified VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP enables a wider range of downstream tasks, improving on both shortcomings of existing models.

Provided is a method and device for outputting a large-capacity 3D model for an augmented reality (AR) device. A method of outputting a large-capacity 3D model for an AR device includes generating a multi-texture and a 3D mesh based on a multi-view image, generating a 3D model using the multi-texture and the 3D mesh, and transmitting, to the AR device, an image of the 3D model in a view, to which a camera of the AR device is directed, according to camera movement and rotation information of the AR device, and the AR device outputs the image in the view, to which the camera is directed.

Provided is a method and device for outputting a large-capacity 3D model for an augmented reality (AR) device. A method of outputting a large-capacity 3D model for an AR device includes generating a multi-texture and a 3D mesh based on a multi-view image, generating a 3D model using the multi-texture and the 3D mesh, and transmitting, to the AR device, an image of the 3D model in a view, to which a camera of the AR device is directed, according to camera movement and rotation information of the AR device, and the AR device outputs the image in the view, to which the camera is directed.

A dynamic image virtualization system and method configured to utilize an AI model in order to conduct a reduced latency real-time prediction process upon at least one input image, wherein said prediction process is designated to create free-viewpoint 3D extrapolated output dynamic images tailored in advance to the preferences or needs of a user and comprising more visual data than the at least one input image.

A dynamic image virtualization system and method configured to utilize an AI model in order to conduct a reduced latency real-time prediction process upon at least one input image, wherein said prediction process is designated to create free-viewpoint 3D extrapolated output dynamic images tailored in advance to the preferences or needs of a user and comprising more visual data than the at least one input image.

An image processing method includes: obtaining a first latent code by encoding an image to be edited in a Style (S) space of a Generative Adversarial Network (GAN), in which the GAN is a StyleGAN; encoding the text description information, obtaining a text code of a Contrastive Language-Image Pre-training (CLIP) model, and obtaining a second latent code by mapping the text code on the S space; obtaining a target latent code that satisfies distance requirements by performing distance optimization on the first latent code and the second latent code; and generating a target image based on the target latent code.