The method for decoding an intra-picture prediction mode includes the steps of: determining whether the intra-picture prediction mode of a current prediction unit is identical to a first intra-picture prediction mode candidate or a second intra-picture prediction mode candidate based on bit information; and when the intra-picture prediction mode of the current prediction unit is identical to the first intra-picture prediction mode candidate and/or to the second intra-picture prediction mode candidate, determining whether the first intra-picture prediction mode candidate or the second intra-picture prediction mode candidate is identical to the intra-picture prediction mode of the current prediction unit on the basis of additional bit information, and decoding the intra-picture prediction mode of the current prediction unit.

This disclosure discloses a method for obtaining a candidate motion vector list, an apparatus, an encoder, and a decoder. The method for obtaining a candidate motion vector list comprises: when a first candidate picture block is encoded/decoded and an inter prediction mode is used, determining whether a reference picture of the first candidate picture block is the same as a reference picture of a current block; and constructing a candidate motion vector list of the current block based on a determining result; when the reference picture of the first candidate picture block is different from the reference picture of the current block, the MV of the first candidate picture block is not used to construct the list. Implementing this disclosure can reduce complexity of a motion information derivation process, and improve coding efficiency.

A controller bypasses processing raw image data captured by an image sensor at a wearable device and selects among modes of operation, to direct the raw image data to a light engine, to a transmitter, and/or to a computer vision engine. The light engine outputs display light based on the raw image data, the transmitter transmits the raw image data external to the wearable device, and the computer vision engine analyzes the raw image data to identify at least one feature represented in the raw image data and outputs computer vision data. The modes of operation selected by the controller reduce or eliminate intensive image signal processing operations performed by the wearable device on the raw image data.

Apparatuses, systems, and techniques are presented to reduce an amount of data to be transmitted for media content. In at least one embodiment, one or more neural networks are used to generate video and audio information corresponding to one or more people based, at least in part, on at least one image and voice information corresponding to the one or more people.

In some embodiments, a method analyzing a first set of values for a first bin plane in a plurality of bin planes. The plurality of bin planes are used to determine a context model for entropy coding of a current block in a video. The method determines whether to use a second set of values from a second bin plane based on the analyzing. When it is determined to use the second set of values, information is calculated for the context model using the first set of values and the second set of values. When it is determined to not use the second set of values, information is calculated for the context model using the first set of values.

A method for video processing is provided. The method includes performing downsampling on chroma and luma samples of a neighboring block of the current video block; determining, for a conversion between a current video block of a video that is a chroma block and a coded representation of the video, parameters of cross-component linear model (CCLM) based on the downsampled chroma and luma samples obtained from the downsampling; applying the CCLM on luma samples located in a luma block corresponding to the current video block to derive prediction values of the current video block; and performing the conversion based on the prediction values.

A method of performing intra prediction of a current block of a picture of a video sequence, includes determining whether a first flag indicates that an intra prediction mode corresponding to the current block is a directional mode, and based on the first flag being determined to indicate that the intra prediction mode corresponding to the current block is the directional mode, determining an index of the intra prediction mode in an allowed intra prediction modes (AIPM) list, and performing the intra prediction of the current block, using the intra prediction mode corresponding to the determined index in the AIPM list.

An image processing apparatus decodes encoded RAW data that includes subband data being encoded with lossy encoding scheme, and determines one of a plurality of classifications based on the decoded subband data, wherein the plurality of classifications are based on a feature of an image. The apparatus also obtains correction data corresponding to the determined classification, and corrects recomposed data, which is obtained by applying frequency recomposition to the decoded subband data, based on the correction data, in order to obtain the corrected data as decoded RAW data.

The present technology relates to devices and systems for multidimensional data visualization and interaction in an augmented reality, virtual reality, or mixed reality image guided surgery. The disclosed embodiment provides a tool for a physician or other medical specialist to load and review medical scans in an AR/VR/MR environment, assisting medical diagnostics, surgical planning, medical education, or patient engagement.

Embodiments of the present invention provide a video signal processing method and device. Particularly, a method for decoding a video signal, may comprise the steps of: checking whether a transfer skip is applied to a current block; obtaining, from a video signal, a transform index for indicating a transform type set applied to the current block when the transform skip is not applied to the current block, wherein the transform type set includes transform types applied to the current block in horizontal and vertical directions; checking whether the transform type set includes DCT2; determining a region to which a primary transform is applied based on a checking result; and performing an inverse transform on the region to which the primary transform is applied in the horizontal and vertical directions using the transform types included in the transform type set.