A system and methods for a CODEC driving a real-time light field display for multi-dimensional video streaming, interactive gaming and other light field display applications is provided applying a layered scene decomposition strategy. Multi-dimensional scene data is divided into a plurality of data layers of increasing depths as the distance between a given layer and the plane of the display increases. Data layers are sampled using a plenoptic sampling scheme and rendered using hybrid rendering, such as perspective and oblique rendering, to encode light fields corresponding to each data layer. The resulting compressed, (layered) core representation of the multi-dimensional scene data is produced at predictable rates, reconstructed and merged at the light field display in real-time by applying view synthesis protocols, including edge adaptive interpolation, to reconstruct pixel arrays in stages (e.g. columns then rows) from reference elemental images.

Comprehensive, compact, and discriminative representations of videos can be obtained using a counting grid representation of the video and aggregating features associated with active locations of the counting grid to obtain a feature representation of the video. The feature representation can be used for video retrieval and/or recognition. In some examples, the techniques may include conducting normalization and dimension reduction on the aggregated features to obtain a further compact and discriminative feature representation. In some examples, the counting grid representation of the video is generated using a pre-trained counting grid model in order to provide spatially consistent feature representations of the videos.

Various embodiments herein provide techniques for scene construction using object based immersive media. Other embodiments may be described and claimed.

In some examples, a method includes serializing, via a processor, image data for a first image to form serialized data mapped to RBG pixels of a second image, the image data in a format other than a RGB pixel format. The method also includes transmitting, by the processor, the serialized data via RGB signaling lines.

In some examples, a method includes serializing, via a processor, image data for a first image to form serialized data mapped to RBG pixels of a second image, the image data in a format other than a RGB pixel format. The method also includes transmitting, by the processor, the serialized data via RGB signaling lines.

A method of coding at least one image comprising the steps of splitting the image into a plurality of blocks, of grouping said blocks into a predetermined number of subsets of blocks, of coding each of said subsets of blocks in parallel, the blocks of a subset considered being coded according to a predetermined sequential order of traversal. The coding step comprises, for a current block of a subset considered, the sub-step of predictive coding of said current block with respect to at least one previously coded and decoded block, and the sub-step of entropy coding of said current block on the basis of at least one probability of appearance of a symbol.

A method of coding at least one image comprising the steps of splitting the image into a plurality of blocks, of grouping said blocks into a predetermined number of subsets of blocks, of coding each of said subsets of blocks in parallel, the blocks of a subset considered being coded according to a predetermined sequential order of traversal. The coding step comprises, for a current block of a subset considered, the sub-step of predictive coding of said current block with respect to at least one previously coded and decoded block, and the sub-step of entropy coding of said current block on the basis of at least one probability of appearance of a symbol.

Innovations in the identification or classification of features in a data set are described, such as a data set representing measurements taken by a scientific instrument. For example, a task-specific processing component, such as a video encoder, is used to generate task-specific metadata. When the data set includes video frames, metadata can include information regarding motion of image elements between frames, or other differences between frames. A feature of the data set, such as an event, can be identified or classified based on the metadata. For example, an event can be identified when metadata for one or more elements of the data set exceed one or more threshold values. When the feature is identified or classified, an output, such as a display or notification, can be generated. Although the metadata may be useable to generate a task-specific output, such as compressed video data, the identifying or classifying is not used solely in production of, or the creation of an association with, the task-specific output.

Innovations in the identification or classification of features in a data set are described, such as a data set representing measurements taken by a scientific instrument. For example, a task-specific processing component, such as a video encoder, is used to generate task-specific metadata. When the data set includes video frames, metadata can include information regarding motion of image elements between frames, or other differences between frames. A feature of the data set, such as an event, can be identified or classified based on the metadata. For example, an event can be identified when metadata for one or more elements of the data set exceed one or more threshold values. When the feature is identified or classified, an output, such as a display or notification, can be generated. Although the metadata may be useable to generate a task-specific output, such as compressed video data, the identifying or classifying is not used solely in production of, or the creation of an association with, the task-specific output.

A moving image analysis apparatus includes at least one of a processor and a circuitry configured to perform operations including acquiring first data and second data used in processing, in which a moving image is compressed and encoded, for a first frame and a second frame, respectively, included in the moving image, detecting first feature data indicating a first feature of the moving image on the basis of the first frame and the first data and detecting second feature data indicating a second feature of the moving image on the basis of the second frame and the second data, and detecting an object included in the first frame on the basis of the first feature data and the second feature data.