Computer processor hardware receives zone information specifying multiple elements of a rendition of a signal belonging to a zone. The computer processor hardware also receives motion information associated with the zone. The motion information can be encoded to indicate to which corresponding element in a reference signal each of the multiple elements in the zone pertains. For each respective element in the zone as specified by the zone information, the computer processor hardware utilizes the motion information to derive a corresponding location value in the reference signal; the corresponding location value indicates a location in the reference signal to which the respective element pertains.

Computer processor hardware receives zone information specifying multiple elements of a rendition of a signal belonging to a zone. The computer processor hardware also receives motion information associated with the zone. The motion information can be encoded to indicate to which corresponding element in a reference signal each of the multiple elements in the zone pertains. For each respective element in the zone as specified by the zone information, the computer processor hardware utilizes the motion information to derive a corresponding location value in the reference signal; the corresponding location value indicates a location in the reference signal to which the respective element pertains.

Example aspects of the present disclosure are directed to a computer-implemented method for determining a perceptual quality of a subject video content item. The method can include inputting a subject frame set from the subject video content item into a first machine-learned model. The method can also include generating, using the first machine-learned model, a feature based at least in part on the subject frame set. The method can also include outputting, using a second machine-learned model, a score indicating the perceptual quality of the subject video content item based at least in part on the feature.

Example aspects of the present disclosure are directed to a computer-implemented method for determining a perceptual quality of a subject video content item. The method can include inputting a subject frame set from the subject video content item into a first machine-learned model. The method can also include generating, using the first machine-learned model, a feature based at least in part on the subject frame set. The method can also include outputting, using a second machine-learned model, a score indicating the perceptual quality of the subject video content item based at least in part on the feature.

A method, apparatus and a non-transitory computer readable medium are provided for receiving an input picture divided into a plurality of coding units (CUs) and comprising a virtual boundaries between a dirty area and a clean area of the input picture, each CU located within either the clean area or the dirty area. The virtual boundary is treated as a picture boundary for coding units within the clean area and as a non-boundary for coding units within the dirty area. For a current CU, a history-based motion vector prediction (HMVP) table can be prepared that identifies other CUs as HMVP candidates for inter prediction, the HMVP candidates being adjacent the current CU. The HMVP candidate CUs are limited to CUs previously coded in the clean area. The current CU can be intra coded based at least upon the HMVP candidates from the HMVP table.

The present disclosure relates to an image processing apparatus and an image processing method capable of suppressing a reduction in encoding efficiency. A bit stream is generated which contains: information indicating a correspondence relation between at least one of a geometry image obtained by projecting position information regarding 3D data representing a three-dimensional structure onto a two-dimensional plane or a texture image obtained by projecting attribute information regarding the 3D data onto a two-dimensional plane, and an occupancy map that is map information indicating whether or not data is present at each position; and encoded data regarding the geometry image, encoded data regarding the texture image, and encoded data regarding the occupancy map. The present disclosure is applicable to, for example, an information processing apparatus, an image processing apparatus, an electronic apparatus, an information processing method, a program, and the like.

The present disclosure relates to an image processing apparatus and an image processing method capable of suppressing a reduction in encoding efficiency. A bit stream is generated which contains: information indicating a correspondence relation between at least one of a geometry image obtained by projecting position information regarding 3D data representing a three-dimensional structure onto a two-dimensional plane or a texture image obtained by projecting attribute information regarding the 3D data onto a two-dimensional plane, and an occupancy map that is map information indicating whether or not data is present at each position; and encoded data regarding the geometry image, encoded data regarding the texture image, and encoded data regarding the occupancy map. The present disclosure is applicable to, for example, an information processing apparatus, an image processing apparatus, an electronic apparatus, an information processing method, a program, and the like.

Highly accurate shape registration processing is performed. In the image processing apparatus, data, which correspond to N frames (N is an integer not less than 2), in units of frames including a plurality of three-dimensional models per frame is acquired. Then, shape registration processing is performed for the acquired three-dimensional models corresponding to the N frames by using information indicating a correspondence relationship of the three-dimensional models between frames of the N frames.

Apparatus and methods for processing video surveillance data includes training a data repository, using a first plurality of surveillance video files including a first plurality of video frames, to identify macroblocks of the video frames representing average content of the first plurality of surveillance video files. An ordered data structure is generated by sorting the plurality of macroblocks of video frames based on image differences within the plurality of macroblocks. The ordered data structure includes a root node. A second plurality of surveillance video files including a second plurality of video frames is received. The second plurality of video frames is inserted into the generated ordered data structure. References to the generated ordered data structure are stored in the data store for each frame of the second plurality of video frames along with a difference between corresponding video frames and references.

One or more hardware components identify a bottleneck stage within a processor pipeline that processes frames of a video stream. The bottleneck stage has a first clock. An upstream stage receives a feedback signal from the bottleneck stage. The upstream stage has a second clock and the feedback signal includes information as to time required by the bottleneck stage to operate on data and information as to time the data spent queued. The upstream stage adjusts the speed at which the upstream stage operates and queues data to approximate the speed at which the bottleneck stage is operating and queuing data.