Abstract: A system may identify a defined rectangular picture area and render video corresponding to the defined rectangular picture area. The system may receive a video bitstream comprising a picture having a header and may receive data specifying a structure of the picture. The system may parse the data specifying the structure of the picture for an identifier corresponding to a defined rectangular area in the first picture and for a tile index of a top left tile in the defined rectangular area. The system may determine one or more tiles comprised in the defined rectangular area based on the identifier corresponding to the defined rectangular area and the tile index of the top left tile. The system may reconstruct the picture including a sub-picture that comprises the defined rectangular area based upon the identifier corresponding to the defined rectangular area. The computing system may render the sub-picture in the defined rectangular area.

Abstract: A computer-implemented method includes: obtaining first environmental factor data of a first time period and first historical payment data of a target Internet of Things (IoT) device in the first time period, in which the first time period is before a first time point; obtaining second environmental factor data of a second time period that is after the first time point; and obtaining a prediction, using a neural network model, operating on at least one computing device, of an estimated statistical distribution of one or more users who execute transactions using the target IoT device in the second time period, in which the first environmental factor data, the first historical payment data, and the second environment factor data are inputs to the neural network model.

Abstract: The present invention relates to a device and method for measuring the amount of a liquid chemical in a plant protection unmanned aerial vehicle (UAV). An ultrasonic sensor is adopted to measure a distance between a liquid chemical level in a liquid chemical measuring tube communicated with a liquid chemical tank and a top end of the liquid chemical measuring tube, and then a height of the liquid chemical in the liquid chemical tank is further determined according to an acquired attitude angle of the UAV and a distance between a central position of the liquid chemical measuring tube and a central position of the liquid chemical tank, thereby accurately measuring the amount of the liquid chemical in the liquid chemical tank of the UAV.

Abstract: An example method includes decoding, from a coded video bitstream, a first syntax element that specifies a constraint for a second syntax element that specifies whether subpicture information is present in the coded video bitstream and whether more than one subpicture is allowed to be present in the coded video bitstream; and decoding, based on the constraint specified by the first syntax element, the second syntax element.

Abstract: Systems, methods, and computer-readable storage media are provided for decoded picture buffer (DPB) operations and rewriting access unit delimiters (AUDs) after bitstream extractions. An example method can include storing one or more pictures associated with an access unit (AU) in a decoded picture buffer (DPB), the AU including a first plurality of pictures, the first plurality of pictures corresponding to a plurality of video coding layers; after each picture of a second plurality of pictures associated with the AU is removed from a coded picture buffer (CPB), removing at least one picture of the one or more pictures from the DPB; and storing, in the DPB, each picture of the second plurality of pictures removed from the CPB.

Abstract: A method of processing video data, the method comprising: decoding a syntax element in a profile tier level syntax structure in a bitstream that includes an encoded representation of the video data; and based on a value of the syntax element being equal to 0, inferring that all constraint fields in a general constraint information syntax structure have values indicating that constraints corresponding to the constraint fields do not apply to the bitstream.

Abstract: An example device for decoding video data, the device including a memory configured to store video data; and one or more processors implemented in circuitry and configured to: determine that adaptive loop filter (ALF) information can be present in a picture header of a picture of the video data and cannot be present in a slice header; in response to determining that the ALF information can be present in the picture header and cannot be present in the slice header, determine that a picture header syntax structure is not present in the slice header (e.g., without decoding data representing a value for a syntax element indicating whether the picture header syntax structure is present in the slice header); and decode the picture according to the determination that the picture header syntax structure is not present in the slice header.

Abstract: An example method includes decoding, via a first syntax level of a video bitstream, a first deblocking filter control syntax element with a value that specifies whether deblocking filter information is present in a second syntax level of the bitstream; decoding, via the first syntax level of the bitstream, a second deblocking filter control syntax element with a value that specifies whether deblocking override is enabled; responsive to the first deblocking filter control syntax element specifying that the deblocking filter information is present in the second syntax level of the bitstream and regardless of the value of the second deblocking filter control syntax element, decoding, via the second syntax level, one or more syntax elements that specify deblocking filter information; and applying, based on the deblocking filter information, a deblocking filter to a block of the video data.

Abstract: Disclosed a crop yield prediction method and system based on low-altitude remote sensing information from an unmanned aerial vehicle (UAV). Obtaining a plurality of images taken by the UAV; stitching the plurality of images to obtain a stitched image; performing spectral calibration on the stitched image to obtain the reflectivity of each pixel in the stitched image; using a threshold segmentation method to segment the stitched image, to obtain a target area for crop yield prediction; using a Pearson correlation analysis method to analyze a correlation between the reflectivity of each band and the growth status and yield of the crop to obtain feature bands; constructing yield prediction factors based on the feature bands; and determining a predicted crop yield value of the target area for crop yield prediction based on the yield prediction factors and a crop planting area of the target area for crop yield prediction.

Abstract: Provided is a method and system for determining plant leaf surface roughness. The method includes: acquiring a plurality of continuously captured zoomed-in leaf images by using a zoom microscope image capture system; determining a feature match set according to the zoomed-in leaf images; removing de-noised images of which the number of feature matches in feature match set is less than a second set threshold, to obtain n screened images; combining the n screened images to obtain a combined grayscale image; and determining plant leaf surface roughness according to the combined grayscale image. In the present disclosure, first, a plurality of zoomed-in leaf images are directly acquired by the zoom microscope image capture system quickly and accurately; the zoomed-in leaf images are then screened and combined to form a combined grayscale image; finally, three-dimensional roughness of a plant leaf surface is determined quickly and accurately according to the combined grayscale image.

Abstract: Example methods and devices for coding video data are disclosed. An example device for coding video data includes memory configured to store the video data, and one or more processors implemented in circuitry and communicatively coupled to the memory. The one or more processors are configured to determine a value of a first syntax element indicative of whether a first constraint is applicable, the first constraint being that rectangular slices are not used for a plurality of pictures. The one or more processors are also configured to decode the plurality of pictures of the video data in accordance with the value of the first syntax element.

Abstract: Example techniques and devices are disclosed. An example device for coding video data includes memory configured to store the video data and one or more processors implemented in circuitry and coupled to the memory. The one or more processors are configured to determine whether an entry in a reference picture list for a current picture is equal to no reference picture. Based on the entry being equal to no reference picture, the one or more processors are configured to determine additional information associated with the entry. The one or more processors are configured to check a constraint for the entry based on the additional information and code the current picture in accordance with the constraint.

Abstract: Example techniques and devices for decoding video data are disclosed. An example device includes memory configured to store the video data and one or more processors implemented in circuitry and communicatively coupled to the memory. The one or more processors are configured to extract a current sub-picture from a bitstream of the video data and parse virtual boundary syntax elements indicative of virtual boundaries for a current picture, wherein the current sub-picture is associated with the current picture. The one or more processors are configured to update the virtual boundaries based at least in part on the virtual boundary syntax elements and decode the current sub-picture based on the updated virtual boundaries.

Abstract: A method for receiving and displaying media content may be provided. The method may include requesting a set of DASH video segments that are associated with various viewports and qualities. The method may include displaying the DASH video segments. The method may indue determining a latency metric based on a time difference between the display of a DASH video segment and one of: a device beginning to move, the device ceasing to move, the device determining that the device has begun to move, the device determining that the device has stopped moving, or the display of a different DASH video segment. The different DASH video segment may be associated with one or more of a different quality or a different viewport.

Abstract: Systems, methods, and instrumentalities are disclosed for a 360-degree video streaming. A video streaming device may receive a 360-degree video stream from a network node. The video streaming device may determine a viewport associated with the video streaming device and/or the 360-degree video stream. The video streaming device may determine (e.g., based on the viewport) to request in advance a first segment and a second segment of the 360-degree video stream. The video streaming device may determine a relative priority order for the first segment and the second segment. The video streaming device may generate an anticipated requests message. The anticipated requests message may indicate the determined relative priority order, for example, by listing the first segment and the second segment in decreasing relative priority based on the determined relative priority order. The video streaming device may send the anticipated requests message to the network node.

Abstract: It is provided a recognition device for analyzing a starvation extent of a whiteleg shrimp based on underwater imaging, including a bracket, a camera mounted on a top of the bracket, a plurality of light sources for illumination, and a processor connected with the camera. The processor receives a shrimp image collected by the camera, extracts an edge image of the shrimp after the collected shrimp image is preprocessed, and calculates a movement speed of the shrimp and a quantity of baits after a head and a tail are recognized, to recognize starvation extent of the shrimp. It is also provided a recognition method for analyzing starvation extent of the whiteleg shrimp based on underwater imaging. The present disclosure may guide feeders to feed and realize reasonable culture for the shrimp, by capturing images of the shrimp in time and determining starvation the extent of shrimp.

Abstract: An example method includes decoding, from a picture header syntax structure of a coded video bitstream, a syntax element indicating whether or not a picture associated with the picture header syntax structure may include multiple different types of Network Abstraction Layer (NAL) units; responsive to the syntax element indicating that the picture may include multiple different types of NAL units, decoding a merged subpicture track that includes the picture where each picture in the merged subpicture track refers to a common picture parameter set (PPS); and reconstructing, based on the common PPS, samples of the picture.

Abstract: A method of processing video data comprises performing a bitstream conformance process that determines whether a bitstream that comprises an encoded representation of the video data conforms to a video coding standard, wherein the bitstream conformance process determines that the bitstream does not conform to the video coding standard when at least one of: a chroma-related constraint flag is equal to 0 and when there are no chroma components for pictures in the bitstream, or an inter prediction-related constraint flag is equal to 0 when all slices of the bitstream are I slices.

Abstract: A method of decoding video data, the method comprising: obtaining, from a bitstream that comprises an encoded representation of the video data, a first syntax element indicating a number of subpictures in each picture in a coded layer video sequence (CLVS); based on the first syntax element indicating that the number of subpictures in each picture in the CLVS is greater than 1, obtaining, from the bitstream, a second syntax element that specifies whether all subpicture boundaries in the CLVS are treated as picture boundaries; and based on the second syntax element specifying that all subpicture boundaries in the CLVS are treated as picture boundaries, decoding each subpicture in each of the pictures in the CLVS as a separate picture.