Abstract: Provided are a data processing method and an apparatus thereof. The method includes: when data writing is to be performed for a target Trunk Group (TKG), determining whether the target TKG is available (S100); when the target TKG is available, performing data writing for the target TKG (S110); and, when the target TKG is unavailable, repairing the target TKG, and performing data writing for the repaired target TKG (S120).

Abstract: Method, an electronic device and a storage medium for living body detection based on face recognition are disclosed. The method comprises: obtaining to-be-detected infrared image and visible light image; performing edge detection and texture feature extraction on the infrared image, and feature extraction on the visible light image through a convolutional neural network; and determining whether the infrared and visible light images pass living body detection based on results of the edge detection and texture feature extraction on the to-be-detected infrared image, and a result of feature extraction on the to-be-detected visible light image through the convolutional neural network. The method, an electronic device and a storage medium for living body detection based on face recognition combine the advantages of three technologies of edge detection, texture feature extraction and convolution neural network, effectively perform living body detection, and improve the determination accuracy.

Abstract: Provided is a decoding method, including: acquiring coded data of a current block; determining size information of subblocks based on subblock partition information of the current block in response to the current block satisfying a subblock partition condition and determining, based on the coded data of the current block, that the current block activates intra subblock partition; determining, based on the size information of each subblock, a transform pair corresponding to the subblock; and performing an inverse transform on inversely quantized data of the subblocks based on the transform pairs corresponding to the subblocks.

Abstract: The present disclosure provides methods, apparatuses and devices for coding and decoding.

Abstract: Embodiments of the present disclosure provide an encoding and decoding method, apparatus and device.

Abstract: Provided are a video playing method and apparatus. Wherein the video playing method comprises: performing video-preprocessing on video data to be played and playing the video data obtained after the video-preprocessing by means of a main process of the Web client; in the case that the performance resource of the Web client is insufficient, creating and starting a video service process, wherein the video service process is configured to perform the video-preprocessing on video data to be played and play the video data obtained after the video-preprocessing. In the technical solution according to the embodiment in the present application, the main process of the Web client can play videos without relying on plug-ins. In the case that the performance resource of the Web client is insufficient, the video service process is used to play videos for the smoothness of video playing, which can also play videos without relying on plug-ins.

Abstract: Coding and decoding methods and apparatuses are provided. A decoding method includes: determining a prediction mode of a current block; when it is determined that the prediction mode of the current block satisfies one of the following conditions, determining the current block satisfies transform skip mode conditions: the prediction mode of the current block is an inter prediction mode; and the prediction mode of the current block is a direct mode.

Abstract: The present application provides a screen projection method, a receiving end device, and a sending end device. The receiving end device performs active wireless scanning and searching on the sending end device, and when the sending end device is scanned, establishes a wireless connection with the sending end device; and once the wireless connection is established, the receiving end device automatically sends a screen projection code to the connected sending end device, and the sending end device may prompt a user to perform a screen projection operation by means of displaying the screen projection code. In this process, there is no need for the user to perform complicated manual operations, the sending end device can automatically acquire the screen projection code, and prompts the user to perform the screen projection operation in a timely manner, thereby improving the timeliness of the screen projection operation.

Abstract: Provided are a window overlay display processing method and apparatus, and an electronic device. The method comprises: in a process of overlay-displaying a following window on a main window, capturing an image of display content of the following window according to an image capture time interval (S501); and displaying the captured image at a display position corresponding to the following window in the main window (S502). By means of the above method, when the display level of the main window is higher than that of the following window, the main window still displays the captured image corresponding to the display content of the following window. In this way, the user will think that he/she is continuing to view the display content of the following window, and will not feel flickering visually, thus improving the display effect of the following window and enhancing the user experience of viewing the following window.

Abstract: The application provides a network camera, a video monitoring system and a method. The network camera comprises: an image sensor, a processor, a memory and a network communication interface; the processor is configured for matching a current image acquired by the image sensor with an image stored in a second storage unit of the memory, so as to obtain a similarity value representing a matching result, and storing an image satisfying a similarity condition in another storage unit of the network camera, reducing the difficulty of image comparison and improving the accuracy of the comparison result.

Abstract: A method and an apparatus for quantizing an activation volume of a deep neural network are disclosed. The method includes: obtaining an activation volume of a network layer in the deep neural network (S101), wherein elements in the activation volume are arranged in three directions: height, width, and depth; dividing depth segments in the activation volume in which a difference among element features is smaller than a preset threshold into a same slice group along the depth direction of the activation volume, so as to obtain a plurality of slice groups (S102); quantizing each slice group respectively by using a quantization parameter corresponding to each slice group obtained through a quantization formula (S103). The quantization error can be reduced through the above method.

Abstract: The present application provides a screen projection method, a receiving end device, and a sending end device. The receiving end device performs active wireless scanning and searching on the sending end device, and when the sending end device is scanned, establishes a wireless connection with the sending end device; and once the wireless connection is established, the receiving end device automatically sends a screen projection code to the connected sending end device, and the sending end device may prompt a user to perform a screen projection operation by means of displaying the screen projection code. In this process, there is no need for the user to perform complicated manual operations, the sending end device can automatically acquire the screen projection code, and prompts the user to perform the screen projection operation in a timely manner, thereby improving the timeliness of the screen projection operation.

Abstract: Encoding and decoding methods and apparatuses, coder-side devices and decoder-side devices are provided, the method includes: obtaining a weighted prediction angle and weight configuration parameters of the current block; where the weight configuration parameters include a weight transform rate and a weight transform start position; configuring reference weight values for surrounding positions outside the current block according to the weight configuration parameters; determining a surrounding matching position to which the pixel position points from the surrounding positions outside the current block based on the weighted prediction angle; determining a target weight value of the pixel position based on the reference weight value associated with the surrounding matching position, determining an association weight value of the pixel position based on the target weight value of the pixel position; determining a first prediction value of the pixel position based on a first prediction mode, determining a second

Abstract: The embodiments of the present application relates to a camera, comprising a first housing, a stitching lens mechanism, and a driving assembly. The stitching lens mechanism is mounted in the first housing, and the stitching lens mechanism comprises at least two first assemblies, the first lens assembly comprises a first lens, a first included angle is formed between at least two first lenses. A driving assembly is connected to the first lens assembly through a rotating assembly. At least two first lenses are distributed in a first plane, the first housing is rotatably arranged in a second plane, and the second plane is perpendicular to the first plane. Each of the first lens assemblies takes pictures in different orientations for the same scene, so that the imaging field of view is larger. So that the first housing can rotate under the drive of the driving assembly, that is, the stitching lens mechanism can rotate.

Abstract: Embodiments of the present application provide a data transmission method, a camera and an electronic device. The method includes: acquiring to-be-sent target data; and sending the target data, as data carried in an effective image area of a first image frame, by using a data sending manner for the effective image area, through a same transmission channel as used in transmitting image data; wherein, the target data is different from the image data and coaxial data, the coaxial data is notification information for an interaction between a data receiving end and a data sending end, and the first image frame has a different type from a second image frame carrying the image data without carrying the target data. Applying the solutions provided by the embodiments of the present application, the transmission of the target data can be realized without additional wiring, thereby reducing the device cost.

Abstract: Provided are a picture enhancement method and apparatus. The picture enhancement method includes: for any one pixel, which meets a first filtering condition, in a current block, determining a first pixel value of the pixel after first filtering; and performing enhancement on a pixel value of the pixel based on the first pixel value and a second pixel value of the pixel point before the first filtering, so as to obtain a third pixel value, which has been subjected to enhancement, of the pixel.

Abstract: A filtering method and apparatus, and a device are provided. The filtering method includes: when it is determined to enable Wiener filtering for an adaptive leveling filter unit of a current picture, deriving the adaptive leveling filter unit from a current Largest Coding Unit (LCU) of the current picture; obtaining reference pixels of the adaptive leveling filter unit and filter coefficients of a Wiener filter; and according to the reference pixels and the filter coefficients of the Wiener filter, performing the Wiener filtering based on sample pixels of the adaptive leveling filter unit; where the Wiener filter is a centrosymmetric filter with a shape of a 7*7 cross plus a 5*5 square, and values of the reference pixels are derived from pixel values of a reconstructed picture before the Wiener filtering.

Abstract: A living body detection method and device are disclosed. Wherein the method comprises the following steps: extracting valid depth data of a target detection object from depth map data containing the target detection object; generating a depth difference histogram based on the valid depth data; and inputting the depth difference histogram into a pre-trained machine learning classifier to obtain a determination result of whether the target detection object is a living body. By adopting this method, the detection accuracy can be improved.