Abstract: A video encoding method includes: determining current coefficients to-be-decoded in a coding block of a video image frame; obtaining a flag combination corresponding to the current coefficients, the flag combination including at least two coefficient absolute level threshold flags; decoding the coefficient absolute level threshold flags in the flag combination to obtain values of the coefficient absolute level threshold flags; and determining the coefficient absolute levels of the current coefficients according to the values of the coefficient absolute level threshold flags.

Abstract: A video decoding method includes performing entropy decoding processing on a code block of a video image frame of a video image frame sequence, to obtain a quantization coefficient block of residual data corresponding to the code block; collecting statistics of quantization coefficients in a target region in the quantization coefficient block, to obtain a quantization coefficient statistical value; selecting a transform matrix combination corresponding to the quantization coefficient statistical value; and performing inverse transform processing on an inverse quantization result of the quantization coefficient block based on the selected transform matrix combination, to obtain reconstructed residual data, the reconstructed residual data being used for video image reconstruction.

Abstract: A video encoding method includes: acquiring a coding block of a video image frame; determining a partitioning mode available to the coding block according to a size of the coding block and an allowable maximum sub-block size ratio in response to a determination that sub-block transform (SBT) is to be performed on the coding block; determining, based on the partitioning mode available to the coding block, a target partitioning mode used during the SBT performed on the coding block; and partitioning the coding block based on the target partitioning mode, and decoding a plurality of sub-blocks obtained through the partitioning.

Abstract: A video decoding method includes: performing entropy decoding processing on an encoding block of a video image frame to obtain a quantized coefficient block of residual data corresponding to the encoding block; calculating quantization coefficients in the quantized coefficient block to obtain an implicitly derived index value; determining a transformation mode of the encoding block according to the implicitly derived index value and a value of an index identifier included in the encoding block; and performing inverse transformation processing on an inverse quantization result of the quantized coefficient block based on the transformation mode of the encoding block.

Abstract: Provided are a video encoding method and apparatus, a video decoding method and apparatus, a computer-readable medium, and an electronic device, The video decoding method includes: decoding a coded block of a video image frame to obtain a target intra prediction mode corresponding to the coded block (510); in response to the target intra prediction mode belonging to a set of specified intra prediction modes, updating statistical values of the set of specified intra prediction modes, a number of the set of specified intra prediction modes being less than a total number of intra prediction modes (520); updating a most probable intra prediction mode list according to the updated statistical values of the set of specified intra prediction modes (530); and decoding another coded block of the video image frame based on the updated most probable intra prediction mode list (540).

Abstract: A picture filtering method, performed by an electronic device, includes: obtaining a picture to be filtered; determining a neural network filter; dividing the picture to be filtered according to a blocking mode corresponding to the neural network filter to obtain one or more picture blocks to be filtered, the blocking mode being a same blocking mode for a training picture used in training the neural network filter; and filtering the one or more picture blocks based on the neural network filter to obtain a filtered picture.

Abstract: A multimedia data processing method, executed by a computer device, includes: determining a first associated image block associated with a target image block to be filtered in multimedia data; acquiring target coding and decoding information associated with the target image block; and filtering the target image block by an image filter based on a neural network according to the first associated image block and the target coding and decoding information to obtain a filtered image block corresponding to the target image block.

Abstract: An image processing method includes displaying a first key frame image obtained from a sequence frame video stream; obtaining a first foreground image and a first background image obtained after foreground and background separation processing is performed on the first key frame image; obtaining a second foreground image obtained after foreground image processing is performed on a first target object in the first foreground image; obtaining a third background image obtained after background repair processing is performed on the first background image using a second background image, where the second background image is a background image included in a second key frame image obtained by photographing a target scene before the first key frame image is obtained; and obtaining a first key frame image obtained after foreground and background compositing processing is performed on the second foreground image and the third background image.

Abstract: Provided are a compound as represented by formula (I), and a pharmaceutically acceptable salt, tautomer, mesomer, racemate, stereoisomer, metabolite, metabolic precursor, or drug precursor thereof, and application thereof as a protein regulator, in particular a GSPT1 protein regulator, and a preparation method.

Abstract: A video decoding method includes: performing entropy decoding on a coding block of a video image frame to obtain a quantization coefficient block of residual data corresponding to the coding block; inversely quantizing the quantization coefficient block to obtain an inverse quantization coefficient matrix; inversely rearranging the inverse quantization coefficient matrix to obtain a processed coefficient matrix, non-zero coefficients in the processed coefficient matrix being concentrated in left, upper and upper left regions of the coefficient matrix; and generating the residual data according to the processed coefficient matrix.

Abstract: The present disclosure relates to the technical field of medical supplies, in particular to a fusion cage. The fusion cage comprises a main body, which is a flat-shaped porous structural body and composed of a plurality of structural units. The fusion cage provided by the present disclosure is the flat-shaped porous structural body composed of the plurality of structural units, the porosity of the whole structure of the fusion cage is controlled by using the parametrization design, so the elastic modulus of a fusion body is effectively reduced, and then each structural unit comprises the basal body and the plurality of extension portions extending from the surface of the basal body, the surfaces of the basal body and/or the extension portions are composed of the plurality of curved surfaces through smooth connection, and such design effectively reduces the problem of stress concentration, and improving the postoperative recovery effect of patients.

Abstract: A vehicle window assembly and a vehicle are provided in the present disclosure. The vehicle window assembly includes a glass plate, a guide rail, a mounting member, and a connecting member. The guide rail defines an accommodating space that has an opening facing a glass plate. At least part of the mounting member is disposed in the accommodating space. The mounting member defines a mounting hole in communication with the accommodating space. The connecting member has one end connected to the glass plate, and the other end penetrating through the mounting hole.

Abstract: A display module includes a display panel with a plurality of bonding electrodes arranged at intervals along a selected side edge of a non-display surface and divided into two bonding electrode groups, a first flexible circuit board and a second flexible circuit board. For the first flexible circuit board, each first conductive contact piece in a first wiring region is connected to a bonding electrode in a first bonding electrode group. For the second flexible circuit board, each second conductive contact piece in a second wiring region is connected to a bonding electrode in a second bonding electrode group. The first wiring region is closer to the selected side edge than the second wiring region in a first direction. The first fan-out region is spaced apart from the second wiring region in a second direction perpendicular to the first direction.

Abstract: Systems and methods for deep neural network (DNN)-based cross component prediction are provided. A method includes inputting a reconstructed luma block of an image or video into a DNN; and predicting, by the DNN, a reconstructed chroma block of the image or video based on the reconstructed luma block that is input. Luma and chroma reference information and side information may also be input into the DNN to predict the reconstructed chroma block. The various inputs may also be generated using processes such as downsampling and transformation.

Abstract: Some aspects of the disclosure provide a method of deep learning-based filtering. The method includes obtaining a reconstructed image corresponding to a coded image and at least a quantization parameter. At least a portion of the reconstructed image is reconstructed based on the quantization parameter. The method also includes generating an input to a deep learning filter according to a point multiplication operation of the reconstructed image and the at least the quantization parameter. The point multiplication operation includes pixel-wise multiplications of the at least the portion of the reconstructed image with the quantization parameter. Further, the method includes generating, by the deep learning filter and in response to the input, a filtered image corresponding to the reconstructed image. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated.

Abstract: Embodiments of this application relate to the video coding and compression field, and disclose an encoding method and apparatus, and a decoding method and apparatus, to resolve a problem that an existing split mode cannot satisfy a relatively complex texture requirement. The decoding method specifically includes: parsing a bitstream to determine a basic split mode for a current to-be-decoded picture block and a target derivation mode for a subpicture block of the current to-be-decoded picture block; splitting the current to-be-decoded picture block into N subpicture blocks in the basic split mode, where N is an integer greater than or equal to 2; deriving one derived picture block from at least two adjacent subpicture blocks in the N subpicture blocks in the target derivation mode; and decoding the derived picture block.

Abstract: A picture filtering method includes determining encoding information of a picture, the encoding information comprising classification information indicating one of an intra encoding mode or an inter encoding mode of the picture. The method further includes inputting the picture and the classification information indicating one of the intra encoding mode or the inter encoding mode into a general filtering model trained using deep learning, and obtaining a filtered picture based on the filtering model performing filtering on the picture based on the encoding information.

Abstract: A vehicle window assembly and a vehicle are provided in the disclosure. The vehicle window assembly includes multiple light guide strips spaced apart from each other, a vehicle window glass sheet, and a sealing box. The multiple light guide strips are arranged on one side surface of the vehicle window glass sheet, the sealing box is disposed at one side of each of the multiple light guide strips away from the vehicle window glass sheet, the sealing box defines grooves at positions respectively corresponding to the multiple light guide strips, and each of the multiple light guide strips is received in one of the grooves. The multiple light guide strips are received in the sealing box, the sealing box is directly fixed to the vehicle window glass sheet by the fixing member.