578 results about "Filter (video)" patented technology

The invention relates to the technical field of image stitching, and discloses a video transmission high-definition image intelligent stitching method and system. The system comprises an image preprocessing module, a feature point matching and screening module, an image optimal splicing seam generation module and a video output module. The method comprises the following steps: firstly, acquiring a video, carrying out dynamic range equalization, carrying out filtering processing by using an adaptive noise reduction method, and carrying out image correction; secondly, extracting feature points by using a self-adaptive corner detection algorithm, matching the feature points based on a multi-dimensional spatial data index to obtain feature point matching pairs, and screening the feature points; searching an overlapping region, and introducing a dynamic search algorithm to generate an optimal image splicing path to obtain an optimal image splicing seam; and finally, dividing the image according to the dynamic grid, and realizing video output by using priority ranking. According to the method, the video images are processed and spliced, the purpose of intelligent image splicing is achieved, and the method is accurate and objective.

Syntax elements allow to enable applying film grain using a neural network post-processing filter. Syntax elements define parameters related to the film grain neural network post- processing filter. An encoded video bitstream carries such syntax elements from an encoding device to a decoding device, thus allowing the encoding device to specify how to apply film grain using a neural network post-processing filter and the decoding device to apply the film grain neural network post-processing filter when displaying the decoded video. Additional syntax elements specify parameters comprising a film grain style, a film grain intensity, a film grain purpose or a region of interest where to apply the film grain.

A video coding mechanism is disclosed. The mechanism includes receiving a bitstream comprising a current picture including a sub-picture coded according to inter-prediction. A motion vector for a block of the sub-picture is determined. A clipping function is applied to sample locations in a reference block to support application of an interpolation filter when the motion vector points outside of the sub-picture and when a flag is set to indicate the sub-picture is treated as a picture. The interpolation filter is applied to results of the clipping function to obtain a predicted sample value. The block is decoded based on the predicted sample value. The block is forwarded for display as part of a decoded video sequence.

An example method of video coding includes receiving a video bitstream comprising a plurality of frames, including a current frame comprising a current block. The method also includes determining an edge direction by performing edge detection for the current block, and determining a difference between a current sample of the current block and a neighboring sample based on the edge direction. The method further includes applying a filter to the current block based on the determined difference.

Methods and apparatuses for video decoding and video encoding and a method of processing visual media data are disclosed. The apparatus for video decoding includes processing circuitry that receives coded information indicating that a current block in a current picture is coded with a decoder-side intra mode derivation (DIMD) mode. A template of the current block includes reconstructed samples in the current picture and is adjacent to the current block. The template includes one of a left template and a top template. The processing circuitry determines a filter type from a plurality of filter types associated with the one of the left template and the top template, applies the DIMD mode to the template based on the determined filter type to determine one or more intra prediction modes for the current block, and reconstructs the current block according to the one or more intra prediction modes.

Embodiments of the disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: performing, for a conversion between a video unit of a video and a bitstream of the video, a refinement on a prediction or reconstruction of the video unit by applying a filter for the video unit; and performing the conversion based on the refined prediction or refined reconstruction.

Approaches presented herein provide for the high quality, high resolution reconstruction of a sequence of decimated images, such as may be useful for remote desktop applications. The video frames can be sub-sampled or decimated such that each encoded frame only includes a fraction (e.g., ¼) of the total pixel values for the full resolution frame. A server can analyze the current and previous video frames at full resolution to determine motion or actionable changes, and can apply a lowpass filter to those values based on the type of interpolation to be performed on the client. Static values can remain where no motion is detected. When a client receives the decimated and encoded video frames, the client can determine areas of motion and can perform bilinear interpolation for only those portions of the image where motion is detected, and can otherwise perform weaving of the static pixel values received over a limited sequence of decimated video frames.

A video encoder is configured to determine to filter video data using a neural network (NN)-based filter and a fixed block size inference, and encode a flag that indicates the fixed block size inference is used for the NN-based filter. Reciprocally, a video decoder is configured to decode a flag that indicates whether a fixed block size inference is used for an NN-based filter, and filter video data using the NN-based filter based on the flag. The flag may be signaled at a sequence parameter set (SPS) level.

A method for coding a video implemented in an encoder or a decoder including the enhanced interpolation filter, EIF, for motion compensation, the method comprising: i) determining control point motion vectors, CPMVs, for a block according to affine inter-prediction, the block being an affine block or a sub-block of the affine block; ii) for a predefined sub-block size determining a reference area for a sub-block with the predefined sub-block size according to values of the CPMVs; iii) comparing the determined reference area with a predefined threshold; iv) applying EIF for motion compensation, comprising deriving the pixel-based motion vector field for the block; wherein if the determined reference area is larger than the threshold, deriving the pixel-based motion vector field for the block further comprises motion vector clipping, wherein motion vector clipping range is determined based on motion model of the block and the size of the block.

The invention discloses a vehicle violation manned intelligent detection method and system based on a CLIP and D-Fine cascade framework, and the method comprises the steps: collecting a real-time video frame image of a preset traffic monitoring network, processing the real-time video frame image, inputting a monitoring image into a CLIP-ILP model, employing the CLIP-ILP model as a filter, and combining with a Top-K screening strategy, and screening out candidate images; and inputting the candidate image into a constructed context enhanced D-FINE detection model, carrying out positioning and multi-class detection on a preset target, outputting a multi-class detection result, and executing verification of a spatial co-occurrence rule, a license plate position heuristic rule, a size consistency rule and a context consistency rule. And marking the result which does not pass the verification as suspicious or rejecting the result, and outputting the result which passes the verification as an illegal manned detection result to the target terminal. According to the method, the problem of balance between the recall rate and the precision in manned detection can be solved.

A mechanism for processing video data is disclosed. The mechanism includes determining to apply an adaptive looper filter (ALF) with an extended tap to a picture in a video. An intermediate filtering result of a second filter is used as input for the extended tap. A conversion is performed between a visual media data and a bitstream based on the ALF.

Systems and methods are provided herein for only including portions of a user's environment that have been approved by a user in a video conference while excluding portions that have not been approved. This may be accomplished by a device receiving a policy identifying one or more approved objects of a scene of a video stream. The device may then generate a filtered video stream by only including portions of the scene that comprise the one or more objects that were approved by the policy in the filtered video stream. The filtered video stream may be combined with other video streams to generate a video conference that is transmitted and / or stored by one or more devices participating in the video conference.

An apparatus comprising an interface and a processor. The interface may be configured to receive pixel data of an environment. The processor may be configured to process the pixel data arranged as video frames, generate a luminance distribution map of the video frames in response to a low-pass filter operation, determine a plurality of defogging intensity weights for the luminance distribution map, perform adaptive smoothing to each of the plurality of defogging intensity weights, and generate defogged video frames in response to the video frames and the plurality of defogging intensity weights with the adaptive smoothing. The plurality of defogging intensity weights may each correspond to one of a plurality of luminance intervals of the luminance distribution map. The adaptive smoothing may be configured to prevent brightness differences in the defogged video frames.

A video decoder is configured to determine, from the encoded video data, a block of a picture; apply a neural network (NN)-based filter to the block to generate a filtered block, wherein applying the NN-based filter comprises transforming the block of the picture with a transform block, wherein transforming the block of the picture with the transform block comprises rounding a floating point value to a nearest integer; determine a decoded version of the block based on the filtered block; and output a decoded version of the picture comprising the decoded version of the block.

Example methods and devices are described for processing video data. An example device includes one or more memories configured to store a reconstructed block of the video data and one or more processors in communication with the one or more memories. The one or more processors are configured to receive encoded video data, the encoded video data representing a block of the video data. The one or more processors are configured to reconstruct the block based on the encoded video data to generate the reconstructed block. The one or more processors are configured to perform a neural network (NN)-based filter process on the reconstructed block to generate a filtered block, wherein as part of performing the NN-based filter process, the one or more processors are configured to apply one or more residual groups to a residual group input, each of the residual groups comprising a respective attention block.

A mechanism for processing video data is disclosed. The mechanism includes determining to signal a processing order or a preferred processing order of different post-processing filters, including zero or more neural-network post-filters (NNPFs) and zero or more non-NNPF post-processing filters, in a supplemental enhancement information (SEI) processing order SEI message. A conversion is performed between a visual media data and a bitstream based on the SEI processing order SEI message.

A method and an apparatus are disclosed for video coding CC-ALF based on a nonlinear cross-component relation. A video decoding device obtains a reconstructed frame that is an output of a sample adaptive offset (SAO) filter and generates an adaptive loop filter output (ALF output) by inputting the reconstructed frame into an adaptive loop filter (ALF). The ALF output includes a luma ALF output and a chroma ALF output. The video decoding device generates corrected values of a chroma component by inputting the luma ALF output into a nonlinear cross-component ALF (nonlinear CC-ALF) and generates an enhanced chroma ALF output by summing the corrected values of the chroma component and the chroma ALF output.

Methods and apparatus for temporally consistent video denoising directed at removing image noise. According to one example, a denoising algorithm uses a noise estimation block and an image denoising block operatively connected to one another. In various examples, the noise estimation block is implemented using a neural network or a filter arrangement including an entropy filter and is designed to analyze noisy frame sequences and generate a noise strength map. With this noise strength map, the image denoising block operates to perform the image denoising more efficiently, effectively reducing the image noise while preserving the texture of the original footage. In some examples, a joint loss objective is used to find configurations of both blocks that result in nearly optimal performance of the denoising algorithm.

The invention provides a debris flow disaster early-warning method based on image recognition, and the method comprises the steps: firstly obtaining two groups of comparison video stream data of a multi-view camera in different time periods, carrying out the mountain feature registration of the two groups of comparison video stream data to guarantee the consistency of space coordinates, filtering and removing invalid interference information through an interference region mask, and obtaining a pre-warning result; and then determining a mountain sliding association difference pixel point set according to the filtered video stream data, and finally constructing a three-dimensional mountain sliding monitoring model in combination with the set and the registered video stream data, thereby determining a target monitoring area of the detection equipment and carrying out monitoring and early warning on the target monitoring area. Spatial position deviation of video stream data in different time periods is eliminated through mountain feature registration, various interference factors in the natural environment are effectively eliminated in combination with interference area mask filtering, mountain sliding can be accurately recognized, the debris flow disaster early warning precision is remarkably improved, the early warning signal false alarm condition is reduced, and the early warning efficiency is improved. And potential safety hazards caused by false alarms are reduced.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing comprises: determining, from a plurality of filter shapes during a conversion between a current video block of a video and a bitstream of the video, a first filter shape for coding a first sample of the current video block; and performing the conversion based on the first filter shape. Compared with the conventional solution, the proposed method can advantageously improve the performance of the filtering tool.

A video encoder and video decoder are configured to determine a filter from a first temporal layer having a first temporal layer ID, determine a scaling factor for the filter from a second temporal layer having a second temporal layer ID, and store the scaling factor in a buffer for future usage based on the second temporal layer ID being less than or equal to the first temporal layer ID.

A video encoding and decoding method and apparatus are disclosed, and relate to the computer field. An encoder side (a decoder side) selects a first type of filter from a plurality of specified types of filters based on a processing parameter indicated by rendering information, and obtains a virtual reference frame of to-be-processed data by using the first type of filter.

A video capture device may be encode a set of original pictures to create encoded video data, decode the encoded video data to create a set of reconstructed pictures, determine a subset of parameters to update from among a plurality of parameters of a post-processing filter network, including using the set of original pictures as ground truth and the set of reconstructed pictures as input to the post-processing filter network, update the subset of parameters to generate updated parameters, and send the encoded video data and the updated parameters to a playback device.

The various implementations described herein include methods and systems for coding video. In one aspect, a video bitstream includes a current image frame having a current coding block and signals a first syntax element for a residual template cross-component residual model (RT-CCRM) mode. When the RT-CCRM mode is enabled, the computing system identifies, in the current coding block, a first chroma sample and one or more luma samples corresponding to the first chroma sample, determines one or more residuals of the one or more luma samples in the current coding block, and applies a residual filter corresponding to the RT-CCRM mode to generate a first residual of the first chroma sample based on the residuals of the one or more luma samples. The computing system reconstructs the current image frame by compensating a predicted chroma sample with at least the first residual to reconstruct the first chroma sample.

An example device for filtering video data includes a memory configured to store video data; and a processing system comprising one or more processors implemented in circuitry, the processing system being configured to: apply one or more neural network processing blocks to intermediate filtered video data, each of the neural network processing blocks including a first 1×1 convolutional filter, a parametric rectified linear unit (PReLU) filter, a second 1×1 convolutional filter, and a 3×3 convolutional filter; apply additional neural network processing blocks to output of the one or more neural network processing blocks to form filtered video data; and output the filtered video data.

A method of processing video data performed by a decoder includes: decoding a bitstream to obtain video data and coding information, the coding information comprising weighting map indication information for defining a weighting map and filter coefficients optimized for the weighting map; obtaining a picture block based on the video data; upsampling the picture block; determining the weighting map using the weighting map indication information; and obtaining an enhanced picture block by applying a signal enhancement filter using the filter coefficients, together with the weighting map, to the upsampled picture block.

The invention relates to the technical field of non-contact physiological signals, and discloses a remote physiological signal detection method based on an adaptive filter, a terminal and a medium. The method comprises the following steps: obtaining a to-be-detected face video, and obtaining a noise rPPG signal through noise processing; performing feature extraction on the face video to obtain a multi-scale space-time diagram; fusing the noise rPPG signal and the multi-scale space-time diagram to obtain fused data; performing denoising processing on the fused data by using a denoising module to obtain denoised data; the de-noising module adopts an adaptive filter de-noising module, the interior of the de-noising module is composed of multiple layers of filter structures, and each layer of filter structure comprises a fixed spectrum shaping filter, a scene adaptive shaping filter and a spectrum period feature extraction filter which are connected in sequence; and outputting a predicted rPPG signal according to the denoised data. According to the method, deep fusion and denoising are carried out on the noise and the multi-scale spatial-temporal characteristic data by using the adaptive filter, and high-precision rPPG signal estimation is realized.

A video decoder is configured to determine, from encoded video data, a block of a picture; apply a neural network (NN)-based filter to the block to generate a filtered block, wherein to apply the NN-based filter, the one or processors process the block by a backbone block, wherein to process the block by the backbone block, the one or more processors are configured to: process input data for the block by a first activation layer; process an output of the first activation layer by a first convolution layer; process an output of the first convolution layer by a second activation layer; process an output of the second activation layer by a second convolution layer; and determine the filtered block based on an output of the second convolution layer.