496 results about "Noise (video)" patented technology

An adaptive digital image processor precedes an MPEG2 encoder. The processor receives a high definition video signal intended for broadcast or storage, and adaptively low-pass filters the signal. The signal is subjected to low-pass two-dimensional filtering to eliminate encoding artifacts and related noise. The video signal is then horizontally down-sampled to create a lower resolution hybrid signal. A receiver decodes and decompresses the hybrid signal. The hybrid signal is upsampled to its original resolution using existing hardware and software with a software modification.

Input data comprising a video signal is processed using a combination of a known image processing method to deblur, or sharpen, the image and convolution with Pixon™ kernels for smoothing. The smoothing process utilizes a plurality of different size Pixon™ kernels which operate in parallel so that the input data are convolved with each different Pixon™ kernel simultaneously. The smoothed image is convolved with the point response function (PRF) to form data models that are compared against the input data, then the broadest Pixon™ kernel that fits the input data within a predetermined criterion are selected to form a Pixon™ map. The data are smoothed and assembled according to the Pixon™ map, then are deconvolved and output to a video display or other appropriate device, providing a clearer image with less noise.

This invention relates to the creation of dictionary functions for the encoding of video signals using matching pursuit compression techniques. After an initial set of reference dictionary images is chosen, training video sequences are selected, and motion residuals are calculated. High energy portions of the residual images are extracted and stored when they match selection criteria with the reference dictionary. An energy threshold is used to limit the number of video signal “atoms” encoded for each frame, thus avoiding the encoding of noise. A new dictionary is then synthesized from the stored portions of the image residuals and the original reference dictionary. The process can then be repeated using the synthesized dictionary as the new reference dictionary. This achieves low bit rate signals with a higher signal-to-noise ratio than have been previously achieved.

A video signal is encoded in a progressive video coder so as to generate a progressive coded video bit stream for transmission over a heterogeneous network. The progressive coded video bit stream is configured so as to be decodable at any one of a series of increasing bit rates up to a maximum bit rate, depending on which of a number of corresponding portions of the progressive coded video bit stream are received by a decoder. Each of the portions is associated with a different bit rate, and one or more of the portions may each also be associated with different values of other parameters such as frame rate, spatial resolution, and peak signal-to-noise ratio. Each of the series of increasing bit rates produces progressively better reconstructed video quality at an output of the decoder. The progressive coded bit stream is transmitted over a first part of the heterogeneous network at a first one of the bit rates. One or more selected portions of the progressive coded video bit stream are then transmitted from the first part of the heterogeneous network to a second part of the heterogeneous network. The selected portions are associated with a second one of the bit rates lower than the first bit rate, and may be selected based on an error detected in the transmission over the first part of the heterogeneous network, and / or a characteristic of the second part of the heterogeneous network. The invention provides efficient bit rate scalability and adaptability and is particularly well-suited for use in conjunction with transmission over heterogeneous wired-to-wireless networks.

A method for video filtering of an input video sequence by utilizing joint motion and noise estimation comprises the steps of: (a) generating a motion-compensated video sequence from the input video sequence and a plurality of estimated motion fields; (b) spatiotemporally filtering the motion compensated video sequence, thereby producing a filtered, motion-compensated video sequence; (c) estimating a standard deviation from the difference between the input video sequence and the filtered, motion-compensated video sequence, thereby producing an estimated standard deviation; (d) estimating a scale factor from the difference between the input video sequence and the motion compensated video sequence; and (e) iterating through steps (a) to (d) using the scale factor previously obtained from step (d) to generate the motion-compensated video sequence in step (a) and using the estimated standard deviation previously obtained from step (c) to perform the filtering in step (b) until the value of the noise level approaches the unknown noise of the input video sequence, whereby the noise level is then characterized by a finally determined scale factor and standard deviation.

The invention discloses video noise reduction device and a video noise reduction method. The method comprises the following steps of: obtaining a brightness difference histogram of a current image by using a denoising result of a previous frame of image and a gradient magnitude histogram of the current image; carrying out noise level evaluation on the current image according to the brightness difference histogram; calculating the spatial distance of any two pixel points in the current image, so as to obtain the spatial similarity of the any two pixel points; carrying out denoising on the current image according to the spatial similarity; calculating a pixel time domain distance between any pixel point in the current image and the pixel point at the position corresponding to the previous frame of denoised image, and calculating the corresponding time domain similarity; carrying out three-dimensional recursive denoising on the video image according to the obtained time domain similarity, the spatial similarity denoising result and the previous frame of denoising result. By adopting the device and method disclosed by the invention, three-dimensional recursive denoising is carried out by using the pertinence of the pixel in space and time, so that strong complicated noise can be removed; an image detail can be kept; the stability of the denoising effect also can be ensured.

The invention provides a self-adaptive real-time denoising scheme for a 3D digital video image, which is used for filtering and denoising a video image in a space domain two-dimensional mode and a time domain one-dimensional mode. The method comprises the following steps of: detecting the motion intensity of the video image, estimating a noise standard difference of the image and setting two different threshold value limits according to an estimation result of the noise standard difference; and adjusting parameters in time domain filtering in a self-adaptive mode according to a relation between the motion intensity and the limits and weighting a time domain filtering result and a space domain filtering result according to the relation in a self-adaptive mode to obtain a denoised output result. In the scheme, the computational load and memory space are small, the 3D digital video image can be denoised effectively in real time, borders and details of the image are protected, and the watching comfort of the 3D digital video image is enhanced greatly.

The invention discloses an adaptive noise intensity video denoising method which is based on motion detection and is embedded in an encoder. The method comprises the following steps: (1) taking a sum of regularization frame differences in a neighborhood as an observed value, dividing input pixels into a static pixel and a dynamic pixel and using filters in different supporting domains for the two kinds of the pixels, wherein a filtering coefficient is adaptively determined according to noise intensity and an image local characteristic; (2) taking a single DCT coefficient or the sum of the several DCT coefficients as the characteristic, using AdaBoost as a tool to construct a cascade-form classifier and using the classifier to select a static block; (3) establishing a function model of connection between DCT coefficient distribution parameters of the video noise intensity and the static block and using the model to estimate the noise signal standard difference. By using noise intensity estimation embedded in the video encoder and a noise reduction technology provided in the invention, few computation costs can be used to acquire the parameters and the information needed by noise filtering. A time efficiency is good. Because a reliable clue is used to determine whether the pixels accord with a static hypothesis, the filter of the invention can effectively filter the noise and simultaneously maintain marginal sharpness of the static image. And motion blur caused by filtering in a motion area can be avoided.

A method for filtering digital video is disclosed. The method generally includes the steps of (A) checking a plurality of original blocks in an original field for a plurality of artifacts, wherein detection of the artifacts is based on an adjustable threshold, (B) first filtering each of the original blocks having at least one of the artifacts to remove the at least one artifact and (C) second filtering each of the original blocks lacking at least one of the artifacts to remove noise, wherein the second filtering is (i) motion compensated and (ii) adaptive to a respective noise level in each of the original blocks.

A method and apparatus for reducing noise and motion artifacts in pixels of a processed or displayed video image by filtering pixel values of the video image based on a first frame having currently stored (filtered) pixel values and a second frame having recently captured but not yet filtered pixel values. The apparatus includes a spatial filter for computing a motion value of a pixel of interest by averaging difference values of selected pixels surrounding the pixel of interest. Also included is a filter function means for producing an output difference value of the pixel of interest based on the motion value and an adder for adding the output difference value to the first frame filtered pixel value of the pixel of interest. Thus, each pixel of the video image is filtered according to the amount of motion in the video image.

A computerized system for up-scaling a source input video from a lower, first resolution to a desired output video having a higher, second resolution, using fractal zooming techniques to replace each individual source pixel of each respective frame of the source input video with a multiple of proposed replacement pixels in the vertical and horizontal dimensions having similar characteristics as the individual source pixel, converting each of the proposed replacement pixels into a desired color space for the desired output video, using low-pass filtering to reduce noise associated with each respective frame of the desired output video, down-sizing the output video to its desired resolution, and outputting each zoomed replacement frame to generate the desired output video.

The invention discloses a Mean shift-based video target tracking method, which extracts the SIFT characteristics of the tracking target and then matches the SIFT characteristics of the target by the Mean-Shift algorithm to realize the target tracking. The method makes full use of the characteristics of invariance against rotation, size scaling and brightness change and high resistance against noise interference and the like. The invention not only considers the real-time performance of the algorithm, but also can well solve the problems of size scaling, object obstacle, rotation, illumination change and the like. The technology has wide application prospect and development potential in the fields of safety monitoring, automobile auxiliary movement, human body movement analysis, robot vision and so on, and can improve the real-time performance and accuracy of the target tracking.

The invention discloses a low-light video image reinforcing method. The method includes the following steps that video image intra-frame reinforcement is achieved; a frame image of a video undergoes intra-frame reinforcement, the reinforcement parameters are calculated and image reconstruction is carried out according to reinforced image information; video image inter-frame reinforcement is achieved; the frame image serves as a standard frame image; features of a next frame image of the standard frame image are compared with features of the standard frame image, whether the standard frame image needs to be reconstructed or not is judged and intra-frame reinforcement is carried out on the next frame image according to the reinforcement parameters of the determined standard frame image. The method is carried out until the whole video image is reinforced. Gray stretch is carried out on the low-light part of the image, noise will not be reinforced in the reinforcement process and basic information of image gray is maintained.

Methods and systems are provided to determine a target tracking box that surrounds a moving target. The pixels that define an image within the target tracking box can be classified as background pixels, foreground pixels, and changing pixels which may include pixels of an articulation, such as a portion of the target that moves relatively to the target tracking box. Identification of background image pixels improves the signal-to-noise ratio of the image, which is defined as the ratio between the number of pixels belonging to the foreground to the number of changing pixels, and which is used to track the moving target. Accordingly, tracking of small and multiple moving targets becomes possible because of the increased signal-to-noise ratio.

An apparatus and method are described for filtering noise internally within a video encoding framework. In various embodiments of the invention, an in-loop noise filter is integrated within an encoding device or framework that reduces noise along a motion trajectory within a digital video signal. This integration of in-loop noise reduction allows both noise filtering parameters and encoding parameters to be more easily related and adjusted. The in-loop noise filter leverages characteristics of digital video encoding processes to reduce noise on a video signal and improve encoding efficiencies of a codec.

The invention relates to a video compression method based on sparse samples, and belongs to the technical field of video compression. The method comprises the steps: S1, data preprocessing; S2, firstly, learning each frame of a video in a data set by utilizing a variation auto-encoder through a video generation method combining the variation auto-encoder and a generative adversarial network, constructing a hidden space with good continuity, and enabling each point in the hidden space to correspond to one frame in the video; inputting the noise and the text into a generator of the generative adversarial network, enabling the generator to generate a plurality of associated points in the latent variable space, and finally generating continuous images are generated through a decoder of a variational auto-encoder; S3, inputting the generated continuous images into a video compression model, screening background frames through a CNN network, and then identifying a target in each frame of image by using a YOLO neural network. According to the invention, the video compression efficiency can be improved, and the network transmission delay and the consumption of local resources are reduced.

The invention provides a coding method, a decoding method, a coder and a decoder of a three-dimension video. The coding method comprises the following steps. Firstly, a depth map and a texture map of a reference viewpoint are coded and a reference viewpoint coding reconstruction depth image and a reference viewpoint coding reconstruction texture image are obtained. A target viewpoint synthesis reference frame is obtained through three-dimension geometric transformation based on the reference viewpoint coding reconstruction depth image, the reference viewpoint coding reconstruction texture image and corresponding camera parameters. A reference viewpoint original texture image is obtained as an original signal and the target viewpoint synthesis reference frame is used as a plus-noise signal to carry out wiener filtering, an optimized target viewpoint synthesis reference frame is obtained and wiener filter parameters are worked out. The optimized target viewpoint synthesis reference frame is added into a reference frame set and the wiener filter parameters are written into a code flow. The coding method, the decoding method, the coder and the decoder of the three-dimension video have the advantages of improving coding efficiency and improving video quality.

The invention provides a method for detecting a video monitoring system and a device thereof; a standard definition television test signal and a high definition television test signal are generated by a signal source generator; the test signals are accessed in a video channel of a video monitoring system to be detected, and with a video index of the video monitoring system to be detected, video data is collected, analyzed and tested by an automatic analyzer based on the control of a single board computer of a computer; and the test result is displayed in the form of graphic and data. The invention can more scientifically and objectively assess the signal index of digital television video equipment, can carry out the test on video indexes of random signal-to-noise ratio, synchronization amplitude, white strip amplitude, luminance nonlinearity, amplitude frequency characteristic and the like of the video monitoring system, simultaneously has the function of image display of the video to be detected, can generate gray scale signals of 11 grade, and meets the detecting requirement of the field of video monitoring; furthermore, the invention has simple structure, convenient use, lower detecting cost, and is applicable to on-site detection and fill the emptiness of special detection instrument in the detection field of domestic and foreign video monitoring project.

A method and apparatus for reducing noise and motion artifacts in pixels of a processed or displayed video image by filtering pixel values of the video image based on a first frame having currently stored (filtered) pixel values and a second frame having recently captured but not yet filtered pixel values. The apparatus includes a spatial filter for computing a motion value of a pixel of interest by averaging difference values of selected pixels surrounding the pixel of interest. Also included is a filter function means for producing an output difference value of the pixel of interest based on the motion value and an adder for adding the output difference value to the first frame filtered pixel value of the pixel of interest. Thus, each pixel of the video image is filtered according to the amount of motion in the video image.

The invention discloses a video watermark embedding method and a video watermark extracting method, wherein the video watermark embedding method comprises the steps as follows: magnifying at a small scale a selected video frame image requiring watermark embedding and conducting self-adaption segmentation processing on a macro block and a subblock; selecting a transformation coefficient and embedding a watermark based on singular value decomposition (SVD) of the subblock and discrete cosine transformation (DCT) of the macro block; reconstructing the video frame image through inverse discrete cosine transformation, inverse process of singular value decomposition and small-scale shrinkage of the image. The video watermark extracting method corresponds to the embedding method, and the watermark extracted from a video frame is subjected to a series of post-processing to obtain a final watermark sequence. The methods provided by the invention have good universality, the video frame for watermark embedding and the selection of the transformation coefficient improve the invisibility and the robustness of the watermark, and the watermark embedding and extracting efficiencies are improved while the attacks against transcoding, noises, frame insertion, frame deletion, resolution change and the like are resisted.

A method and apparatus are provided for streaming scalable video over fading wireless channels. The method includes building a model relating to a relationship between an average data rate and an average peak signal-to-noise ratio for a video sequence encoded using scalable video coding and having a base layer and one or more enhancement layers. The method also includes computing a vector relating to a set of average data rates for a particular boundary point on an achievable rate region for a transmission strategy. The boundary point is a function of a parameter set for a plurality of users. The achievable rate region is based upon the model. The method further includes scheduling the plurality of users to receive the video sequence over a wireless channel, such that at a given transmission time slot a particular one of the plurality of users associated with a maximum value is selected. The maximum value is based on the vector and a channel capacity available to the particular one of the plurality of users.

The invention discloses a stereoscopic video quality evaluation method based on a binocular minimum discernible distortion model, which includes: firstly building a binocular stereoscopic brightness relation model, and researching respective pixel brightness of a left viewpoint video and a right viewpoint video and the brightness relation of the pixel brightness of the left viewpoint video and the right viewpoint video when they integrate to form a stereoscopic video ; building a stereoscopic image just noticeable difference (JND) model according to background brightness and texture masking, and building a stereoscopic video JND model according to a visual threshold value masked by video interframe; obtaining a visual threshold value of a function based on space-time contrast sensitivity through the function based on space-time contrast sensitivity and relative parameters of a display device; obtaining a binocular JND model by combining the stereoscopic video JND model and the visual threshold value of the function based on space-time contrast sensitivity; and finally building a binocular perception peak signal-to-noise ratio based on stereoscopic video quality for evaluating stereoscopic video quality. The method is based on the binocular JND model, leads evaluation of stereoscopic video quality to be identical with video quality subjectively perceived by human eyes, and correctly reflects human eye visual stereoscopic video perception quality.

A video processing device (100) includes: an edge histogram generating section (141) and a noise reducing section (150). The generating section (141) generates an edge histogram of a decoded image obtained from a video signal. The noise reducing section (150) (i) determines whether or not a ratio of a sum of frequencies of classes higher than a predetermined class of an edge histogram to a sum of all frequencies the edge histogram is equal to or greater than a predetermined threshold value and (ii) performs a smoothing process on the decoded image with a smoothing intensity adjusted in accordance with whether or not the ratio is equal to or greater than the predetermined threshold value.

A system and method for lossy image and video compression and transmission that utilizes a neural network as a function to map a known noise image to a desired or target image, allowing the transfer only of hyperparameters of the function instead of a compressed version of the image itself. This allows the recreation of a high-quality approximation of the desired image by any system receiving the hyperparameters, provided that the receiving system possesses the same noise image and a similar neural network. The amount of data required to transfer an image of a given quality is dramatically reduced versus existing image compression technology. Being that video is simply a series of images, the application of this image compression system and method allows the transfer of video content at rates greater than existing technologies in relation to the same image quality.

Acquired video signals are forwarded to pre-processing block (103) for sampling, gain amplification and A / D conversion, and then forwarded to image buffer (104). Video signals in image buffer (104) are forwarded to noise estimation block (106). On the basis of taking conditions, video signals and a reference noise model, noise estimation block (106) works out the quantity of noise per ISO sensitivity, and per color signal. The calculated noise quantity is forwarded to noise reduction block (105). On the basis of the noise quantity estimated at the noise estimation block (106), the noise reduction block (105) applies noise reduction processing to video signals in image buffer (104), and video signals after noise reduction processing are forwarded to signal processing block (108).

A adaptive noise reduction filter is provided for reducing noise in a video signal. Each pixel in a portion of a video frame is evaluated to determine a likelihood L of impulse noise corruption to each pixel. A total number P of pixels in the video frame that have a likelihood of impulse noise corruption is determined. One of a plurality of spatial noise reduction filters is selected to use on the video frame based on the total number P and on the likelihood L of impulse noise corruption to a current pixel. A motion value for each of the pixels in the portion of the video frame may be determined and used to inhibit spatial noise reduction filtering of each pixel that has a low motion value.

A method for restoring video data of a pipe based on computer vision is provided, including: performing gray stretching on pipe image / video collected by a pipe robot; processing noise interference by smoothing filtering; extracting an iron chain from the center of a video image as a template for location; performing target recognition on the center of video data by an SIFT corner detection algorithm; detecting ropes on left and right sides of a target by Hough transform; performing gray covering on the iron chain at the center of the video image and the ropes on two sides; and restoring data by an FMM image restoration algorithm.

A technique to reduce ringing artifacts in highly compressed block-based image / video coding is applied to each reconstructed frame output from the decoder. For each pixel block of a reconstructed frame, low-pass filtering is then adaptively applied according certain calculated differences between adjacent pixel values. For each pixel, a determination is made as to what type of horizontal filter, if any, is to be applied. Depending on the results of that determination, the pixel may remain unfiltered or may have a 2- or 3-tap horizontal filter applied to it. A similar process is undertaken to determine what type of vertical filter, if any, is to be applied, no filter, a 2-tap or a 3-tap vertical filter.

The present invention discloses a method for detecting semantic event in video and a system thereof. The method of the invention comprises the following steps: normalizing movement vector and filtering noise movement vector; estimating camera movement; dividing movement object; and detecting semantic event. The system of the invention comprises the following components: a movement vector preprocessing module which is used for normalizing the movement vector and filtering the noise movement vector with an experience regulation; a camera movement estimating module which is used for determining the movement mode and movement parameter of camera; a movement object dividing module which is used for dividing movement object with the movement vector after movement compensation of camera; and a semantic event detecting module which is used for detecting the semantic event in the video lens. The method and system for detecting semantic event in video can detect the semantic event conception in the video more accurately and high-efficiently.

The invention relies to a video data mining-based non-contact type object vibration frequency measurement method. In the prior art, a contact type vibration transducer, installed on an object and used for measuring the vibration of the object, causes the problems of complicated techniques, high cost and unachievable installation on a to-be-measured object can be solved by adopting the above method of the invention. The method comprises the steps of constructing an ideal camera noise model for a digital camera to take photos, calculating the vibration frequency of each pixel in the video according to the ideal camera noise model, and obtaining the vibration frequency of a to-be-measured object. According to the technical scheme of the invention, the defects of the conventional v contact type object vibration frequency measurement method are overcome. Meanwhile, the defect that the existing machine vision-based non-contact type object vibration frequency measurement method is limited by constraint conditions is also overcome. The above method is applied to off-line video data and online video data. Moreover, the method is simple in equipment, low in cost, good in adaptability and wide in application range.