458 results about "Noise (video)" patented technology

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.

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.

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.

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 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.

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 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.

Media content typically includes closed captioning information such as subtitles in domestic and foreign languages. Techniques and mechanisms provide that closed captioning information may be toggled on/off using menu options and preferences as well as automatically managed by intelligently monitoring the environment surrounding a device. Device sensors such as microphones and vibration monitors determine the noise level of an environment as well as the spectral characteristics of the noise to determine whether the noise profile would interfere with the video playback experience. A particular environmental noise profile could automatically trigger the display of closed captioning information or present an easy access, otherwise unavailable toggle to display closed captioning information associated with a video stream.

A video-signal processing method for measuring a noise level of an input video signal includes the steps of detecting an intra-field or intra-frame feature for each region that, is set in the input video signal, the intra-field or intra-frame feature representing a noise level in the input video signal within a field or frame; checking the intra-field or intra-frame feature against a noise-free-region threshold, and excluding regions with which the infra-field or intra-frame feature is less than the noise-free-region threshold from subjects of noise-level measurement; and outputting a result of noise-level measurement by statistically processing the intra-field or intra-frame features of regions remaining without being excluded.

The invention belongs to the technical field of computer vision, and discloses a video target tracking method. According to the invention, fused HOG and LBP features are used as the input of relative filtering tracking. Compared with traditional and pure gray value and HOG features, the fused feature can better describe a target. The HOG features are used to better describe the edge and other features of an image. The LBP features are used to better describe the texture features of the image. The noise impact is overcome. The combination of HOG and LBP features can make tracking more stable.

The invention belongs to the technical field of neural network security, and particularly relates to a backdoor attack method of a video analysis neural network model. For more severe backdoor attackimplementation environments such as a high sample dimension, a high frame resolution and a sparse data set of a video, a video backdoor pollution sample construction framework is used for carrying outbackdoor attack on a video analysis neural network model. The video backdoor pollution sample construction framework comprises three parts: a task-oriented model high-sensitivity video backdoor modegeneration algorithm, a feature fuzzy model low-sensitivity anti-noise video sample generation algorithm and a pollution sample generation and attack algorithm; gradient information is introduced fromtwo aspects of a backdoor mode and an original sample to establish association between an attack target value and the backdoor mode. The method provided by the invention has the advantages of high attack success rate, high secrecy, good robustness, good expansibility and the like, and has very good generalization in a video analysis neural network model.