335 results about "YCbCr" patented technology

The invention discloses a color digital image enhancing and denoising method under random illumination. The method is characterized by comprising the following steps of: 1, image denoising processing, namely converting an original image from an RGB space to a YCbCr space, removing a Gaussian noise by using a Gaussian filter and removing a salt and pepper noise by performing median filter; 2, image brightness / contract stretch processing, converting the image from the RGB space to an HSI space, decomposing the image by using a two-sided filter, processing by an improved Retinex model algorithm, and obtaining a new image saturation by performing saturation compensation; and 3, performing fusion display on the image acquired after denoising the YCbCr space and the image acquired by HSI space processing. By the method, the color constancy of the image can be kept, the dynamic range of the image can be improved well, and simultaneously noises of the image can be inhibited and removed with less texture and detail information loss.

The invention provides a method and apparatus for coding information that is specifically adapted for smaller presentation formats, such as in a hand held video player. The invention addresses, inter alia, reducing the complexity of video decoding, implementation of an MP3 decoder using fixed point arithmetic, fast YcbCr to RGB conversion, encapsulation of a video stream and an MP3 audio stream into an AVI file, storing menu navigation and DVD subpicture information on a memory card, synchronization of audio and video streams, encryption of keys that are used for decryption of multimedia data, and very user interface (Ul) adaptations for a hand held video player that implements the improved coding invention herein disclosed.

The present invention is directed to an image information encoding apparatus adapted for performing intra-image encoding based on resolution of color components and color space of an input image signal. An intra prediction unit serves to adaptively change block size in generating a prediction image based on a chroma format signal indicating whether resolution of color components is one of 4:2:0 format, 4:2:2 format, and 4:4:4 format, and a color space signal indicating whether color space is one of YCbCr, RGB, and XYZ. An orthogonal transform unit and a quantization unit serve to also change orthogonal transform technique and quantization technique in accordance with the chroma format signal and the color space signal. A reversible encoding unit encodes the chroma format signal and the color space signal to include the encoded signals into image compressed information.

The invention relates to a human face detection and tracking method based on a Gaussian skin color model and feature analysis. The method comprises the following steps of: firstly, conducting statistics to a large quantity of human face image data and constructing a Gaussian skin color model in a YCbCr color space; then, shifting a video image sequence to the YCbCr space from an RGB (Red, Green and Blue) space, working out a skin color likelihood graph by using the Gaussian model, selecting adaptive threshold values to conduct skin color segmentation and using the geometric features and the structural features of human faces on the basis to realize accurate human face detection; and finally, adopting an improved CAMShift algorithm to track the human faces to realize the rapid detection ofthe human faces in a video. The human face detection and tracking method provided by the invention has obvious advantages in aspects of recognition accuracy, tracking speed and robustness, and can effectively solve the problem in the human face tracking under complex conditions such as the posture change and distance change of the human faces in the video, the likely skin color interference existing in a background and the like.

The invention relates to a dexterous hand teleoperation control method based on Kinect human hand motion capturing. The hardware for realizing the method comprises a Kinect sensor, a computer and a remote robot dexterous hand system. A mixing movement outline model is used for tracking the outline of the hand, hand posture tracking is realized through a particle filter algorithm, the far-end situation is observed through feedback image information transmitted by a robot in real time, and therefore a specific operation task is implemented. Through the movement outline model combining depth information and YCbCr skin color detection space technology, the problem that a real solution can not be obtained easily when an image contains high noise or a target is provided with a weak boundary is solved. A particle filter posture detection method based on template matching is adopted, and the human hand three-dimensional posture with high-dimensional characteristics can be detected and estimated. Through a master-slave operation combining a manual control mode and a program mode, convenience and rapidness are realized, and the guiding performance and usability of human-machine interaction are represented.

Under the premise of no extraneous high-resolution image library, a super-resolution sparse representation method for acquiring a high-resolution image is provided. The method comprises the steps of: (1) carrying out space conversion on a given low-resolution color image to obtain the YCbCr space image of the color image, and reconstructing the constituents of Cb and Cr by using an interpolation method; (2) constructing a database used for training, namely, a high-resolution image block Xh and a low-resolution image block Xl, and combining the two image blocks into a database X; (3) generating a dictionary D from the database X by using a sparse coding method, decomposing the dictionary D into a high-resolution image dictionary Dh and a low-resolution image dictionary Dl; (4) solving a sparse coefficient by using the Dl and characteristic images corresponding to an image of upsampling the low-resolution image by 2 times; (5) solving an image of upsampling the original low-resolution image by 3 times through the sparse coefficient and the Dh; and (6) combining Y, Cb, and Cr to obtain a YCbCr image, and converting the YCbCr image into an RGB image and storing the RGB image to obtain the final super-resolution representation image.

Disclosed is an apparatus for processing a digital signal in an image sensor. The apparatus includes: a line memory unit for storing an inputted digital signal of red (R), green (G) and blue (B); a kernel generation unit for generating a plurality of kernels after receiving the RGB digital signal from the line memory unit; a false color detection unit for detecting false colors by using the plurality of kernels; an edge detection unit for detecting edges by using the plurality of kernels; an interpolation unit for interpolating the RGB digital signal from the line memory unit; a color space conversion unit for converting the interpolated RGB digital signal into a YCbCr signal; and an image enhancement unit for suppressing the false colors and enhancing the edges in the YCbCr signal in response to outputs of the false color detection unit and the edge detection unit.

The invention provides a simple steel seal digital automatic identification method with high accuracy rate. The method comprises the following steps of: first, roughly positioning character region; dividing the character region; and then converting a digital image from a RGB (Red-Green-Blue) space to a YCbCr space; extracting character contour from a Y component of the digital image by using a method combined with Canny operator of block self-adaptive threshold and edge closure; and identifying numbers by using a cascade template matching grouping method. The simple steel seal digital automatic identification method with high accuracy rate, provided by the invention, has the advantages of good detection result edge connection degree, low noise sensitivity, strong real-time property and the like; the steel seal numbers can be accurately identified under the condition of strong interference in real time; and requirements of industrial automatic detection and informatization are satisfied.

Various techniques are provided herein for processing raw image data in front-end processing logic of an image signal processing system. In one embodiment, the front-end processing logic includes a statistics processing unit configured to process raw image data acquired by an image sensor to obtain one or more sets of statistics. The statistics processing unit may first correct defective pixels in the raw image data and then correct lens shading errors in the raw image data prior to extracting the statistics information. In certain embodiments, black level compensation may be applied between the defective pixel correction and lens shading correction steps, and inverse black level compensation may be applied between the lens shading correction step and the extraction of the statistics information. The acquired statistics information may be utilized by an image signal processing pipeline for converting the raw image data into a color (e.g., RGB) and / or luma (e.g., YCbCr) image.

The invention is characterized by the fact that an area sensor for outputting an analog signal responsive to the light reception amount of light of CMYG is used and when CMYG image data is converted into RGB image data, RGB image data having a domain also in negative values is generated without performing processing of putting the RGB image data into values of 0 or more, without decreasing the information amount of the CMYG image data. Further, the invention is characterized by the fact that when the image data is recorded in a file section 16 finally as a JPEG file, the pixel data of each color of YCbCr is represented as data type of eight bits and the information amount per pixel is 24 bits for recording more color information without increasing the memory capacity.

The invention provides a face detection method based on a Gaussian model and a minimum mean-square deviation, and relates to a face recognition technology. A face detection method based on the Gaussian model and the minimum mean-square deviation under the premise of a complicated background, a side face, a stopper and a plurality of faces. The method comprises the following steps of: building a YCbCr Gaussian model: building the YCbCr Gaussian model for face skin color distribution according to collected skin color sample data, and performing lighting compensation on the image, wherein in the YCbCr, Y is a brightness component, Cb is a blue chroma component, and Cr is a red chroma; performing skin color segmentation on the image by using the built YCbCr Gaussian model and the minimum mean-square deviation; performing binaryzation on a skin color region, and processing a binary image by opening to eliminate a small bridge and discrete points; rejecting detected non-face regions in the similar skin color or skin color according to future knowledge of a face; and finally marking a face position by using a rectangular frame.

A digital white balance device is simply implemented in a digital processing scheme by employing a grey world algorithm. In the device, a timing controller receives vertical and horizontal synchronization signals of an input image and produces a timing control signal. An RGB multiplier multiplies input RGB image data by RGB gains received from an RGB gain controller. A first YCbCr averaging unit converts input RGB image data to YCbCr image data, and obtains first YCbCr averages of this YCbCr image data. A second YCbCr averaging unit converts output RGB image data to YCbCr image data, and obtains second YCbCr averages of this YCbCr image data. According to the timing control signal, the RGB gain controller compares the second YCbCr averages with predetermined target YCbCr averages, respectively, and obtains RGB gains based on the first YCbCr averages, according to the compared result, and provides them to the RGB multiplier.

A video signal receiving apparatus comprises a HDMI receiver, signal processing section and control section. The HDMI receiver separates a digital signal conformable to HDMI standards into video component and audio component, determining whether a received digital video signal is a RGB or YCbCr signal, and changing its setting in accordance with the determination result. The signal processing section decodes video signal and audio signal separated by the HDMI receiver to restore analog video signal and audio signal. The control section changes the setting of the HDMI receiver by an operation.

The processing of RGB image data can be optimized by performing optimization operations on the image data when it is converted into the YCbCr color space. First, a raw RGB color space is converted into a YCbCr color space, and raw RGB image data is converted into YCbCr image data using the YCbCr color space. For each Y-layer of the YCbCr image data, a 2D LUT is generated. The YCbCr image data is converted into optimized CbCr image data using the 2D LUTs, and optimized YCbCr image data is generated by blending CbCr image data corresponding to multiple Y-layers. The optimized YCbCr image data is converted into sRGB image data, and a tone curve is applied to the sRGB image data to produce optimized sRGB image data.

Provided are video encoding and decoding methods and apparatuses for encoding a video by variably selecting one from two or more difference color formats. Accordingly, by using at least one of channel state information and a result of encoding a video in a predetermined encoding unit encoded in advance, whether or not to convert a video in a current encoding unit of an input RGB (red, green, blue) color format into a YCbCr color format is adaptively determined to perform encoding. Therefore, a video with uniform quality corresponding to a channel environment having a variable bit-rate can be provided.

A multiple pornographic image classification method based on an image segmentation algorithm and deep learning relates to the technical field of information, especially to the technical field of image identification. The method is characterized by comprising the following four major steps: skin color identification, principal component analysis of the skin color region, deep learning, and pornographic image identification based on a convolutional neural network. First, non-pornographic images are screened out through a skin color pixel detection and skin region partitioning algorithm based on the YCbCr theory. Undetermined images are input to a LeNet5-based convolutional neural network model for identification after feature extraction. Compared with the traditional identification based on skin color and features, the method can eliminate the noise influence of non-body-part images, is not constrained by light and human postures, and can greatly improve the accuracy of traditional pornographic image classification. Compared with general deep learning based on a convolutional neural network, the method does not need massive labeled images, and the characteristics of a deep residual network determine that the model can better analyze the characteristics of pornographic images. Only through about ten hours of training, an identification effect above 90% can be achieved.

A method for detecting human skin tone in a video signal by comparing image data from the video signal to a color profile table obtained from a plurality of training sequences. The image data comprising a plurality of pixels each having a plurality of color components, the components preferably being in the YCbCr color space. The method includes separating image data for each frame in the video signal into sets of data, averaging the image data in each data set to generate mean values for each color component in the data set, comparing the mean values to a stored color profile correlating to human skin tone, and identifying data sets falling within the stored color profile.

A texture compression engine of a graphics device receives an uncompressed texture of a 3D graphic application. The received uncompressed texture is transcoded into an AVC reference picture stream. A plurality of mipmaps is constructed from the received uncompressed texture. The texture compression engine determines a texture compression rate based on available memory capacities. The texture compression engine compresses the received texture and its mipmaps at the determined texture compression rate. The compressed texture and mipmaps are further transcoded into the AVC reference picture stream and stored. The transcoded texture and mipmaps comprise either RGB or YCbCr components for a RGB uncompressed texture. The transcoded texture and mipmaps comprise monochrome or luma components for an ARGB uncompressed texture. A graphics accelerator in the graphics device is operable to acquire the stored texture and mipmaps for a 3D graphics scene. The acquired texture and mipmaps are decompressed by AVC decoding.

The invention relates to a human ear detection method with phase optimization under complex static color background; the method has the following steps of: (1) selecting YCbCr space as a skin color division space, using a Gaussian model as a skin color distribution model, conducting skin color likelihood score conversion and dynamic threshold division to the conversed image; (2) using a morphological method to optimize each divided region and conducting skin color region screening so as to exclude the skin color region not covering human side face and reduce interference; (3) using a wavelet modulus maximum method to detect the image edges under different scales and overlapping edge binary images under different scales, thereby not only accurately detecting the internal and external edges of human ear, but also suppressing noise interference; and (4) realizing human ear detection by conducting expansion, filling, refinement and reconstruction to the edge binary images. The test result shows that the method obtains good effect and is expected to provide useful reference for the development of a human ear automatic identification system.

The invention mainly relates to a method for automatically detecting acupuncture points on the back of a human body by an image processing technology mainly, so that the current situation that doctors artificially and subjectively determine acupuncture points is improved, the efficiency of detecting the acupuncture points is improved, unnecessary manpower is reduced, the foundation for automatic diagnosis and classification systems of diseases and human health automatic analysis systems are laid, and necessary technical support is provided. The method comprises the following steps of calculating YCbCr value of an input image; performing skin color segmentation by an elliptical model to acquire a human body back figure; acquiring the upper edge curve and the lower edge curve of the human body back by using a Canny edge detection algorithm and seeking white points from inside to outside; calculating a midpoint to fit a straight line so as to acquire a human body spine line; rotating the image to acquire a target image; performing grayscale standardization on a sample image; summating and averaging to acquire a template image; matching the target image with the template image; and determining the positions of the acupuncture points of the target image through the positions of the acupuncture points in the template image.

The invention is applicable on the image treatment field, which provides a method, a system and a picture pick-up device for adjusting white balance, wherein the method comprises the following steps switching a image data of a RGB format into a image data of a YCbCr format, estimating the color temperature information of reference white points in images, calculating the gain of channels of R, G and B according to the effective color temperature information, and using the gain of channels of R, G and B to adjust the pixel values of R, G and B in the images. The invention estimates the availability of the color temperature of the reference white point during the white balance adjustment and adjusts the white balance according to the effective color temperature information, which can improve the efficiency and accuracy of the white balance adjustment.

The algorithm is used in the image compression field. It integrates the color space transition, quantification and DCT three steps in order to reduce the operand; while reducing the operand of color transition, except the little adjustment of quantification step length, the structure and operand in both DCT and quantification process steps are not influenced. In case the image sub-sampling is 4:4:4, the new algorithm realizes the 40% and 42% reduction on the multiplication and additive operation in process of transition from RGB to YCbCr in compression process; in the de-compression process, the 60% and 42% reduction on the multiplication and additive operation is realized in the process of transition from YCbCr to RGB.

It is possible to realize a novel video processing device that can utilize the compatibility between the spaces of an encoded image in an xvYCC system with a wide color gamut and an encoded image in the conventional YCbCr system. The video signal processing device (100) detects the type of the recording medium with a video medium type detection portion (104), obtains the color gamut of the video signal with a video color gamut obtaining portion (103), determines a color adjustment parameter with a color adjustment parameter determining portion (106), performs color adjustment on the video signal with a video color adjustment portion (102) based on that parameter, records the color-adjusted video signal to the recording medium.

The invention discloses a traffic light identification method and device, and belongs to the field of traffic safety. The traffic light identification method comprises the steps that an original image is collected; threshold segmentation is conducted on three channel values of the original image in an RGB space to obtain a black region binary image; according to the characteristics of a light panel, the light panel area is positioned in the black region binary image; the portion, in the light panel region, of the original image is converted from the RGB space to a YCbCr space to obtain a red and yellow region binary image and a green region binary image through segmentation; according to the characteristics of traffic lights, traffic light candidate regions are determined on the red and yellow region binary image and the green region binary image; graying and normalization are conducted on the portion, in the traffic light candidate regions, of the original image, and Gabor wavelet transformation is conducted on the image obtained after graying and normalization to obtain a Gabor wavelet image; the amplitude of the Gabor wavelet image is sampled to obtain a feature vector; the similarity between the feature vector and a training sample is compared to determine the types of the traffic lights.

The invention provides a fatigue driving monitoring device and a fatigue driving monitoring method. The fatigue driving monitoring method comprises the steps of: 1) acquiring an acquired image; 2) converting the acquired image into a YCbCr space, extracting a skin color region and carrying out binarization to obtain a face rectangular frame; 3) scanning pixel points in the face rectangular frame, increasing radius by taking each pixel point as a circle center, finishing the scanning until the pixel points exceeding a preset proportion in the scanning regions are white points or the scanning regions reach the boundary of the face rectangular frame, and regarding the diameter of a maximum scanning regions as pupil diameter; 4) and judging whether a driver is fatigued through monitoring the pupil size in real time according to a PERCLOS algorithm. The fatigue driving monitoring device and the fatigue driving monitoring method can form the face rectangular frame in the YCbCr space quickly, simply and accurately, and detects the fatigue state of the driver through combining the real-time pupil size monitoring with the PERCLOS fatigue detection method. The fatigue driving monitoring device and the fatigue driving monitoring method are simple and efficient, fast in operation speed and high in accuracy, and the monitoring process and result are not affected by the inclination angle of face.

The invention discloses a video-based flame detection method, and solves the technical problem in improving the flame detection accuracy. The video-based flame detection method comprises the followingsteps of obtaining a video image sequence; performing image preprocessing; performing color detection; performing flame feature extraction; and performing AdaBoost prediction. Compared with the priorart, the method has the advantages that all candidate flame points meeting a condition in a video image are found out according to characteristics of flame pixels in RGB and YCbCr color spaces; then,the video image is subjected to block segmentation; covariance matrixes corresponding to color and brightness attributes of flame pixel sets in video blocks are calculated; upper or lower triangularparts in the color and brightness covariance matrixes are extracted as eigenvectors; an obtained eigenvector set serves as an input of an AdaBoost classifier; the eigenvectors are input to an AdaBoostclassification model; an output is a judgment whether a fire happens or not; the fire happening situation can be detected out in real time; the false alarm rate can be reduced; and relatively high accuracy and strong robustness are achieved.

A method for detecting human skin tone in a video signal by comparing image data from the video signal to a color profile table obtained from a plurality of training sequences. The image data comprising a plurality of pixels each having a plurality of color components, the components preferably being in the YCbCr color space. The method includes separating image data for each frame in the video signal into sets of data, averaging the image data in each data set to generate mean values for each color component in the data set, comparing the mean values to a stored color profile correlating to human skin tone, and identifying data sets falling within the stored color profile.

A signal conversion apparatus, connected to one or more electronic apparatuses and a display device, mainly includes one or more input interfaces, a signal conversion unit, and an output interface. Each input interface is for connecting to an electronic apparatus and receiving signals therefrom. The input interfaces include analog interfaces and digital interfaces. The signal conversion unit is for receiving the signals in multiple formats from the input interfaces, and converting the signals, when necessary, into a predetermined format, which is supported by the display device. The predetermined format includes RF format, RCA format, S-Video format, YPbPr / YCbCr format, VGA format, MPEG format, DVI format, HDMI format, and so on. The output interface is for transmitting the signals from the signal conversion unit to the display device through a cable. The display device receives the signals from the output interface and displays corresponding information thereof.

An image processing method is provided and includes the following steps. A source image arranged in a Bayer pattern is captured. A first-order image process is performed on the source image to produce a first image in YCbCr format. A second-order image process is performed on the source image to produce a second image in YCbCr format. Then, a noise-reducing process is performed on the first image in YCbCr format to produce a noise-reduced image. A blending operation is performed on luminance image of the noise-reduced image and luminance image of the second image in YCbCr format, and thereafter a chrominance image is combined with the blended image to produce a processed image. A noise reducing degree of the noise-reduced image is higher than that of the second image in YCbCr format.