1009 results about "RGB color space" patented technology

An apparatus and method for image data compression performs a modified zero-tree coding on a range of image bit plane values from the largest to a defined smaller value, and a vector quantizer codes the remaining values and lossless coding is performed on the results of the two coding steps. The defined smaller value can be adjusted iteratively to meet a preselected compressed image size criterion or to meet a predefined level of image quality, as determined by any suitable metric. If the image to be compressed is in RGB color space, the apparatus converts the RGB image to a less redundant color space before commencing further processing.

Strategies are described for processing image information in a linear form to reduce the amount of artifacts (compared to processing the data in nonlinear form). Exemplary types of processing operations can include, scaling, compositing, alpha-blending, edge detection, and so forth. In a more specific implementation, strategies are described for processing image information that is: a) linear; b) in the RGB color space; c) high precision (e.g., provided by floating point representation); d) progressive; and e) full channel. Other improvements provide strategies for: a) processing image information in a pseudo-linear space to improve processing speed; b) implementing an improved error dispersion technique; c) dynamically calculating and applying filter kernels; d) producing pipeline code in an optimal manner; and e) implementing various processing tasks using novel pixel shader techniques.

A method for coding an RGB color space signal is disclosed. In accordance with the method, a base plane is encoded using an independent mode, and an enhanced plane is encoded by referring to the base plane without converting the RGB color space signal into YCbCr color space signal to reduce a redundancy between RGB planes and improve a compression ratio of an image.

The invention relates to a dynamic contrast enhancement device and a dynamic contrast enhancement method. The device comprises a color space switching module, a histogram statistical module, an enhanced mapping function module and a brightness transformation module, wherein the color space switching module is used for switching an input image data from a red, green and blue (RGB) color space to aluma and chroma (YUV) color space; the histogram statistical module is used for counting a gray histogram of an image according to a brightness component Y; the enhanced mapping function module is used for calculating weights of a plurality of brightness component intervals in the histogram and obtaining an adaptive mapping function f according to the designed mapping table of each brightness component interval and the weights; and the brightness transformation module is used for transforming the brightness Y of the image to a novel brightness Y' according to the adaptive mapping function f, and outputting the brightness in the RGB color space through the color space switching module. By the device, the image is divided into the plurality of brightness component intervals, the weights arecalculated respectively, and the adaptive mapping function is obtained by a weighting method to transform the image brightness; therefore, the processed image can keep a mean brightness, the contrastis effectively improved, and the problems of level reduction and details loss caused by the traditional histogram equalization are solved.

A system and method for matching a color with a corresponding color in a defined color space scans an object having the color to be matched and produces a color image data signal that is representative of the object, maps the color image data signal to the defined color space to find the corresponding color, and informs a user of the corresponding color.

The invention discloses a driving safety monitoring system based on face orientation and visual focus, which comprises a vision sensor and an intelligent processor. The intelligent processor comprises an image acquisition module, a complexion area detection module, an edge extraction module, an eye detection module, a face orientation analysis module and a driver safety driving judgment module, wherein the complexion area detection module is used for converting an RGB color space into a YCrCb color space and detecting a complexion area; the edge extraction module is used for adopting a Canny operator to carry out edge extraction so as to acquire a head image outline; the eye detection module is used for carrying out Hough conversion for the marginalized face and positioning eyes; the face orientation analysis module is used for determining a mouth area, positioning the mouth, calculating left and right areas of the face respectively by using the eyes and the mouth as reference, and calculating the ratio of the left and right areas of the face; and the driver safety driving judgment module is used for judging an unsafe driving state if the calculated ratio of the left and right areas of the current face is out of a preset interval according to the preset interval of the ratio of the left and right areas of the face, and sending out an alarm command. The driving safety monitoring system has strong capacity of resisting disturbance and high accuracy.

An image sensing apparatus comprising: a CCD, optical system for forming an image on the CCD; A / D converter for converting an image signal outputted by the CCD to a digital signal; color interpolator for performing color interpolation on the digital signal converted by the A / D converter and generating image data on R, G and B color planes; color space converter for converting RGB color space to YUV colorimetric system; and median filter for reducing pseudo color components, generated by the color interpolator, by controlling color difference signals U and V. By cutting high frequency components of the signal, filtered by the median filter, then thinning out YUV signals and performing JPEG compression, pseudo color components generated by color interpolation can be reduced.

The present invention discloses a road detection algorithm of fusion of area and edge information. The algorithm comprises: collecting road image, and employing self-adaption median filtering and noise reduction to obtain enhanced images; selecting the R component of the enhanced images in the RGB color space, employing a maximum between-cluster variance (Otsu) method to realize the road area segment of the images to obtain a binary segmentation image, and optimizing the binary segmentation image through adoption of the serial mathematical morphology; detecting the binary segmentation image through adoption of the optimal edge detection Canny detector to detect the binary segmentation image and obtain the road edge detection information, and employing the Otsu method to calculate the dual threshold in the optimal edge detection Canny detector; and employing the binary segmentation image and the road edge detection information to obtain a road border line in the image. The road detection algorithm of fusion of area and edge information employs the image processing technology to detect and extract the road information for the autonomous navigation system of a cable tunnel robot; and moreover, the road detection algorithm of fusion of area and edge information is a self-adaption road border line segmentation fusion method and can obtain a smooth and accurate road edge.

Foreground images, such as cursors, may be displayed over an image by selecting individual pixel colors to contrast with surrounding background pixels. The background pixels in, around and behind the foreground image may be converted from a red-green-blue (RGB) color space to a luminance isolating color space, such as YUV, HSL or the like. The converted pixel information may be copied, stored, or drawn into a separate compositing window. After converting to the luminance isolating color space, the luminance values of the pixels may be adjusted to increase the contrast between the foreground image and the background image. Portions of the foreground image may also be blurred, such as by applying a Gaussian or box blur, such as to soften the edges. The pixel information may then be converted back into the RGB color space and blended with the background information using alpha information for the foreground and background images.

The invention provides an image reinforcement method, an image reinforcement device and a display device. The image reinforcement method comprises the steps of: converting an original image from a RGB (red-green-blue) color space into an HIS (Hue-Saturation-Intensity) color space; maintaining the tone component, and respectively performing reinforcement processing on the brightness component and the saturation level component to obtain a processed image; and converting the processed image from the HIS color space into the RGB color space. The image reinforcement method provided by the invention converts the image from the RGB color space into the HIS color space to perform the reinforcement processing so as to avoid the defect of color loss.

The contamination level estimation method for high voltage insulators collects samples of naturally contaminated insulators and builds an image data set for the collected insulators. Flashover voltages of several insulators samples are measured. ESDD levels of the collected insulators are estimated. Images are input to image processing algorithms to extract representative features. The images are segmented. Transforming the image from RGB color space into grayscale model excludes the background from the image. Subsequently, the segmented images are transferred back to RGB color space model using matrix manipulation. Since contaminants on the insulator surface affect the color of the insulator, the segmented image is transformed from RGB to HSV color space which is used for extracting statistical and linear algebraic features from the hue image. A trained artificial neural network correlates the extracted features to the contamination levels enabling testing of other contaminated insulators.

The invention discloses an enhancement method for a low-illumination image. The enhancement method comprises the steps that the low-illumination image to be processed is acquired and then the low-illumination image to be processed is transferred to an HSV color space from an RGB color space, and a chroma component, a saturation component and a brightness component are acquired; the brightness component is decomposed into a reflection component and an irradiation component by adopting an alternating minimization method based on a Retinex algorithm; the irradiation component and the reflection component are respectively enhanced and then synthesized into an enhanced brightness component; the saturation component is self-adaptively adjusted and then an enhanced saturation component is obtained; the chroma component, the enhanced brightness component and the enhanced saturation component are synthesized into a new HSV image; and the obtained new HSV image is converted into an RGB image, and an enhanced image is obtained through white balance processing. Definition of the low-illumination image can be greatly enhanced, and details are enabled to be reproduced so that the method is high in applicability and high in robustness and can be widely applied to the field of image processing.

The invention discloses a deep-convolution-neural-network-based super-resolution reconstruction method of a single image. The method comprises: step one, carrying out pretreatment; to be specific, converting an inputted image from an RGB color space to a YCbCr color space and only take a Y channel; step two, carrying out down sampling on the image after the pretreatment at the step one and carrying out interpolation by two channels to form training data of the channel 1 and the channel 2; step three, carrying out dense small block extraction on the image after the pretreatment at the step one and using a result as a tag; step four, with a gradient descent method, and a reverse conduction algorithm, optimizing a network model continuously by using the combined training data of the channel 1 and channel 2 as the input of a deep convolution neural network model and the tag as the output of the deep convolution neural network model; and step five, inputting a low-resolution image, carrying out dual-channel interpolation and then outputting a high-resolution image by using the trained deep depth convolution neural network. The method has advantages of light structure and good recovery quality.

The invention relates to an image-processing-based insulator defect detection method and system. An image including an insulator is transformed from an RGB color space into an HIS color space, segmentation is carried out to obtain single-channel images of a tone component and a saturability component; intersection taking is carried out to extract an insulator contour, and morphological corrosion, dilation operation, regional growth, and connection region marking are carried out on an operator using an oval as a structural element in the insulator contour image; according to areas of all connection regions, a non-insulator image is rejected from the image obtained after the regional growth operation to obtain a pseudo standard binary image; and an image obtained by a closed operation is compared with the pseudo standard binary image and an insulator defect is determined based on the comparison result. The detection method is easy to implement; the operation becomes simple; and the operation speed increases.

The embodiment of the invention discloses an image feature extraction method and a device thereof, wherein, the image feature extraction method comprises the steps as follows: a weighted image is generated according to a collected image and a pixel of the weighted image is obtained from weighting the sum-difference ratio of a corresponding pixel of the pixel of the weighted image in a RGB color space and the gray value of a corresponding pixel in a Lab color space; a binary image is generated according to the weighted image and comprises a target image and a background image; a hole of the target image in the binary image is filled and noise in the binary image is also removed; edge images of the target image after filling and contour-rectangle images of the edge images are both obtained; and as to any contour-rectangle image, the center and radius of a corresponding circle is obtained according to the calculation of the edge image in the contour-rectangle image. The image feature extraction method and the device thereof proposed solve problems in the recognition of a target-segmented image when targets are more, overlapped with each other and blocked, and are characterized by accurateness and high speed.

The invention relates to a method for fire identification through infrared and visible-light video image fusion. A fire is identified through an infrared image preprocessing module, a suspicious infrared image judging module, an infrared image flame determining module, and a visible light fusion comprehensive judgment module. The infrared image preprocessing module is used for carrying out graying treatment, region connection, binarization and dilation operation on an image. The suspicious infrared image judging module is used for calculating the difference between the gray-scale map and the binary image to determine a suspicious image of which the scene changes. The infrared image flame determining module is used for calculating the similarity between two sequential frames of suspicious image to determine whether there is flame. The visible light fusion comprehensive judgment module is used for judging whether the RGB value of a corresponding visible-light image meets a color model of flame based on the model of the flame in an RGB color space and finally judging whether there is a fire. Fusion identification of infrared and visible-light images is realized, missing and false fire reporting is avoided, and the accuracy of fire identification is improved.

The invention relates to a facial beautification method and electronic equipment in a real-time video. The facial beautification method comprises the steps of obtaining video frames through a camera; determining skin color areas and non-skin color areas through skin color detection, respectively converting an original image into HSV (Hue-Saturation-Value) and RGB (Red-Green-Blue) spaces, determining areas within a range threshold of limited skin color in the HSV and RGB color spaces as the skin color areas and other areas as the non-skin-color areas, and separating the skin areas from the image; integrally performing pyramid bilateral filtering on the original image, wherein noisy points are considered if a certain threshold is exceeded, and performing medium filtering on the noisy points; directly replacing the skin areas in the original image with images after filtering, and fusing the original image and the images after filtering by the non-skin color areas. By adopting the facial beautification method, the skin color of figures in the video is obviously beautified, the facial beautification efficiency in the real-time video is improved, and the user experience effect is increased.

A color measuring device includes a storage unit configured to store therein colorimetric values corresponding respectively to colors constituting a reference chart in a predetermined color space that is device-independent; an image capturing unit configured to capture the reference chart and a subject for color measurement simultaneously to acquire RGB values of the reference chart and RGB values of the subject; a search unit configured to search for RGB values of four points corresponding to vertices of a polyhedron in the reference chart, the polyhedron including a specified RGB value of the subject in an RGB color space; a calculating unit configured to calculate a linear transformation matrix for converting the RGB values of the four points into the corresponding colorimetric values; and a conversion unit configured to convert the specified RGB value into a corresponding colorimetric value in the predetermined color space based on the linear transformation matrix.

This invitation discloses a method of encoding / decoding a color QR (Quick Response) code. The method maintains the reliability and robustness of a standard black and white QR code, and employs a lossless compression algorithm to make the data capacity of a color QR code much larger than the data capacity of other colored two-dimensional code of a same kind. In addition, encoding colors are not required to be the most distant from each other in an RGB color space in order to enhance color resolution. A process of encoding / decoding a color QR code does not require color space transfer, i.e., color information loss can be avoided. Color QR cod decoding has no special requirements for ambient lighting, and does not need to utilize reference colors as a palette, thereby greatly reducing computational complexity, and obtaining 100% decoding accuracy.

The invention provides a traffic sign detection method in a natural scene. The method comprises the steps that a detection image shot in the natural scene is acquired; luminance information of the detection image is subjected to statistical analysis, different luminance areas are divided according to grade luminance threshold values, pixel ratios of different luminance areas are calculated respectively, and the image is divided into a dark scene, a bright scene, a backlighting scene and a normal scene according to all the pixel ratios and scene classification threshold values; gamma parameter values are selected according to scene classification results, and an adaptive Gamma enhancement algorithm is adopted to perform image enhancement processing on a classification image; a partitioning algorithm is selected to perform image color partitioning according to different scenes in an RGB color space to obtain suspected target areas; a grayscale image obtained after color partitioning is subjected to binarization processing to obtain the suspected target areas after binarization; and the suspected target areas are screened through a feature screener to position traffic sign regions. Through the method, robustness and real-time performance of sign detection can be both considered.

The embodiment of the invention discloses a color gamut adjusting method and a device for a color image. The color gamut adjusting method comprises the following steps: carrying out color space conversion on the color image in an RGB (Red, Green, Blue) color space to obtain tone of each pixel point; evenly dividing a circular distribution diagram into N axes according to the number N of primary colors, wherein the axes represent the primary colors, and angles formed between the axes represent assorted colors; according to the position of the tone of each pixel point in the color image on the circular distribution diagram and a preset algorithm, calculating a saturation gain value of each pixel point, and adjusting the saturation of each pixel point according to the saturation gain value of each pixel point; converting the color image after saturation adjustment back to the RGB color space; converting the color image after saturation adjustment from the RGB color space to an RGBW (Red, Green, Blue, White) color space and outputting the color image. According to the embodiment of the invention, the saturation is adjusted according to different tones to enable the color gamut of an input image to fall in a color gamut range which can be displayed by an output display, and a display effect of the image can be improved.

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.

The invention discloses a color fusion method of a night vision low-light image and an infrared image based on color transfer. The present invention first linearly fuses visible light and infrared images to obtain the , , and components of the gray-scale fused image. Utilize the brightness and color distribution of the reference image to adjust the gray-scale fusion image, and adjust the adjusted image data from YUV space back to RGB space to obtain the final color fusion image. The fusion image obtained by the method of the present invention and the color reference The brightness distribution of the image is similar and has a good visual effect.

The present invention provides a multi-camera images stitching and color homogenizing method, comprising the following steps of: S1, increasing luminance of each image by using Gamma correction; S2, selecting an image that has greatest image information entropy from the images with increased luminance as a reference image, wherein the remaining picture are target images; S3, transforming each image from an RGB color space to a YCbCr color space; S4, adjusting the color and luminance of the target images to be consistent with those of the reference image in the YCbCr color space by using the reference image as a reference; S5, performing a color space inverse transformation on each image, to obtain images that locate in the RGB space. S6, performing stitching on the images by using an image homonymy points matching algorithm, to obtain a preliminary panorama image; and S7, performing contrast enhancement processing on the obtained preliminary panorama image, to obtain a luminance and color balanced panorama image. The multi-camera images stitching and color homogenizing method of the present invention is simple and quick in calculation method, and can be applied to color homogenizing processing of color images.

The invention discloses a low illumination image color preserving enhancement method based on improved Retinex. The method is concretely performed according to the following steps that a low illumination filtering input diagram I is transformed to a YUV color space from an RGB color space, and a grayscale map is used as the filtering input diagram I; the Y component RY, FGF, i of the reflection component R of the enhanced image are obtained by using the improved Retinex method; the UV component of the enhanced image is calculated; and the enhanced image is transformed to the RGB color space from the YUV color space, and color component feedback type enhancement is performed on the transboundary pixel points so as to obtain the enhanced low illumination color image. The fast guide filter, the single scale Retinex and multichannel color preserving enhancement are fused, the enhanced image color is abundant and brightness and detail enhancement is obvious so that the problems of high time cost and halo, detail blurring and color distortion in the prior art can be solved.

The invention relates to a method for detecting the color cast of a video image based on Lab chrominance space. The method comprises the following steps: a) converting RGB (Red Green Blue) color space into the Lab chrominance space; b) calculating color cast factors. The invention provides a new color cast detecting factor, which is successively used for detecting the color cast of the image. First, the image is converted from the original RGB color space into the Lab chrominance space; then according to the distinction of the spatial gray level histogram distribution of a chrominance and b chrominance between a normal image without color cast and an abnormal image with color cast, the mean value of the spatial gray level histogram distribution of the a chrominance and the b chrominance are respectively calculated; then according to the statistical distribution between the mean value and the mid value of histograms, the color factors are calculated. A lot of experiments of real street view image databases prove that the new color cast detecting factor disclosed by the invention can really reflect the degree of color cast of images so as to complete color cast detection. The method has the advantages of high detection speed and accurate precision.

The invention relates to the technical field of color space conversion and discloses a method and a system for processing RGB (red, green and blue) data. The method includes the following steps of firstly, converting three colors of input R, G and B into a HSV (hue, saturation and value) color space; secondly, adjusting the value of luminance V on the premise of keeping the value H (hue) and the value S (saturation) in the HSV color space constant; thirdly, converting the value H, the value S and the value V (value), which are processed in the second step, into the RGBW (red, green, blue and white) color space to be displayed. By converting the RGB color space into the HSV color space, promoting the luminance V on the premise of keeping the H and the S in the HSV color space constant, and converting the HSV color space into the RGBW color space then, display luminance can be improved when the hue, the saturation and natural transition of colors can be kept better.

A new color model HMMD is disclosed based upon hue, the shade, the tone, the tint and the brightness of a color, and a color quantizing method using the HMMD color space. The present invention regards the colors adjacent to a given color in the color space as the neighboring colors to consider indices of the neighboring colors as the partial values according to the distance. Accordingly, the present invention can accomplish a color quantization close to the change of the color sensed by the human eye, thereby capable of enhancing a performance of the image searching system based on content.< / PTEXT>