YCbCr Space Research Articles

An Improved QIM Watermarking Algorithm

Put forward an improved QIM watermark embedding algorithm. The carrier image is transformed into YCbCr space, lifting wavelet transform the three components, then using the QIM quantization method embeds watermark into high frequency coefficients of Cb and Cr component, and Y component of low frequency coefficients. The experimental results show that embedded watermark which using improved algorithm has good invisibility. Anti attack test results show that after using common attack method attack the image embedded watermark, it can still extract the watermark completely. The anti-attack performance of improved QIM algorithm is much better than texture watermarking algorithm based on DCT.

A false color image fusion method based on multi-resolution color transfer in normalization YCBCR space

In this paper, a false color image fusion method for merging visible and infrared images is proposed. Firstly, the source images and reference image are decomposed respectively by Laplacian pyramid transform. Then the grayscale fused image and the difference images between the normalized source images are assigned to construct YCBCR components. In the color transfer step, all the three channels of the color space in each decomposition level are processed with the statistic color mapping according to the color characteristics of the corresponding sub-images of the reference image. Color transfer is designed based on the multi-resolution scheme in order to significantly improve the detailed information of the final image, and to reduce excessive saturation phenomenon to have a comparatively natural color appearance compared with the classical global-coloring algorithm. Moreover, the differencing operation between the normalized source images not only provides inter-sensor contrast to make popping the potential targets but also weakens the influence of the ambient illumination variety to a certain degree. Finally, the fused results and several metrics for evaluation of fused images subjectively and objectively illustrate that the proposed color image fusion algorithm can yield a more complete mental representation of the perceived scene, resulting in better situational awareness.

A fast algorithm for YCbCr to perception color model conversion based on fixed-point DSP

Real-time video processing heavily relies on the color space conversion. Due to the real-time requirement, the traditional conversion methods often suffer from the moderate conversion speed, inaccuracy and low video quality. In order to meet the efficiency requirement of color space conversion in real-time video systems, we present a fast algorithm for color space YCbCr to perception color model conversion, which is based on the simplified shift and look-up table (SSLUT). The approach can be divided into two steps. At first, the simplified fixed-point shift method is used to convert YCbCr to RGB. Then look-up table assists with the YCbCr to perception color model conversion, such as HSV, HSI and HSL. To validate the proposed fast algorithm, the conversion speed and accuracy are compared with the traditional methods based on Code Composer Studio (CCS) test platform. Moreover, we make the video quality evaluation by using the peak signal-to-ratio (PSNR) and the structural similarity (SSIM). Experimental results illustrate the real-time, robustness and accuracy of the fast algorithm.

Measurement Coding for Compressive Sensing of Color Images

From the perspective of reducing the sampling cost of color images at high resolution, block-based compressive sensing (CS) has attracted considerable attention as a promising alternative to conventional Nyquist/Shannon sampling. On the other hand, for storing/transmitting applications, CS requires a very efficient way of representing the measurement data in terms of data volume. This paper addresses this problem by developing a measurement-coding method with the proposed customized Huffman coding. In addition, by noting the difference in visual importance between the luma and chroma channels, this paper proposes measurement coding in YCbCr space rather than in conventional RGB color space for better rate allocation. Furthermore, as the proper use of the image property in pursuing smoothness improves the CS recovery, this paper proposes the integration of a low pass filter to the CS recovery of color images, which is the block-based l<SUB>20 </SUB>-norm minimization. The proposed coding scheme shows considerable gain compared to conventional measurement coding.

Multi-Resolution Kronecker Compressive Sensing

From the perspective of reducing the sampling cost of color images at high resolution, block-based compressive sensing (CS) has attracted considerable attention as a promising alternative to conventional Nyquist/Shannon sampling. On the other hand, for storing/transmitting applications, CS requires a very efficient way of representing the measurement data in terms of data volume. This paper addresses this problem by developing a measurement-coding method with the proposed customized Huffman coding. In addition, by noting the difference in visual importance between the luma and chroma channels, this paper proposes measurement coding in YCbCr space rather than in conventional RGB color space for better rate allocation. Furthermore, as the proper use of the image property in pursuing smoothness improves the CS recovery, this paper proposes the integration of a low pass filter to the CS recovery of color images, which is the block-based -norm minimization. The proposed coding scheme l<SUB>20</SUB> shows considerable gain compared to conventional measurement coding.

Face Detection Algorithm Based on Multi-orientation Gabor Filters and Feature Fusion

In order to enhance the accuracy of multi-pose and multi-expression face detection, this paper proposes an algorithm based on multi-orientation Gabor feature fusion of mean and variance of sub-images. Firstly, to remove the huge background regions, we segmented images based on YCbCr space and then used two-eye templates to locate faces in skin-color regions by template matching. Secondly, the features were extracted from the face regions which had been removed the nose parts. After filtering by four-direction Gabor filters, the images were divided and then the corresponding constituent parts were formed. Thirdly, we calculated the mean and variance of the constituent parts block by block and processed them with features fusion. At last Support Vector Machine (SVM) and Back Propagation net (BP) were used for classification detection. The experimental results show that the algorithm has higher detection accuracy than other similar algorithms in multi-pose and multi-expression facial detections. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.3432

A Method of Hand Signal Segmentation Based on YCbCr Space and Background Difference

This paper proposes a method of segmentation of hand signal, based on the skin color model in YCbCr space and background subtraction under complex background. This paper discussed the reasonable threshold selection of Cb, Cr in the skin color model and the segmentation selection of skin area combining the background segmentation. Finally, an inequality of hands outline feature is proposed to complete the division processing of the palm part of the area of skin. Experiments show the accurate segmentation of gesture under a complex static background.

A Robust Face Detection Method Based on Skin Color and Edges

In this paper we propose a method to detect human faces in color images. Many existing systems use a window-based classifier that scans the entire image for the presence of the human face and such systems suffers from scale variation, pose variation, illumination changes, etc. Here, we propose a lighting insensitive face detection method based upon the edge and skin tone information of the input color image. First, image enhancement is performed, especially if the image is acquired from an unconstrained illumination condition. Next, skin segmentation in YCbCr and RGB space is conducted. The result of skin segmentation is refined using the skin tone percentage index method. The edges of the input image are combined with the skin tone image to separate all non- face regions from candidate faces. Candidate verification using primitive shape features of the face is applied to decide which of the candidate regions corresponds to a face. The advantage of the proposed method is that it can detect faces that are of different sizes, in different poses, and that are making different expressions under unconstrained illumination conditions

Texture Feature Extraction to Colorize Gray Images

is work presents a simple process for gray image colorization, using a colored image which is similar to this gray scale image but not the colorized version of the gray image. This colored image is retrieved from the data base of colored images that has been created for this purpose. Here, the texture properties of the colored images are extracted and stored. For the purpose of colorization these features are compared with those of the gray image to be colorized and the best matching image is found out from the database. For colorization of this gray scale image a decorrelated color space YCbCr is utilized. This technique is completely automatic and no human intervention is required in the process of colorization. Apart from this the technique presented here is very fast and produces good quality results as compared to the conventional colorization methods. Texture features used here to calculate a texture similarity measure are energy, entropy, contrast, homogeneity, autocorrelation based on correlation matrix as well as coarseness and directionality.

Single-Channel Color Image Encryption Using the Reality-Preserving Fractional Discrete Cosine Transform in YCbCr Space

A novel single-channel color image encryption algorithm is proposed, which utilizes the reality-preserving fractional discrete cosine transform in YCbCr space. The color image to be encrypted is decomposed into Y, Cb, and Cr components, which are then separately transformed by Discrete Cosine Transform (DCT). The resulting three spectra sequences, obtained by zig-zag scanning the spectra matrices, are truncated and the lower frequency coefficients of the three components are scrambled up into a single matrix of the same size with the original color image. Then the obtained single matrix is encrypted by the fractional discrete cosine transform, which is a kind of encryption with secrecy of pixel value and pixel position simultaneously. The encrypted image is convenient for display, transmission and storage, thanks to the reality-preserving property of the fractional discrete cosine transform. Additionally, the proposed algorithm enlarges the key space by employing the generating sequence as an extra key in addition to the fractional orders. Simulation results and security analysis demonstrate the proposed algorithm is feasible, effective and secure. The robustness to noise attack is also guaranteed to some extent.

A New Algorithm for Human Face Detection Using Skin Color Tone

Human recognition systems have gained a considerable attention during last decade due to its vast applications in the field of computer and advantages over previous biometric methods. There are many applications with respect to security, sensitivity and secrecy. Face detection is the most important and first step of recognition system. This paper introduces a new approach to detection systems using the skin color of a subject. This system can detect a regardless of the background of the picture, which is an important phase for identification. The images used in this system are color images which give additional information about the images than the gray images provide. In detection, the two respective classes are the face and the non-face This new approach to detection is based on color tone values specially defined for skin area detection within the image frame. This system first resizes the image, and then separates it into its component R, G, and B bands. These bands are transformed into another color space which is YCbCr space and then into YC'bC'r space (the skin color tone). The morphological process is implemented on the presented image to make it more accurate. At last, the projection area is taken by this system to determine the area. Experimental results show that the proposed algorithm is good enough to localize a human in an image with an accuracy of 92.69%.

Hybrid Color Image Compression Using Discrete Cosine Transform and Discrete Wavelet Transform

In this research we undertake a study of image compression based on the discrete cosine transform(DCT) and discrete wavelet transform(DWT). Then a hybrid color image compression algorithm based on DCT and DWT is proposed. This algorithm is implemented through transform the color image using DWT in the YCbCr space first, and then DCT in the low frequency, adopt huffman coding, RLE and arithmetic coding in the encoded mode. In experiments, the results outperform the only DCT and the only DWT typically higher in peak signal-to-noise ratio and have better visual quality.

Face Detection Method Using PCNN and Skin Color Model

This paper presented a face detection method for the color image using pulse coupled neural network (PCNN) and skin color model. The color image which is processed well through light compensation is converted from RGB to YCbCr color space, then the skin area are divided into sub-block, and skin color segmentation is made for the image in YCbCr space. Finally, we use PCNN to extract all sub-block ignition time sequence, and calculate various sub-block difference degrees between target face and the tested image, if the difference degree is the smallest, then the target face himself is the same person. Experimental results show that the proposed method has higher accuracy and robustness, can obtain satisfactory detection effect.

Video Vehicle Detection Method Based on Multiple Color Space Information Fusion

A detection method which selective fuses the nine detection results of RGB, YCbCr and HSI color space according to the image color space relative independence of each component and complementarities is approached in order to improve vehicle video detection accuracy. The method fuses three different detection results in nine components by the value of H when the value of both S and I are higher and does another three detection results when the value of both S and I are smaller. Experiments show that the method compared to the traditional method using only the detection results of the brightness component improved substantial, reduced empty of the detected vehicle a large extent and increased traffic information data accuracy depending on the detection result.

Color Image Encryption Based on the Multiple-Order Discrete Fractional Cosine Transform and Chaos in YCbCr Space

Based on the theory that a color image can be decomposed into three primary components and each one can be seen as a gray image, we propose a color image encryption method with multiple-order discrete fractional cosine transform (MODFrCT), which is a kind of encryption with the secrecy of pixel value and pixel position simultaneously. The complex number mode that has a real part and an imaginary one is used in this encryption method to save the transmission channel. Human vision is more sensitive to the Y component than to other two components in YCbCr color space and this color format is used for encrypting the color image. Chaos is introduced to scramble the image phases both in spatial and transformation domains. The numerical simulations demonstrate the validity and efficiency of this scheme and the robustness of the method against occlusion attack is examined.

Comparison of Feature Selection Processes for Image Retrieval Applications

A process of choosing a subset of original features, so called feature selection, is considered as a crucial preprocessing step to image processing applications. There are already large pools of techniques developed for machine learning and data mining fields. In this paper, basically two methods, non-feature selection and feature selection, are investigated to compare their predictive effectiveness of classification. Color co-occurrence feature is used for defining image features. Standard Sequential Forward Selection algorithm are used for feature selection to identify relevant features and redundancy among relevant features. Four color spaces, RGB, YCbCr, HSV, and Gaussian space are considered for computing color co-occurrence features. Gray-level image feature is also considered for the performance comparison reasons. The experimental results are presented.

A real-time face detection and recognition system for a mobile robot in a complex background

This article presents a real-time face detection and recognition system for mobile robots based on videos with a complex background. In the visual system, we propose a multi-information method consisting of an Adaboost algorithm, and color information for the face detection part. The interesting targets in the video will first be detected by the Adaboost algorithm, which is robust to illumination. Then the skin color model in YCbCr space will be employed to select the parts that may not be skin areas from the information detected by the Adaboost algorithm. An embedded hidden Markov model (EHMM) is presented, using a 2-DCT feature vector as the observation vector, to recognize the faces detected. The whole process of detecting and recognizing a frame, which is 320 × 240, will take 1.4 s with the rapid recognition parameters and 4.2 s with the slow recognition parameters.

Extraction of color-intensity feature towards image authentication

A color-intensity feature extraction method is proposed aimed at supplementing conventional image hashing algorithms that only consider intensity of the image. An image is mapped to a set of blocks represented by their dominant colors and average intensities. The dominant color is defined by hue and saturation with the hue value adjusted to make the principal colors more uniformly distributed. The average intensity is extracted from the Y component in the YCbCr space. By quantizing the color and intensity components, a feature vector is formed in a cylindrical coordinate system for each image block, which may be used to generate an intermediate hash. Euclidean distance is modified and a similarity metric introduced to measure the degree of similarity between images in terms of the color-intensity features. This is used to validate effectiveness of the proposed feature vector. Experiments show that the color-intensity feature is robust to normal image processing while sensitive to malicious alteration, in particular, color modification.

HSM: A New Color Space used in the Processing of Color Images

Inspired on the techniques used by painters to overlap layers of various hues of paint to create oil paintings, and also on observations of the the arrangement of Short-(S), Middle-(M), and Long-(L) wavelength-sensitive cones of the human retina for the interpretation of the colors, this paper proposes the use of the new color space called HSM to the processing of color images. To demonstrate the applicability of the HSM color space in the processing of color images, this paper proposes the pixelbased segmentation of a digital image of “human skin” or “non-skin”, the sketch of the face image and the pixel-based segmentation of the trumpet flowers tree (ype). The performance of the HSM color space in the pixel-based segmentation is compared with the HSV, YCbCr and TSL color spaces while the sketch of the face image is also compared with HSV, YCbCr and TSL colors spaces and the edge detectors of the Sobel, Prewitt, Roberts, Canny and Laplacian of Gaussian methods. The results demonstrate the potential of the proposed color space.

TRAFFIC VIDEO-BASED MOVING VEHICLE DETECTION AND TRACKING IN THE COMPLEX ENVIRONMENT

Moving vehicle detection and tracking is the key technology in the intelligent traffic monitoring system. For the shortcomings and deficiencies of the frame-subtraction method, a novel Marr wavelet, kernel-based background modeling method and a background subtraction method based on binary discrete wavelet transforms (BDWT) are introduced. The background model keeps a sample of intensity values for each pixel in the image and uses this sample to estimate the probability density function of the pixel intensity. The density function is estimated using a new Marr wavelet kernel density estimation technique. The background and current frame are transformed by BDWT, and moving vehicles are detected in the binary discrete wavelet transforms domain. For the shortages of RGB (Red, Green, Blue) or HSV (Hue, Saturation, Value) color space-based vehicle shadow segmentation algorithms, shadow segmentation algorithm based on YCbCr color space and edge detection is proposed. The original data of the shadow according to the characteristics of the YCbCr space is chosen, and then, combined with edge detection, the shape and location of the vehicle region is determined. An automatic particle filtering algorithm is used to track the vehicle after detection and obtaining the center of the object. An actual road test shows that the algorithm can effectively remove the influence of pedestrians and cyclists in the complex environment, and can track the moving vehicle exactly. The algorithm with better robustness has a practical value in the field of intelligent traffic monitoring.