Two-Field Scheme: Spatiotemporal Modulation for Field Sequential Color LCDs

A novel displaying method, two-field driving scheme, was proposed for field sequential color LCDs without color filter. A spatially modulated color backlight provided multi-primary low-resolution optical stimuli, which were then compensated by an LC panel to display detailed colorful images. That is, the manipulation on three (or more) spatial and two temporal degrees of freedom was sufficient to convey full color information. The simulation results showed that the proposed method achieved acceptable color reproduction accuracy, average CIEDE2000 color difference (DeltaE₀₀) < 3. The least number of fields particularly alleviated the demand for fast-response LC modes in sequential-type LCDs. Furthermore, color break-up suppression was observed due to less chrominance difference between the two fields.     

Significance of incorporating chrominance information for effective color-to-grayscale image conversion

This paper provides an alternative framework for color-to-grayscale image conversion by exploiting the chrominance information present in the color image using singular value decomposition (SVD). In the proposed technique of color-to-grayscale image conversion, a weight matrix corresponds to the chrominance components is derived by reconstructing the chrominance data matrix (planes a* and b*) from the eigenvalues and eigenvectors computed using SVD. The final grayscale converted image is obtained by adding the weighted chrominance data to the luminous intensity which is kept intact for the CIEL*a*b* color space of the given color image. The effectiveness of the proposed grayscale conversion is confirmed by the comparative analysis performed on the color-to-gray benchmark dataset across 10 existing algorithms based on the standard objective measures, namely normalized cross-correlation, color contrast preservation ratio, color content fidelity ratio, E score and subjective evaluation.     

Luminance Adaptive Coding of Chrominance Signals

We describe two techniques for digital coding of the chrominance components of a color television signal. Both techniques make use of an observation that in color pictures most of the locations of large spatial changes in the chrominance are coincident with large spatial changes in the luminance. This allows us to predict the chrominance samples more efficiently using the previously transmitted chrominance and luminance samples, and the present luminance sample. In general, we determine which of the previous luminance samples best represents the present luminance sample and use the corresponding previous chrominance sample to represent the present chrominance sample. We present results of computer simulations of two such coding schemes. The first scheme, in which the chrominance components are coded by a DPCM coder, uses adaptive prediction of the chrominance components based on the luminance. In the second scheme, the chrominance signal is adaptively extrapolated from its past using the luminance signal for adaptation. Only those chrominance samples where the extrapolation error is more than a threshold are transmitted to the receiver. The addresses of such samples are derived from the luminance signal and therefore need not be transmitted. Our computer simulations on videotelephone type of pictures, indicate that, for the predictive coding, the entropy of the coded chrominance signals can he reduced by about 15 to 20 percent by adaptation. This results in a bit rate of 0.55 bits/ luminance pel, for transmission of chrominance information. Using adaptive extrapolation, only about 20 percent of the chrominance samples need to be transmitted which results in a bit rate of approximately 0.58 bits/luminance pel.     

The eidochromatic transform for color-image coding.

We introduce the eidochromatic transform as a tool for improved lossy coding of color images. Many current image-coding formats (such as JPEG 2000) utilize both a color-component transform (relating values of different image components at a single location) and a wavelet or other spatial transform (relating values of a single-image component at proximate, but different image locations). The eidochromatic transform further reduces redundancy by relating image values simultaneously across color components and in the two spatial dimensions. Our approach is to introduce an additional transform step following the color-component and spatial transforms. In tests, this step reduced the overall static entropy of the chrominance components of quantized transformed images by up to 40% or more. Combined with JPEG 2000's modeling and coding method, the eidochromatic transform was found to reduce the size of lossily coded color images by up to 27% overall.     

Quantization of TV Chrominance Signals Considering the Visibility of Small Color Differences

A quantizing method for chrominance signals is presented which takes into account that the visibility of small color differences and the areas of reproducible colors are a function of luminance. For the evaluation of quantizing characteristics and for an estimate of the effectiveness of such a procedure, CIE (Commission International de l'Eclairage) formulas are used. It is proposed to subdivide the color space into several layers of constant luminance signal and to choose, switched by the luminance signal, an individual quantizer for each layer. Such quantizers are calculated for two different coding schemes: quantizing the TV chrominance signalsU_{R-Y}andU_{B-Y}directly or quantizing chrominance signals which have been transformed into a color space derived from a neurophysiological model of vision. Because of practical reasons the quantization ofU_{R-Y}andU_{B-Y}is preferred and elaborated in more detail. The calculated non-linear quantizing grids are approximated by two different methods, with small rectangular elements, or they are piecewise linear approximated by interpolation. To achieve a color difference ofDeltaE_{1964 CIE} = 4.4as maximum distortion, a PCM quantization of 5 bits inU_{R-Y}andU_{B-Y}is required. First order entropies indicate an average bit rate of 3.5 forU_{R-Y}and 2.7 forU_{B-Y}.     

YUV空间中基于稀疏自动编码器的无监督特征学习

现有无监督特征学习算法通常在RGB色彩空间进行特征提取,而图像和视频压缩编码标准则广泛采用YUV色彩空间。为了利用人类视觉特性和避免色彩空间转换所消耗的计算量,该文提出一种基于稀疏自动编码器在YUV色彩空间进行无监督特征学习的方法。首先在YUV空间随机采集图像子块并进行白化处理,然后利用稀疏自动编码器进行无监督局部特征学习。在预处理阶段,针对YUV空间亮度和色度通道相互独立的特性,提出一种将亮度和色度进行分离的白化措施。最后用学习到的局部特征在大尺寸图像上进行卷积操作从而获得全局特征,并送入图像分类系统进行性能测试。实验结果表明：只要对亮度分量进行适当的白化处理,在YUV空间中的无监督特征学习就能够获得相当于甚至优于RGB空间的彩色图像分类性能。     

Linear demosaicing inspired by the human visual system.

There is an analogy between single-chip color cameras and the human visual system in that these two systems acquire only one limited wavelength sensitivity band per spatial location. We have exploited this analogy, defining a model that characterizes a one-color per spatial position image as a coding into luminance and chrominance of the corresponding three colors per spatial position image. Luminance is defined with full spatial resolution while chrominance contains subsampled opponent colors. Moreover, luminance and chrominance follow a particular arrangement in the Fourier domain, allowing for demosaicing by spatial frequency filtering. This model shows that visual artifacts after demosaicing are due to aliasing between luminance and chrominance and could be solved using a preprocessing filter. This approach also gives new insights for the representation of single-color per spatial location images and enables formal and controllable procedures to design demosaicing algorithms that perform well compared to concurrent approaches, as demonstrated by experiments.     

融合亮度信息与色度信息的图像梯度提取方法

梯度信息对人眼视觉感知具有重要作用,准确的图像梯度描述对于图像边缘提取、图像增强、图像分割等都有着极为重要的意义。传统的梯度提取方法通常仅利用图像的亮度信息,当图像颜色发生改变而亮度变化不大时,传统方法获得的梯度数值则会与人眼的视觉感知产生矛盾。提出一种融合亮度信息与色度信息的图像梯度提取方法。该方法在保留亮度梯度信息的基础上,引入基于人眼视觉模型的CIE-L*a*b*颜色模型获得色度梯度信息,而后将归一化的亮度梯度与色度梯度进行了梯度融合。实验结果表明,该方法可充分利用亮度信息与色度信息,产生更符合人眼视觉感知的梯度描述。     

四元数域彩色图像整体式水印算法

该文提出一种结合四元数变换域和四元数分解方法的整体式彩色图像水印算法。首先对彩色载体图像进行分块四元数傅里叶变换得到其频域矩阵,然后对频域单位小块进行四元数奇异值分解,根据分解得到四元数酉矩阵前若干列中对角线元素的幅值构造水印序列,并将水印隐藏到分解得到的实系数奇异值中。仿真实验表明,该文提出的水印方法不仅可以把嵌入水印带来的误差扩散到载体图像的各个颜色分量上,而且比传统的将彩色图像划分为各个单独色彩通道,分别进行水印嵌入的方法不可见性更好,并且,通过构造与奇异值酉矩阵相关的水印序列可以有效解决现有四元数水印算法存在的误检测率以及水印图像易伪造问题。     

On independent color space transformations for the compression ofCMYK images

Device and image-independent color space transformations for the compression of CMYK images were studied. A new transformation (to a YYCC color space) was developed and compared to known ones. Several tests were conducted leading to interesting conclusions. Among them, color transformations are not always advantageous over independent compression of CMYK color planes. Another interesting conclusion is that chrominance subsampling is rarely advantageous in this context. Also, it is shown that transformation to YYCC consistently outperforms the transformation to YCbCrK, while being competitive with the image-dependent KLT-based approach.     

Supervised‐learning‐based algorithm for color image compression

A correlation exists between luminance samples and chrominance samples of a color image. It is beneficial to exploit such interchannel redundancy for color image compression. We propose an algorithm that predicts chrominance components Cb and Cr from the luminance component Y. The prediction model is trained by supervised learning with Laplacian‐regularized least squares to minimize the total prediction error. Kernel principal component analysis mapping, which reduces computational complexity, is implemented on the same point set at both the encoder and decoder to ensure that predictions are identical at both the ends without signaling extra location information. In addition, chrominance subsampling and entropy coding for model parameters are adopted to further reduce the bit rate. Finally, luminance information and model parameters are stored for image reconstruction. Experimental results show the performance superiority of the proposed algorithm over its predecessor and JPEG, and even over JPEG‐XR. The compensation version with the chrominance difference of the proposed algorithm performs close to and even better than JPEG2000 in some cases.     

Dual domain watermarking for authentication and compression of cultural heritage images

This paper proposes an approach for the combined image authentication and compression of color images by making use of a digital watermarking and data hiding framework. The digital watermark is comprised of two components: a soft-authenticator watermark for authentication and tamper assessment of the given image, and a chrominance watermark employed to improve the efficiency of compression. The multipurpose watermark is designed by exploiting the orthogonality of various domains used for authentication, color decomposition and watermark insertion. The approach is implemented as a DCT-DWT dual domain algorithm and is applied for the protection and compression of cultural heritage imagery. Analysis is provided to characterize the behavior of the scheme under ideal conditions. Simulations and comparisons of the proposed approach with state-of-the-art existing work demonstrate the potential of the overall scheme.     

Color contrast enhancement method using steerable pyramid transform

A new method of contrast enhancement based on steerable pyramid transform is presented in this work. The use of steerable filters is motivated by the fact that the images are to be observed by human and therefore it would be better to incorporate some knowledge on the Human Visual System in the design of the image processing tool. Here, the frequency and directional selectivity of the HVS is modeled by the steerable filters. The contrast is amplified using a selective nonlinear function which simulates the nonlinearity response of the HVS to the luminance stimuli. So the basic idea is to enhance the luminance signal irrespective of the two chrominance components using a multidirectional and multiscale decorrelation color transform. Initially the rgb (red, green and blue) color image is converted to lab (luminance and chrominance) color image. Only the luminance component is transformed by the steerable pyramid transform, so that the luminance component is independently decomposed into different scale and orientation sub-bands. The contrast in each sub-band is enhanced using a nonlinear mapping function. Finally the rgb color image is obtained from the enhanced luminance component along with the original chrominance components. The performance of the proposed method is objectively evaluated using spectrum energy analysis and a visibility map based on a perceptual filtering model. The obtained results confirm the efficiency of the method in enhancing subtle details without affecting color balance and without the usual noise amplification and edge ringing effect.     

Automatic Image Segmentation by Dynamic Region Growth and Multiresolution Merging

Image segmentation is a fundamental task in many computer vision applications. In this paper, we propose a new unsupervised color image segmentation algorithm, which exploits the information obtained from detecting edges in color images in the CIE L_*a_*b_* color space. To this effect, by using a color gradient detection technique, pixels without edges are clustered and labeled individually to identify some initial portion of the input image content. Elements that contain higher gradient densities are included by the dynamic generation of clusters as the algorithm progresses. Texture modeling is performed by color quantization and local entropy computation of the quantized image. The obtained texture and color information along with a region growth map consisting of all fully grown regions are used to perform a unique multiresolution merging procedure to blend regions with similar characteristics. Experimental results obtained in comparison to published segmentation techniques demonstrate the performance advantages of the proposed method.     

Color Correction for Multi-view Images Using Relative Luminance and Chrominance Mapping Curves

In order to capture 3D scenes, a multi-view camera consisting of two or more cameras is widely used; however, color consistency among views is not guaranteed in many situations. In this paper, we design relative mapping curves with consideration of the properties of luminance and chrominance components to improve the consistency. The input images are categorized into source and reference views. We convert their color domain to the YUV color space, and estimate coefficients in the mapping curves by analyzing correspondences between the two views. After that, we generate lookup tables and convert the color distributions of the source views. From the experimental results, we confirm that our proposed method improves the visual quality of multi-view images and reduces Euclidean distances in the CIELab color space among views.     

Color image resolution conversion.

In this paper, we look at the problem of spatially scaling color images. We focus on an approach that takes advantage of the human visual system's color spatial frequency sensitivity. The algorithm performs an efficient least-squares (LS) resolution conversion for the luminance channel and a low-complexity pixel replication/reduction in the chrominance channels. The performance of the algorithm is compared to a LS method in sRGB and CIELAB color spaces, as well as standard bilinear interpolation in sRGB space. The comparisons are made in terms of computational cost and color error in sCIELAB.     

Low bit-rate compression of underwater image based on human visual system

Image plays an irreplaceable role compared with the text and sound in the underwater data collection and transmission researches. However, it suffers from the limited bandwidth of the underwater acoustic communication which cannot afford the large image data. Compressing the image data before transmission is an inevitable process in the underwater image communication. As usual, the natural image compression methods are directly applied to the underwater scene. As we all know, underwater image has different degradation from the natural one due to the optical transmission property. Low illumination in underwater will cause more seriously blurring and color fading than that in the air. It is a great challenge to decrease the bit-rate of the underwater image while preserving the compressed image quality as much as possible. In this paper, the Human Visual System (HVS) is taken into account during the compressing and the evaluating stages for the underwater image communication. We present a new methodology for underwater image compression. Firstly, by taking the human visual system into account, the chrominance perception operator is proposed in this paper to neglect the imperceptible chrominance shift which is widely exited in the underwater imaging to improve the image compression rate. Secondly, depth of field(DOF) of underwater image is usually shallow and most of the usable image has targets in it. An ROI extraction algorithm based on Boolean map detection is then used for the underwater image compression so as to reduce the bitrate of the compressed image. Furthermore, the underwater image is grainy and low contrast, that means the degradation happens in some regions of the image would not be perceived. Just notice difference(JND) sensing algorithm based on the spatial and frequency domain masking feature of HVS is also considered in the image processing. By combining the three aspects above, hybrid wavelet and asymmetric coding are used together to promote the underwater image compression, so that the image can have better quality and less redundancy. Experiments show that the proposed method can make full use of the inherent characteristics of underwater images, and maximize the visual redundancy of underwater images without reducing the visual perception quality of reconstructed images.     

Directional processing of color images: theory and experimentalresults

The processing of color image data using directional information is studied. The class of vector directional filters (VDF), which was introduced by the authors in a previous work, is further considered. The analogy of VDF to the spherical median is shown, and their relation to the spatial median is examined. Moreover, their statistical and deterministic properties are studied, which demonstrate their appropriateness in image processing. VDF result in optimal estimates of the image vectors in the directional sense; this is very important in the case of color images, where the vectors' direction signifies the chromaticity of a given color. Issues regarding the practical implementation of VDF are also considered. In addition, efficient filtering schemes based on VDF are proposed, which include adaptive and/or double-window structures. Experimental and comparative results in image filtering show very good performance measures when the error is measured in the L*a*b* space. L*a*b* is known as a space where equal color differences result in equal distances, and therefore, it is very close to the human perception of colors. Moreover, an indication of the chromaticity error is obtained by measuring the error on the Maxwell triangle; the results demonstrate that VDF are very accurate chromaticity estimators.     

Color image coding using morphological pyramid decomposition

Presents a new algorithm that utilizes mathematical morphology for pyramidal coding of color images. The authors obtain lossy color image compression by using block truncation coding at the pyramid levels to attain reduced data rates. The pyramid approach is attractive due to low computational complexity, simple parallel implementation, and the ability to produce acceptable color images at moderate data rates. In many applications, the progressive transmission capability of the algorithm is very useful. The authors show experimental results for color images at data rates of 1.89 bits/pixel.     

Lossless compression of color map images by context tree modeling.

Significant lossless compression results of color map images have been obtained by dividing the color maps into layers and by compressing the binary layers separately using an optimized context tree model that exploits interlayer dependencies. Even though the use of a binary alphabet simplifies the context tree construction and exploits spatial dependencies efficiently, it is expected that an equivalent or better result would be obtained by operating directly on the color image without layer separation. In this paper, we extend the previous context-tree-based method to operate on color values instead of binary layers. We first generate an n-ary context tree by constructing a complete tree up to a predefined depth, and then prune out nodes that do not provide compression improvements. Experiments show that the proposed method outperforms existing methods for a large set of different color map images.