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。     

Vision-based strategy to reduce the perceived color misregistrationof image-capturing devices

One of the common artifacts for three-row charge-coupled device (CCD) desktop scanners is color misregistration between the red, green, and blue layers of an image. This causes both color fringing and blur in the resulting scanned images, which we quantify by linear system theory analysis. Knowing the bandwidth and peak sensitivity asymmetries in the opponent color representation of the visual system, we develop a method to reduce the color misregistration artifact by attempting to capture signals in an approximate opponent color space. To facilitate separate capture of the luminance and chrominance signals, we use a new sensor arrangement. The luminance signal (Y) is captured at the full resolution using one row of the three-row CCD linear arrays. The first chrominance signal is captured on another row with a interleaved half resolution red (R) and half resolution luminance sensor elements and the second chrominance signal is similarly captured on a third row using blue (B) and luminance. Since each luminance and chrominance signal is isolated on a single row and since there is no registration error within a row, color misregistration is theoretically prevented in luminance as well as in the chrominance signals. Simulation shows that the new method does reduce blur and the visibility of color fringing. Because residual luminance and chrominance misregistration may occur we conduct a psychophysical experiment to judge the improvement in the scanned image quality. The experiment shows that this new capture scheme can significantly reduce the perception of misregistration artifacts. Finally, we use an image processing model of the visual system to quantify the visible differences due to misregistration and compare these to the psychophysical results     

Multiframe demosaicing and super-resolution of color images.

In the last two decades, two related categories of problems have been studied independently in image restoration literature: super-resolution and demosaicing. A closer look at these problems reveals the relation between them, and, as conventional color digital cameras suffer from both low-spatial resolution and color-filtering, it is reasonable to address them in a unified context. In this paper, we propose a fast and robust hybrid method of super-resolution and demosaicing, based on a maximum a posteron estimation technique by minimizing a multiterm cost function. The L1 norm is used for measuring the difference between the projected estimate of the high-resolution image and each low-resolution image, removing outliers in the data and errors due to possibly inaccurate motion estimation. Bilateral regularization is used for spatially regularizing the luminance component, resulting in sharp edges and forcing interpolation along the edges and not across them. Simultaneously, Tikhonov regularization is used to smooth the chrominance components. Finally, an additional regularization term is used to force similar edge location and orientation in different color channels. We show that the minimization of the total cost function is relatively easy and fast. Experimental results on synthetic and real data sets confirm the effectiveness of our method.     

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.     

基于小波的交替集合投射插值方法

大多数的数码相机都是根据特定的模板而使用颜色滤波阵列来对红、绿、蓝的颜色进行采样.每个像素的位置上只有一种颜色的采样,其他的颜色部分则需要插值恢复.这是数码相机的工作流程上一项重要的任务.该文提出了一种新的插值恢复技术,它有效利用了颜色信号之间相关性,采用基于小波的交替集合投射插值算法,这种算法可以实现更好的视觉效果,实验效果令人满意.     

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.     

Fast image and video colorization using chrominance blending.

Colorization, the task of coloring a grayscale image or video, involves assigning from the single dimension of intensity or luminance a quantity that varies in three dimensions, such as red, green, and blue channels. Mapping between intensity and color is, therefore, not unique, and colorization is ambiguous in nature and requires some amount of human interaction or external information. A computationally simple, yet effective, approach of colorization is presented in this paper. The method is fast and it can be conveniently used "on the fly," permitting the user to interactively get the desired results promptly after providing a reduced set of chrominance scribbles. Based on the concepts of luminance-weighted chrominance blending and fast intrinsic distance computations, high-quality colorization results for still images and video are obtained at a fraction of the complexity and computational cost of previously reported techniques. Possible extensions of the algorithm introduced here included the capability of changing the colors of an existing color image or video, as well as changing the underlying luminance, and many other special effects demonstrated here.     

优化式着色算法的混色机制

基于局部关系模型的优化式着色方式,难以定量描述其全局特性信息诸如着色结果与用户输入间关系。该文首先提出一种优化式着色框架,证明两种常用算法为其特例而且等价,然后将每个像素的着色结果转化为各涂抹颜色的组合形式,并通过数学分析推导出组合系数非负且归一化,从而证明优化着色具有混色机制,其结果由涂抹颜色混色而成,并推导出混色权重与首达概率的等价性,最后基于混色机制对现有算法提出改进。实验结果表明混色机制的引入既有助于提高着色效果对涂抹数量与位置的鲁棒性,也便于实时挑选涂抹颜色。     

Color to gray and back: color embedding into textured gray images.

We have developed a reversible method to convert color graphics and pictures to gray images. The method is based on mapping colors to low-visibility high-frequency textures that are applied onto the gray image. After receiving a monochrome textured image, the decoder can identify the textures and recover the color information. More specifically, the image is textured by carrying a subband (wavelet) transform and replacing bandpass subbands by the chrominance signals. The low-pass subband is the same as that of the luminance signal. The decoder performs a wavelet transform on the received gray image and recovers the chrominance channels. The intent is to print color images with black and white printers and to be able to recover the color information afterwards. Registration problems are discussed and examples are presented.     

Spatio-spectral color filter array design for optimal image recovery

In digital imaging applications, data are typically obtained via a spatial subsampling procedure implemented as a color filter array-a physical construction whereby only a single color value is measured at each pixel location. Owing to the growing ubiquity of color imaging and display devices, much recent work has focused on the implications of such arrays for subsequent digital processing, including in particular the canonical demosaicking task of reconstructing a full color image from spatially subsampled and incomplete color data acquired under a particular choice of array pattern. In contrast to the majority of the demosaicking literature, we consider here the problem of color filter array design and its implications for spatial reconstruction quality. We pose this problem formally as one of simultaneously maximizing the spectral radii of luminance and chrominance channels subject to perfect reconstruction, and-after proving sub-optimality of a wide class of existing array patterns-provide a constructive method for its solution that yields robust, new panchromatic designs implementable as subtractive colors. Empirical evaluations on multiple color image test sets support our theoretical results, and indicate the potential of these patterns to increase spatial resolution for fixed sensor size, and to contribute to improved reconstruction fidelity as well as significantly reduced hardware complexity.     

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。     

Color plane interpolation using alternating projections

Most commercial digital cameras use color filter arrays to sample red, green, and blue colors according to a specific pattern. At the location of each pixel only one color sample is taken, and the values of the other colors must be interpolated using neighboring samples. This color plane interpolation is known as demosaicing; it is one of the important tasks in a digital camera pipeline. If demosaicing is not performed appropriately, images suffer from highly visible color artifacts. In this paper we present a new demosaicing technique that uses inter-channel correlation effectively in an alternating-projections scheme. We have compared this technique with six state-of-the-art demosaicing techniques, and it outperforms all of them, both visually and in terms of mean square error.     

多视点视频中基于乘加误差模型的亮度和色差校正

由于相机性能的差异,多视点视频序列之间总是存在亮度和颜色的差异,这种差异会影响多视点视频压缩和显示等后续处理过程的性能.本文由此提出了一种适用于多视点视频的亮度和色差(亮色度)校正算法.根据相机成像原理,不同相机的亮色度差异可以归类为乘性误差和加性误差.灰度级偏移,相机电子元件的放大和量化因子差异等都可以由乘加误差模型表示.为降低计算复杂度,本文采用直方图形状匹配的方法计算乘加误差模型的参数.该算法可作为多视点视频系统中的预处理过程,实验结果表明,该算法能够有效地提高后续压缩过程的性能.     

Demosaicing with directional filtering and a posteriori decision.

Most digital cameras use a color filter array to capture the colors of the scene. Downsampled versions of the red, green, and blue components are acquired, and an interpolation of the three colors is necessary to reconstruct a full representation of the image. This color interpolation is known as demosaicing. The most effective demosaicing techniques proposed in the literature are based on directional filtering and a posteriori decision. In this paper, we present a novel approach to this reconstruction method. A refining step is included to further improve the resulting reconstructed image. The proposed approach requires a limited computational cost and gives good performance even when compared to more demanding techniques.     

Demosaicing With Directional Filtering and a posteriori Decision

Most digital cameras use a color filter array to capture the colors of the scene. Downsampled versions of the red, green, and blue components are acquired, and an interpolation of the three colors is necessary to reconstruct a full representation of the image. This color interpolation is known as demosaicing. The most effective demosaicing techniques proposed in the literature are based on directional filtering and a posteriori decision. In this paper, we present a novel approach to this reconstruction method. A refining step is included to further improve the resulting reconstructed image. The proposed approach requires a limited computational cost and gives good performance even when compared to more demanding techniques     

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.     

基于Delaunay三角形网格的彩色视频帧间编码方法

充分利用彩色视频亮度和色度分量之间的相关性,提出了一种基于DT网格的彩色视频帧间编码方案。该方案在运动估计时仅对亮度分量Y进行DT(Delaunay triangulation)描述,对亮度分量的网格节点进行连续运动估计,利用6参数方法得到三角形内像素点的运动矢量,经过相似变换得到色度分量的运动矢量。另外对残差图像进行特殊处理和编码。实验结果表明,相对于适合低码率传输的H.263编码方法,在相同的压缩比下该方案解码图像有更好的主客观质量。     

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}.     

Color Superresolution Reconstruction and Demosaicing Using Elastic Net and Tight Frame

The goal of color superresolution reconstruction and demosaicing is to get an enhanced resolution image from raw single-chip data of Bayer color filter array. Two parts of regularization method, fidelity and regularization terms, are discussed in detail to solve the problem. Elastic net is successfully applied to variable-selection method; we utilize it as a novel fidelity term to improve performance and make the reconstructed image more suitable for human visual system. Piecewise linear framelet operators are recently adopted to image denoising, which are utilized to detect multiorientation variation of the signal in color correlation space. $K_{rm R}$ as green (G) minus red and $K_{rm B}$ as G minus blue are a color correlation space of low-pass signal, and luminance component contains most of the information of a full-color image. Thus, $K_{rm R}/K_{rm B}$ plus luminance component is considered as a color correlation space for regularization. Experimental results show that our algorithm is efficient in removing visual artifact, preserving the edges of image with high-peak signal-to-noise ratio, and satisfying visual effect.      

3D Color Set Partitioning in Hierarchical Trees

This paper introduces the 3D color set partitioning in hierarchical trees (3D-CSPIHT) low bit rate embedded video coding scheme. The codec exploits the correlation between temporal and spatial wavelet coefficients and the interdependency between luminance and chrominance components to code color video sequences without the need for explicit bit allocation. Besides offering rate scalability, the new codec also produces multi-resolution scalable code streams. The hierarchical variable size block matching motion estimation technique is also integrated to demonstrate the motion estimation option with 3D-CSPIHT. The coding results show that 3D-CSPIHT produces better performance and visual quality compared to 3D-SPIHT.