An HVS based adaptive quantization scheme for the compression of color images

In this paper a Human Visual System based adaptive quantization scheme is proposed. The proposed algorithm supports perceptually lossless as well as lossy compression. The algorithm uses a transform based compression approach using the wavelet transform, and has incorporated vision models for the compression of both luminance and chrominance components. The major strength of the coder is the incorporation of the vision model for the chrominance components and the optimum way in which the scales are distributed among the luminance and chrominance components to achieve higher compression ratios. The perceptual model developed for the color components gives flexibility for giving more compression for the color components without causing any color degradations. For each image the visual thresholds are evaluated and an optimum bit allocation is done in such a way that the quantization error is always less than the visual distortion for the given rate. To validate the strength of the proposed algorithm, the perceptual quality of the images reconstructed using the proposed coder is compared with the images reconstructed with JPEG2000 standard coder, for the same compression. To evaluate the perceptual quality of the compressed images latest perceptual quality matrices such as Structural Similarity Index, Visual Information Fidelity and Visual Signal-to-Noise Ratio are used. The results obtained reveal that the proposed structure gives excellent improvement in perceptual quality compared to the existing schemes, for both lossy as well as lossless compression. These advantages make the proposed algorithm a good candidate for replacing the quantizer stage of the current image compression standards.     

Colorization in YCbCr color space and its application to JPEG images

This paper presents a colorization method in YCbCr color space, which is based on the maximum a posteriori estimation of a color image given a monochrome image as is our previous method in RGB color space. The presented method in YCbCr space is much simpler than that in RGB space and requires much less computation time, while both methods in YCbCr and RGB space produce color images with comparable PSNR values. The proposed colorization in YCbCr is applied to JPEG compressed color images aiming at better recovery of downsampled chrominance planes. Experimental results show that colorization in YCbCr is usually effective for quality improvement of JPEG color images.     

基于小波变换的水下降质图像复原算法

根据水下成像的物理模型,提出一种基于小波变换的水下降质图像清晰化处理算法。该算法将RGB图像转换为YUV图像,根据图像的对比度,对亮度Y图像利用小波变换自适应估计介质散射光的大小,增强水下降质图像的对比度,并在小波变换的低频子带上进行非线性亮度调节,消除水下图像的光照不均问题,将亮度Y图像的处理结果与颜色分量U、V合成得到清晰的水下彩色图像。实验结果表明,该算法可以自适应实现水下观测图像的清晰化处理。     

基于四元数的改进型最小核值相似区边缘检测

针对基于亮度和色度的彩色图像边缘检测在检测过程中忽略亮度和色度之间关联性而导致部分边缘不能有效地被检测出来的问题,提出了一种基于四元数的改进型最小核值相似区(SUSAN)边缘检测算法。首先,利用四元数矢量旋转原理将HSI颜色空间的三维信息映射成二维平面信息实现空间降维,同时引入标量V来综合表示H、S、I三通道之间的关系;然后,将标量V作为算子的核函数;最后,利用改进的SUSAN算子完成图像的边缘检测。实验结果表明,提出的算法针对色度相同、饱和度存在差异以及饱和度相同、色度存在差异的彩色图像,在边缘检测的定位误差率上降低了1.5%。在实际的应用中,能够更好地获得图像中的目标信息,同时也为后续的分割和识别研究提供更好的先验知识。     

基于PCA的可变框架模型Retinex图像增强算法*

针对恶劣天气下拍摄图像的退化现象,提出了一种基于主成分分析（principal component analysis,PCA）的可变框架模型Retinex图像增强算法。PCA变换提供通道间良好的正交性,可避免由于亮度调整带来的色度失真。该算法通过PCA变换得到图像亮度分量、色度分量,对得到的亮度分量使用改进的可变框架模型Retinex进行处理,适当调整色度分量,最后对处理得到的RGB图像进行去相关拉伸。实验结果表明,该方法能有效改善恶劣天气造成的图像退化现象,提高图像的清晰度。     

一种基于空间域的彩色图像水印算法

多媒体数字水印可以为媒体提供所有权证明和知识产权保护,或用来验证媒体的内容是否真实可信。该文提出一种基于空间域的彩色图像水印算法,利用不同处理和攻击对YUV彩色空间中亮度通道Y和色度通道UV的破坏力不同的特性,根据图像亮度和纹理的不同,对亮度和色度通道分别嵌入不同强度的水印信息。在水印检测算法中,为提高水印对图像增强处理的稳健性,对待检图像和原始图像进行了归正。实验结果表明该方法能很好地抗击JEPG压缩的攻击,同时对中值滤波等低通滤波器、亮度/对比度等图像增强、锐化、平滑、栅栏及噪声干扰等攻击也具有较好的稳健性。     

基于HVS和交叉掩蔽的彩色图像编码算法

以层树分集（SPIHT）编码方案为基础,结合人类视觉系统（HVS）模型和人类视觉对彩色图像分量亮度和色度的不同敏感性,提出了一种基于非对称编码和交叉掩蔽的小波域彩色图像压缩编码算法。该算法首先将原始图像从RGB空间转换到YCbCr空间,然后对YCbCr空间的各分量进行离散小波变换;之后根据人类视觉对彩色图像的亮度分量的敏感性,用交叉掩蔽模型对亮度分量的小波系数进行加权处理;与此同时,利用非对称编码和SPIHT编码思想完成图像的压缩。仿真实验结果表明,文中算法是一种高效的图像压缩编码方法,其压缩效果明显优于SPIHT编码方案。     

Color spectral analysis for spatial structure characterization of textures in IHLS color space

In this article, a linear prediction model based approach for color texture characterization and classification in the improved hue luminance and saturation color space is presented. Pure chrominance structure information is used in addition to the normally used luminance structure information for color texture classification. Hue and saturation channels of a color image in IHLS color space are combined using a complex exponential to give a single channel which holds all the chrominance information of the image. Two dimensional complex multichannel versions of the non-symmetric half plane autoregressive model, the quarter plane autoregressive model and the Gauss Markov random field model are used to perform parametric power spectrum estimation of both luminance and the “combined chrominance” channels of the image. The accuracy and precision of these spectral estimates are proven quantitatively by performing tests on a large number of images. Spectral distance measures are calculated for the spectral information of luminance and chrominance channels individually as well as combined through a combination coefficient. Using these distance measures, color texture classification is done with k-nearest neighbor algorithm. Experimental results verify that the IHLS color space exhibits better performance than the RGB color space indicating the significance of using IHLS for such analysis. They also show that color texture characterization and percentage classification obtained by combined luminance and chrominance structure information is better than the color texture classification done using only the luminance structure information.     

基于共生矩阵的彩色图像增强算法

针对传统的直方图均衡化会使图像过分增强,产生不自然的现象,提出一种利用二维共生矩阵的彩色图像增强算法。首先,利用相邻3×3像素之间的相关性来构造共生矩阵;然后,对彩色图像中的亮度分量进行共生直方图均衡;最后,将增强后的图像进行暗原色处理得到比较理想的图像。从所提增强算法与其他3种典型的增强算法的比较可看出:所提算法不仅考虑了图像的全局与局部信息,而且降低了直方图峰值和光环效应,提高了图像对比度,有较好的增强效果。     

基于多梯度的区域自适应去马赛克算法*

针对Bayer格式的彩色滤波阵列,鉴于传统去马赛克算法存在图像纹理和边缘模糊的现象,提出了一种基于多梯度的区域自适应去马赛克算法.该算法利用区域水平和垂直方向的梯度组算子来判断区域平滑程度和插值方向,从而有效地恢复绿色分量,同时根据色度差恒定原理来还原另外两个丢失的颜色信息.实验结果显示,相对于传统插值算法提高了图像的峰值信噪比,锐化了图像的纹理和边缘,改善了图像的视觉质量.     

一种新的半脆弱彩色图像数字水印算法

提出了一种基于内容的半脆弱彩色图像数字水印嵌入算法, 该算法可同时用于彩色图像的版权保护和内容认证．首先将彩色载体图像从 RGB 空间转换到 YCrCb 空间．然后对亮度 (Y) 进行 L+1 级小波分解, 并将分解后的小波近似系数用混沌序列进行调制从而生成基于图像内容的数字水印信号．最后对亮度 (Y) 及色差 (Cr 和 Cb) 进行 L 级小波分解, 并结合视觉感知特性及局部系数相关特性, 通过分块量化措施将水印信号嵌入到载体图像 (Y、Cr和Cb) 的小波域中．仿真实验表明, 本文算法不仅具有较好的透明性, 对JPEG压缩、叠加噪声、平滑滤波等常规图像处理操作具有较好的鲁棒性, 而且能够对剪切、替换等恶意图像篡改作出报警并确定被篡改位置．同时常规图像处理下其误检率与漏检率均比较低．     

An improved contrast enhancing approach for color-to-grayscale mappings

Despite the widespread availability of color sensors for image capture, the printing of documents and books are still primarily done in black-and-white for economic reasons. In this case, the included illustrations and photographs are printed in grayscale, with the potential loss of important information encoded in the chrominance channels of these images. We present an efficient contrast enhancement algorithm for color-to-grayscale image conversion that uses both luminance and chrominance information. Our algorithm is about three orders of magnitude faster than previous optimization-based methods, while providing some guarantees on important image properties. More specifically, our approach preserves gray values present in the color image, ensures global consistency, and locally enforces luminance consistency. Our algorithm is completely automatic, scales well with the number of pixels in the image, and can be efficiently implemented on modern GPUs. We also introduce an error metric for evaluating the quality of color-to-grayscale transformations.     

基于颜色和纹理特征提取的接地网腐蚀等级分析

针对目前我国发变电站接地网材料普遍采用的碳钢材料易腐蚀,且人眼判别腐蚀情况误差较大的问题,提出了采用图像处理技术对采集的腐蚀图像进行颜色和纹理特征提取以及分析的方法。首先,在亮度/色度彩色空间中,采用亮度色度非线性重组对腐蚀图像进行增强处理;其次,通过局部特征一致性,采用Gabor滤波器和彩色空间提取图像的颜色、纹理和梯度幅值等特征;然后,采用人工蜂群算法得到分割图像的最佳种子点和最佳相似值,采用种子区域增长 将图像分割成小的腐蚀区域;最后,度量这些小的腐蚀区域的相似性,从而完成腐蚀等级的分析。     

基于JPEG标准静止图像压缩算法的分析与研究

介绍了JPEG标准、JPEG2000标准及其相比JPEG标准的改进和优点。通过对JPEG标准压缩流程及算法的分析与研究,得到JPEG标准压缩的基本原理：利用人类的视觉特点对亮度分量的精度敏感,而对色度分量的精度迟钝,将RGB颜色模式转换为YCrCb颜色模式;基于视觉特点来抑制高频部分使用离散余弦变换和量化来实现;应用完全可逆的熵编码即霍夫曼编码使比特序列更小。     

一种基于细节层分离的单曝光HDR图像生成算法

针对利用单幅低动态范围（Low dynamic range,LDR）图像生成高动态范围（High dynamic range,HDR）图像细节信息不足的问题,本文提出了一种基于细节层分离的单曝光HDR图像生成算法.该算法基于人类视觉系统模型,首先分别提取出LDR图像的亮度分量和色度分量,对伽马校正后的亮度分量进行双边滤波,提取出亮度分量的基本层,再对基本层和亮度分量进行遍历运算,得到亮度分量的细节层;然后,构造反色调映射函数,分别对细节层和伽马校正后的亮度图像进行扩展,得到各自的反色调映图像;之后,将反色调映射后亮度分量与压缩后的细节层进行融合,得到新的亮度分量.最后,融合色度分量与新的亮度分量,并对融合后图像进行去噪,得到最终的HDR图像.实验表明该算法能挖掘出部分隐藏的图像细节信息,处理效果较好,运行效率高,具有较好的鲁棒性.     

Choice of a pertinent color space for color texture characterization using parametric spectral analysis

This article presents a comparison of different color spaces including RGB, IHLS and L?a*b* for color texture characterization. This comparison is based on the fusion of the independent spatial structure and color feature cues. In IHLS and L*a*b*, two channel complex color images are created from the luminance and the chrominance values. For such images, two dimensional complex multichannel linear prediction models are used to perform parametric power spectrum estimation and the structure feature cues are computed from this estimated power spectrum. Quantitative comparison of auto spectra of luminance and combined chrominance channels for different color spaces is done. This comparison is based on the degree of decorrelation between luminance and chrominance information provided by different color space transformations. Three dimensional histograms are used as color feature cues. Then, to classify color textures, Kullback-Leibler divergence based symmetric distance measures are calculated for pure color, luminance structure and chrominance structure feature cues. Individual as well as combined effect of information from all feature cues on classification results is then compared for different color spaces and different color texture data sets. The proposed color texture classification method performs better than the state of the art methods in certain cases. The L*a*b* color space gives us a better characterization of the chrominance spatial structure as well as the overall spatial structure for all of the chosen data sets. Experimental results on pixel classification of color textures are also presented and discussed.     

结合背景光融合及水下暗通道先验和色彩平衡的#br# 水下图像增强

水下环境、光线衰减和拍摄方式造成水下图像具有不同色调、对比度和模糊度.基于图像成像模型的水下图像复原方法通常基于暗通道先验或最大像素先验,容易受到水下复杂环境的干扰而输出低质量的复原图像,因此文中提出基于背景光融合及水下暗通道先验和色彩平衡的水下图像增强方法.首先,提出多候选背景光融合方法,估计正确的背景光.然后,基于高质量水下图像统计得出水下暗通道先验,计算更准确的RGB分量传输地图.将复原图像从RGB颜色模型转换到CIE-Lab颜色模型,对L亮度分量和a、b色彩分量分别进行归一化拉伸和优化调整,进一步提高复原后水下图像的亮度和对比度.多种定性和定量分析说明文中方法增强的图像在对比度、亮度和颜色上的显示效果优于大部分现有的水下图像增强方法复原的图像.     

No-reference stereoscopic image quality assessment using quaternion wavelet transform and heterogeneous ensemble learning

As the demand for high-quality stereo images has grown in recent years, stereoscopic image quality assessment (SIQA) has become an important research area in modern image processing technology.In this paper, we propose a no-reference stereoscopic image quality assessment (NR-SIQA) model using heterogeneous ensemble learning ‘quality-aware’ features from luminance image, chrominance image, disparity and cyclopean images via quaternion wavelet transform (QWT). Firstly, luminance image and chrominance image are generated by CIELAB color space as monocular perception, and the novel disparity and cyclopean images are utilized to complement with monocular information. Then, a number of ‘quality-aware’ features in the quaternion wavelet domain are discovered, including entropy, texture features, energy features, energy differences features and MSCN coefficients of high frequency sub-band. Finally, a heterogeneous ensemble model via support vector regression (SVR) & extreme learning machine (ELM) & random forest (RF) is proposed to predict quality score, and bootstrap sampling and rotated feature space are used to increase the diversity of data distribution. Comparing with the state-of-the-art NR-SIQA models, experimental results on four public databases prove the accuracy and robustness of the proposed model.     

Web-based image indexing and retrieval in JPEG compressed domain

As the majority of content-based image retrieval systems operate on full images in pixel domain, decompression is a prerequisite for the retrieval of compressed images. To provide a possible on-line indexing and retrieval technique for those jpg image files, we propose a novel pseudo-pixel extraction algorithm to bridge the gap between the existing image indexing technology, developed in the pixel domain, and the fact that an increasing number of images stored on the Web are already compressed by JPEG at the source. Further, we describe our Web-based image retrieval system, WEBimager, by using the proposed algorithm to provide a prototype visual information system toward automatic management, indexing, and retrieval of compressed images available on the Internet. This provides users with efficient tools to search the Web for compressed images and establish a database or a collection of special images to their interests. Experiments using texture- and colour-based indexing techniques support the idea that the proposed algorithm achieves significantly better results in terms of computing cost than their full decompression or partial decompression counterparts. This technology will help control the explosion of media-rich content by offering users a powerful automated image indexing and retrieval tool for compressed images on the Web.J. Jiang: Contacting author     

基于区域特征加权的JPEG改进算法

JPEG标准在对彩色图像编码时,为了提高图像的压缩率,需要将图像从RGB颜色空间转换到YCbCr颜色空间并对颜色分量Cb与Cr进行重采样.颜色空间转换与重采样会引起还原图像光谱畸变现象.在研究图像融合技术的基础上,提出一种基于区域特征的动态加权色彩空间变化方法.根据主观目视和客观评价指标,对还原图像进行了比较和分析.仿真试验表明,提出的方法可以提高还原图像的光谱分辨率,同时很好保持了原图像的空间分辨率.