一种八叉树色彩量化算法的改进

在设备限定条件和期望提高效率、内存使用率以及减少图像大小的条件下使用八叉树算法进行任意目标颜色数的量化操作具有一定难度.本文即对该问题进行了深入分析和研究,提出了将八又树算法与聚类分析相结合,先使用八义树算法量化到设备限定颜色数,再使用聚类分析量化到目标数,然后调整调色板的改进方法.该改进算法在实验中证明了其有效性.     

量化颜色直方图的改进与应用

对图像进行灰度量化时,容易把颜色不同而灰度相同的像素归为一类,造成量化误差而影响评价结果。基于HSV颜色空间,通过统计样本数据,结合人眼观察彩色的视觉特性,确定了颜色量化范围,建立了适合伪装特点的颜色直方图,利用距离函数进行了目标颜色特征的伪装效果评价。实验结果表明,利用改进的量化颜色直方图评价目标伪装效果,能够客观地...     

基于八叉树颜色量化的OpenGL实现

色彩量化是数字图像处理中的基本技术之一.在三维场景中,颜色是三维外观的重要方面,如何对颜色进行量化,使其产生逼真的效果,是一个非常复杂的问题.在计算机图形中,现有的颜色量化方法有四种.文中利用比较先进的八叉树方法与OpenGl结合,给出一种颜色量化的算法.通过这种算法可以在渲染场景中产生较好的真彩色图像文件.     

基于MVF和改进八叉树的屏幕图像采集方案

作为远程视频监控系统的一个关键部分之一,监控画面的实时采集存在2个问题,一个是以传统屏幕DC法进行截图速率比较慢,另一个是图片数据传输量比较大,加重网络负载.为解决以上问题,以MVF(内存映射文件)法优化屏幕DC截图法的工作效率;以改进的八叉树算法对所采集的图片进行量化,在保证图像成像效果的基础上降低图像数据量.实验结果证明,基于MVF优化的屏幕DC截图法的工作速率得到改善,经改进的八叉树量算法量化处理和误差扩散处理后的图像具有明显层次感、颗粒感,带状现象得到改善.     

基于多尺度区域混色的RGB欠采样图像颜色错误评价

针对彩色阵列显示设备中由于采用亚像素寻址技术引起的颜色错误问题,该文提出了一种定位和量化的方法。基于人眼空间混色特性,指出颜色误差的度量应在多尺度混色区域下进行。以亚像素矩型排布为例,从点阵理论的结构基元出发,设计多尺度混色区域的形状与大小,实现了颜色错误的定位与量化。分析和实验表明,以多尺度区域混色颜色误差为基础的自适应颜色误差消除法在弱化颜色错误的同时保持了更多原有图像的细节。     

图像无损压缩的预测编码及量化误差处理

本文提出了一种基于预测编码并对量化误差进行四叉树编码从而实现对灰度图像无损压缩的方法。采用固定预测系数的算法，使得预测过程速度很快；通过四叉树编码方法处理量化误差，避免了对小数编码和处理这一复杂的过程。整个实现过程十分简单，实验结果显示，图像通过预测器后的平均码长明显小于原始图像的熵。     

一种结合人眼对比度感知特性的彩色图像压缩算法

为了使图像压缩后的效果更加符合人眼感知特性,提出了一种结合人眼对比度敏感视觉特性的图像压缩算法。算法首先结合视觉特性和图像变换域频谱系数特征,提出一种图像的角频率的计算方法,并依据计算的角频率提出一种人眼觉察图像最小误差阈值的计算方法;然后以此阈值作为量化步长,提出一种图像变换域频谱系数的量化方法;最后采用霍夫曼编码算法进行编解码,实现图像的压缩。并对三幅彩色图像进行了仿真实验,结果表明:与JPEG技术相比,三幅彩色图和各分量图的平均压缩比、PSNR和SSIM依次提高了10.4807%、6.9879%和2.6494%。表明提出的结合人眼视觉特性的图像压缩算法是一种较好的、有实用价值的压缩算法。     

基于人眼视觉系统的误差分布反馈量化策略

为了提高视频运动编码的质量,满足人眼视觉特性,降低量化编码失真,提出一种基于人眼视觉系统的误差分布反馈量化策略.该算法利用视频图像的运动目标与时间活动性关系,实现了运动场矢量分层描述,并通过各种活动性因子调节实际量化参数的大小.最后利用误差分布自适应的调整量化步长.实验结果表明,本文算法在不引入更多计算复杂度的同时,相同码率下的单位像素蕈建量化误差与图像质量(PSNR)优于TM5算法.     

基于色彩量化及索引的图像检索

提出了一种基于色彩量化及索引的图像检索新方法.结合人眼视觉感知特性,首先将图像划分成4×4大小的非重叠子块,通过块梯度的大小,在亮度空间将图像子块划分为视觉均衡块和非均衡块.若为均衡块,则用该块RGB空间的颜色均值作为其代表颜色值,然后转换到HSV空间,并量化成32个等级,形成32维索引直方图(S_HIST);若非均衡块,根据保持颜色矩不变技术将该图像子块在RGB空间量化两种颜色,然后转化到HSV空间,并将每种颜色量化成32个等级,形成496维的索引直方图(D_HIST).最后,综合索引特征,进行图像检索.实验结果表明:该算法是非常有效的.     

彩色矩阵显示器亚像素采样混色错误的分析与评价

从彩色矩阵显示的亚像素排布(SPA)出发,详细分析了亚像素采样(SPS)颜色错误出现的原因及条件,指出了颜色错误与SPA的关系,得出对任意一种SPA颜色错误只发生在与排布错误方向平行且图像输入频率大于Nyquist频率的区域。根据人眼视觉特性,提出了区域混合的颜色误差评测方法。此方法以原图像颜色为基准,最小化结构基元覆盖区域下的颜色误差,对图像累计该误差并以累计结果的各种统计指标作为显示图像与原始图像颜色偏差的一种度量。计算结果与仿真实验的分析结果相符,能够正确反应出SPS对混色问题的影响。     

Design and analysis of vector color error diffusion halftoningsystems

Traditional error diffusion halftoning is a high quality method for producing binary images from digital grayscale images. Error diffusion shapes the quantization noise power into the high frequency regions where the human eye is the least sensitive. Error diffusion may be extended to color images by using error filters with matrix-valued coefficients to take into account the correlation among color planes. For vector color error diffusion, we propose three contributions. First, we analyze vector color error diffusion based on a new matrix gain model for the quantizer, which linearizes vector error diffusion. The model predicts the key characteristics of color error diffusion, esp. image sharpening and noise shaping. The proposed model includes linear gain models for the quantizer by Ardalan and Paulos (1987) and by Kite et al. (1997) as special cases. Second, based on our model, we optimize the noise shaping behavior of color error diffusion by designing error filters that are optimum with respect to any given linear spatially-invariant model of the human visual system. Our approach allows the error filter to have matrix-valued coefficients and diffuse quantization error across color channels in an opponent color representation. Thus, the noise is shaped into frequency regions of reduced human color sensitivity. To obtain the optimal filter, we derive a matrix version of the Yule-Walker equations which we solve by using a gradient descent algorithm. Finally, we show that the vector error filter has a parallel implementation as a polyphase filterbank.     

Fuzzy algorithms for combined quantization and dithering

Color quantization reduces the number of the colors in a color image, while the subsequent dithering operation attempts to create the illusion of more colors with this reduced palette. In quantization, the palette is designed to minimize the mean squared error (MSE). However, the dithering that follows enhances the color appearance at the expense of increasing the MSE. We introduce three joint quantization and dithering algorithms to overcome this contradiction. The basic idea is the same in two of the approaches: introducing the dithering error to the quantizer in the training phase. The fuzzy C-means (FCM) and the fuzzy learning vector quantization (FLVQ) algorithms are used to develop two combined mechanisms. In the third algorithm, we minimize an objective function including an inter-cluster separation (ICS) term to obtain a color palette which is more suitable for dithering. The goal is to enlarge the convex hull of the quantization colors to obtain the illusion of more colors after error diffusion. The color contrasts of images are also enhanced with the proposed algorithm. We test the results of these three new algorithms using quality metrics which model the perception of the human visual system and illustrate that substantial improvements are achieved after dithering     

彩色图像误差扩散多值量化滤波器的优化设计

在多值量化情况下 ,因量化误差为白噪声的理想条件无法满足 ,故实施误差扩散处理时仅单纯地进行高通滤波 (误差蓝化 )将损害图像固有的低频特性。误差扩散算法中较为理想的误差滤波器应在高频域具有较大增益 ,而在低频域具有有限度衰减。在兼顾滤波器稳定性因素的同时对误差滤波器的脉冲响应进行了重新设计 ,并以此构成了用于彩色图像颜色量化的多色调误差扩散优化处理算法。     

Color error-diffusion halftoning

Grayscale halftoning converts a continuous-tone image (e.g., 8 bits per pixel) to a lower resolution (e.g., 1 bit per pixel) for printing or display. Grayscale halftoning by error diffusion uses feedback to shape the quantization noise into high frequencies where the human visual system (HVS) is least sensitive. In color halftoning, the application of grayscale error-diffusion methods to the individual colorant planes fails to exploit the HVS response to color noise. Ideally the quantization error must be diffused to frequencies and colors, to which the HVS is least sensitive. Further it is desirable for the color quantization to take place in a perceptual space so that the colorant vector selected as the output color is perceptually closest to the color vector being quantized. This article discusses the design principles of color error diffusion that differentiate it from grayscale error diffusion, focusing on color error diffusion halftoning systems using the red, green, and blue (RGB) space for convenience.     

基于自适应量化矩阵的硬判决量化算法

在视频编码中,视频量化一般分为硬判决量化(HDQ)和软判决量化(SDQ),HDQ与SDQ相比,编码性能虽有所损失,但其编码复杂度低,易于硬件实现的优点依旧是主流编码器所主要采用的量化算法.人眼具有对图像中的高频细节不敏感的特性.因此,基于Bayes最小误判概率约束,离线构建基于视频内容自适应的量化矩阵,在模拟感知SDQ算法机理下,对高频低频分量采用不同的量化步长,提高视频的主观质量和HDQ算法性能.仿真实验表明,相比于传统的HDQ算法,该文算法能达到平均5.048％的码率节省,其中WVGA和WQVGA格式平均达到10.65％的码率节省.相比于感知SDQ算法,平均码率增加仅有1.464％;算法复杂度方面,编码一帧的时间相比于感知SDQ节省了32.956％.     

Fuzzy error diffusion

Quantization errors are generally hidden by performing a dithering operation on the image. A common method is to utilize error diffusion. However, this method is prone to error accumulation, resulting in color impulses and streaks. This paper presents a new approach to error diffusion dithering through a fuzzy error diffusion algorithm. In this method, the amount of error to be diffused is determined by considering the relative location of the pixel not only to the closest codebook vector, but to all other palette entries. The goal is to hide the quantization errors by error diffusion, while preventing the excess accumulation of errors. This is achieved through an attraction-repulsion schema according to a fuzzy membership function. We also explored methods to speed up the fuzzy error diffusion process through a L-filter approach by determining a fixed set of membership values. We have implemented the fuzzy error diffusion algorithm for color images and achieved drastic improvements, resulting in superior quality dithered images and significantly lower mean squared error values. A different error measure modeling the characteristic of the human visual system also indicates the superiority of our method.     

融合矢量量化与LBP的图像检索算法

基于内容的图像检索算法一直是图像领域研究的热门课题,因此提出一种新的融合矢量量化与LBP的图像检索算法。首先,将彩色图像转化到HSI颜色空间,进行矢量量化编码,统计图像码字出现的频数,形成颜色直方图,完成颜色特征的提取;然后,再将彩色图像转化成灰度图像,利用局部二进制模式(LBP)算法提取纹理特征;最后,相似度计算采用颜色特征和纹理特征相似度加权平均,并且改变颜色特征和纹理特征的权值,多次实验,得到使查准率最高的权值。实验结果表明,算法能有效地提升图像检索性能。     

RGB calibration for color image analysis in machine vision

A color calibration method for correcting the variations in RGB color values caused by vision system components was developed and tested in this study. The calibration scheme concentrated on comprehensively estimating and removing the RGB errors without specifying error sources and their effects. The algorithm for color calibration was based upon the use of a standardized color chart and developed as a preprocessing tool for color image analysis. According to the theory of image formation, RGB errors in color images were categorized into multiplicative and additive errors. Multiplicative and additive errors contained various error sources-gray-level shift, a variation in amplification and quantization in camera electronics or frame grabber, the change of color temperature of illumination with time, and related factors. The RGB errors of arbitrary colors in an image were estimated from the RGB errors of standard colors contained in the image. The color calibration method also contained an algorithm for correcting the nonuniformity of illumination in the scene. The algorithm was tested under two different conditions-uniform and nonuniform illumination in the scene. The RGB errors of arbitrary colors in test images were almost completely removed after color calibration. The maximum residual error was seven gray levels under uniform illumination and 12 gray levels under nonuniform illumination. Most residual RGB errors were caused by residual nonuniformity of illumination in images, The test results showed that the developed method was effective in correcting the variations in RGB color values caused by vision system components.