RLE改进算法对BMP图像无损压缩的实现

BMP是一种广泛使用的位图图像.RLE是利用统计学实现的一种无损压缩算法,广泛应用于位图图像的压缩中.本文对RLE的原理进行了介绍并试图对其进行一些改进,以期提高对灰度BMP图像的压缩率.     

基于VC6.0的灰度位图处理的实现

介绍了一种在微软Visual C 6.0编译环境下处理灰度BMP位图的实现方法，给出了部分关键函数的源代码以及几幅经过处理以后得到的图像。     

用VC++处理灰度位图

介绍微软Visual C 6.0环境下处理灰度BMP位图的实现方法。     

基于BP网络的车牌数字识别

本文论述BP算法的实现以及在利用BP算法时应注意的几个问题,然后利用BP网络对经过图像预处理,并归一化到12×8象素大小的车牌图像中的阿拉伯数字位图进行识别。实验结果表明,采用本文提出的BP网络识别方案,算法简便,识别率高,同样可适用于强噪声环境中的印刷体字符识别。     

基于Gauss-Laplace算子的灰度图像边缘检测

在分析常用的图象边缘检测算子基础上,为更精确地检测具有灰度渐变和噪声较多的灰度图像,论文首先引入一种改进的灰度图像的边缘检测算子,接着实验验证该算子得到的图像比别的边缘检测算子处理效果更好,更清楚和更符合人或计算机识别要求。同时,给出了在VC++6.0编程环境下BMP位图处理的方法和介绍的边缘检测算子的实现方法。     

基于PSO—BP神经网络的图像压缩算法

本文阐述了使用BP神经网络压缩图像的方法和粒子群算法（PSO）的原理。为提高BP算法的训练速度和图像重建质量．本文设计了一种利用PSO—BP网络进行图像压缩的算法，该算法结合了PSO算法和BP算法的优点，将BP网络的训练过程分为两个阶段。实验表明，利用该算法压缩图像，不仅速度较快，而且重建后的图像质量有明显提高。     

利用Delphi 6实现拉普拉斯高斯边缘检测算法

本文描述了拉普拉斯高斯边缘检测算法结合算法在Delphi 6编程环境下对BMP格式的灰度图像进行了边缘检测处理,从而体现其优越性。     

基于纹理和BP神经网络的SAR图像分类

研究基于纹理和BP神经网络的SAR图像分类。首先用增强FROST滤波算法对SAR图像进行去噪处理。然后基于灰度共生矩阵理论提取去噪后的SAR图像多种纹理特征,并通过大量实验筛选出有效的纹理特征。最后,结合纹理特征,分别采用经典的最大似然分类法和BP神经网络分类法对SAR图像进行分类。实验结果表明：纹理信息辅助SAR图像的灰度进行分类,大大地提高了SAR图像的分类精度;基于BP神经网络的SAR图像分类精度高于最大似然分类法的分类精度。     

基于PSO-BP神经网络的图像压缩算法

本文阐述了使用BP神经网络压缩图像的方法和粒子群算法(PSO)的原理.为提高BP算法的训练速度和图像重建质量,本文设计了一种利用PSO-BP网络进行图像压缩的算法,该算法结合了PSO算法和BP算法的优点,将BP网络的训练过程分为两个阶段.实验表明,利用该算法压缩图像,不仅速度较快,而且重建后的图像质量有明显提高.     

原始图像数据向BMP位图文件的转换

文章通过对Windows中标准图像格式BMP位图文件格式的分析及其特殊规则的处理,提出J由原始16级灰度数据向BMP位图文件的转换方法,给出了具体的转换步骤.并将该方法应用到实际的图像数据中去,实验证明,该转换方法简单、有效,可以实现原始图像数据向BMP位图的转换.     

在VC环境下对位图图像的处理过程

数字图像处理是利用计算机技术对数字图像进行处理的技术,它有着广泛的应用前景。本文研究了位图图像的原理及存储方式,在VC环境下研究并实现了256色位图转灰度图,灰度图二值化和位图的旋转操作。     

Two-Bit Graphics

Ordinary bitmaps allow pixels to be black or white. We introduce a second bitmap, the "alpha" bitmap, which allows pixels to be transparent as well. The alpha bitmap makes it possible to have black-and-white images that are nonrectangular or that have holes in them. It also provides a richer set of operations for working with bitmaps. We present the mathematics for a two-bit compositing algebra, and suggest extensions for two-bit compositing, painting, and region filling. Each of these operations can be implemented with ordinary bitblts and presented on ordinary bitmap displays. We analyze the cost of each two-bit operation in terms of the number of bitblts it requires.     

An efficient authentication method for AMBTC compressed images using adaptive pixel pair matching

The existing image authentication methods for absolute moment block truncation coding (AMBTC) modify the bits of quantitation levels or bitmaps to embed the authentication code (AC). However, the modification of the bits in these methods is equivalent to the LSB replacement, which may introduce undesirable distortions. Besides, the modification of bitmap for embedding AC reduces the image quality significantly, especially at image edges. Moreover, the existing methods might not be able to detect some special modifications to the marked image. In this paper, we propose an efficient authentication method for the AMBTC compressed image. AC is obtained from the bitmap and the location information, and is embedded into the quantization levels using the adaptive pixel pair matching (APPM) technique. Since the bitmap is unchanged and the APPM embedment is efficient, a high image quality can be achieved. The experimental results reveal that the proposed method not only significantly reduces the distortion caused by embedding but also provides a better authentication result when compared to the prior state-of-art works.

     

RLH: Bitmap compression technique based on run-length and Huffman encoding

In this paper we propose a technique of compressing bitmap indexes for application in data warehouses. This technique, called run-length Huffman (RLH), is based on run-length encoding and on Huffman encoding. Additionally, we present a variant of RLH, called RLH-N. In RLH-N a bitmap is divided into N-bit words that are compressed by RLH. RLH and RLH-N were implemented and experimentally compared to the well-known word aligned hybrid (WAH) bitmap compression technique that has been reported to provide the shortest query execution time. The experiments discussed in this paper show that: (1) RLH-compressed bitmaps are smaller than corresponding WAH-compressed bitmaps, regardless of the cardinality of an indexed attribute, (2) RLH-N-compressed bitmaps are smaller than corresponding WAH-compressed bitmaps for certain range of cardinalities of an indexed attribute, (3) RLH and RLH-N-compressed bitmaps offer shorter query response times than WAH-compressed bitmaps, for certain range of cardinalities of an indexed attribute, and (4) RLH-N assures shorter update time of compressed bitmaps than RLH.     

Compressed bitmap indexes: beyond unions and intersections

Compressed bitmap indexes are used to speed up simple aggregate queries in databases. Indeed, set operations like intersections, unions and complements can be represented as logical operations (AND, OR and NOT) that are ideally suited for bitmaps. However, it is less obvious how to apply bitmaps to more advanced queries. For example, we might seek products in a store that meet some, but maybe not all, criteria. Such threshold queries generalize intersections and unions; they are often used in information‐retrieval and data‐mining applications. We introduce new algorithms that are sometimes three orders of magnitude faster than a naïve approach. Our work shows that bitmap indexes are more broadly applicable than is commonly believed. Copyright © 2014 John Wiley & Sons, Ltd.     

基于Zh变换的视频对象形状编码方法

根据视频对象形状的特点，提出了一种新的视频对象形状编码方法．这种方法仍然属于基于位图的方法，但是具有很好的可扩展性和压缩效率．这种方法的核心是Zh变换．利用Zh变换可以把表示视频对象形状的二值位图分解为一系列较小的二值位图．这些小位图从粗到精地描述了视频对象的形状，这使得本方法具有很好的可扩展性．压缩效率的提高来自以下两个方面：(1)通过设置变换终止条件，可以自动标记位图中远离对象边缘的块；(2)采用基于上下文的算术编码方法编码变换后的小位图，由于变换后的数据之间存在较强的相关性，算术编码器可以根据已经编码的数据更准确地预测下一个被编码位．     

Better bitmap performance with Roaring bitmaps

Bitmap indexes are commonly used in databases and search engines. By exploiting bit‐level parallelism, they can significantly accelerate queries. However, they can use much memory, and thus, we might prefer compressed bitmap indexes. Following Oracle's lead, bitmaps are often compressed using run‐length encoding (RLE). Building on prior work, we introduce the Roaring compressed bitmap format: it uses packed arrays for compression instead of RLE. We compare it to two high‐performance RLE‐based bitmap encoding techniques: Word Aligned Hybrid compression scheme and Compressed ‘n’ Composable Integer Set. On synthetic and real data, we find that Roaring bitmaps (1) often compress significantly better (e.g., 2×) and (2) are faster than the compressed alternatives (up to 900× faster for intersections). Our results challenge the view that RLE‐based bitmap compression is best. Copyright © 2015 John Wiley & Sons, Ltd.