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IJG（Independent JPEG Group，联合图像专家组）IJG JPEG库：系统的体系结构。

本文件是JPEG软件的一部分。

使用和销售的条件，请参照随带的readme文件。

本文件给出了JPEG软件的大体结构，包括系统中各个模块中的函数及模块间的接口。

更多关于数据结构和行业协议的详解请参照包含文件和源程序中的注释。

.我们假设读者已经对JPEG标准有了一定的了解。

Readme文件中列出了关于的JPEG 的参考书目。

文件libjpeg.doc从程序师实际应用的角度描述了库，因此最好先读那个文件再来看本文件。

同时，coderules.doc文件介绍了代码中的类型规定。

本文件中，JPEG的专业术语遵循JPEG标准。

“component”：颜色信道。

例如：红色或亮度。

“sample”：采样单元。

（某一图像数据的数）。

“coefficient”：频率系数。

（DCT变换的输出数）。

“block”：采样单元或频率系数中的一个8*8的组。

“MUC”（最小编码单元）：隔行扫描机制中块的大小，由采样因数决定；或某一非隔行扫描中的单独块。

我们不交叉使用术语“pixel”和“sample”。

当说到“pixel”时他代表全幅图像的一个成分，而“sample”指采样图像的一成分。

因此“sample”的数量可能经颜色信道发生改变而“pixel”不会改变。

（这一术语区分没有严格的贯穿代码始终，但使用在那些一旦混淆就将导致错误的地方。

）***系统特征***IJG的发行包括两部分：* JPEG压缩与解压的子程序库。

* cjpeg/djpeg，两个应用库，转化JFIF、JPEG到其他图像格式的软件实例。

cjpeg/djpeg不是很复杂，他们仅仅加入了对几种未压缩的图像格式使用接口和I/O程序的简单命令行。

这个文档浓缩在库本身中。

我们希望这个库能够支持所有的JPEG基线，甚至顺序和向前DCT处理。

但不支持分级处理。

The library does not support the lossless (spatial) JPEG process. Lossless JPEG shares little or no code with lossy JPEG, and would normally be used without the extensive pre- and post-processing provided by this library.We feel that lossless JPEG is better handled by a separate library.Within these limits, any set of compression parameters allowed by the JPEG spec should be readable for decompression. (We can be more restrictive about what formats we can generate.) Although the system design allows for all parameter values, some uncommon settings are not yet implemented and maynever be; nonintegral sampling ratios are the prime example. Furthermore,we treat 8-bit vs. 12-bit data precision as a compile-time switch, not arun-time option, because most machines can store 8-bit pixels much more compactly than 12-bit.For legal reasons, JPEG arithmetic coding is not currently supported, but extending the library to include it would be straightforward.By itself, the library handles only interchange JPEG datastreams --- in particular the widely used JFIF file format. The library can be used by surrounding code to process interchange or abbreviated JPEG datastreams that are embedded in more complex file formats. (For example, libtiff uses this library to implement JPEG compression within the TIFF file format.)The library includes a substantial amount of code that is not covered by the JPEG standard but is necessary for typical applications of JPEG. These functions preprocess the image before JPEG compression or postprocess it after decompression. They include colorspace conversion, downsampling/upsampling, and color quantization. This code can be omitted if not needed.A wide range of quality vs. speed tradeoffs are possible in JPEG processing, and even more so in decompression postprocessing. The decompression library provides multiple implementations that cover most of the useful tradeoffs, ranging from very-high-quality down to fast-preview operation. On thecompression side we have generally not provided low-quality choices, since compression is normally less time-critical. It should be understood that the low-quality modes may not meet the JPEG standard's accuracy requirements; nonetheless, they are useful for viewers.*** Portability issues ***Portability is an essential requirement for the library. The key portability issues that show up at the level of system architecture are:1. Memory usage. We want the code to be able to run on PC-class machineswith limited memory. Images should therefore be processed sequentially (in strips), to avoid holding the whole image in memory at once. Where afull-image buffer is necessary, we should be able to use either virtual memory or temporary files.2. Near/far pointer distinction. To run efficiently on 80x86 machines, the code should distinguish "small" objects (kept in near data space) from "large" ones (kept in far data space). This is an annoying restriction, but fortunately it does not impact code quality for less brain-damaged machines,and the source code clutter turns out to be minimal with sufficient use of pointer typedefs.3. Data precision. We assume that "char" is at least 8 bits, "short" and "int" at least 16, "long" at least 32. The code will work fine with largerdata sizes, although memory may be used inefficiently in some cases. However, the JPEG compressed datastream must ultimately appear on external storage as a sequence of 8-bit bytes if it is to conform to the standard. This may pose a problem on machines where char is wider than 8 bits. The library representscompressed data as an array of values of typedef JOCTET. If no data type exactly 8 bits wide is available, custom data source and data destination modules must be written to unpack and pack the chosen JOCTET datatype into8-bit external representation.*** System overview ***The compressor and decompressor are each divided into two main sections:the JPEG compressor or decompressor proper, and the preprocessing or postprocessing functions. The interface between these two sections is the image data that the official JPEG spec regards as its input or output: this data is in the colorspace to be used for compression, and it is downsampledto the sampling factors to be used. The preprocessing and postprocessingsteps are responsible for converting a normal image representation to or from this form. (Those few applications that want to deal with YCbCr downsampled data can skip the preprocessing or postprocessing step.)Looking more closely, the compressor library contains the following main elements:Preprocessing:* Color space conversion (e.g., RGB to YCbCr).颜色空间变换* Edge expansion and downsampling. Optionally, this step can do simplesmoothing --- this is often helpful for low-quality source data.边缘扩展和降低采样，这个可选项使采样平滑。

Floyd-Steinberg误差扩散算法是一种用于图像处理的算法，其主要应用于半色调技术，以改进图像的细节和外观质量。

在半色调技术中，图像被分割成一系列的像素点，每个像素点包含了红色、绿色和蓝色三个通道的值。

对于每个像素点，算法会根据其当前的RGB值和最近的可用颜色进行比较，找到最匹配的颜色，并将该像素点的RGB值更新为最匹配颜色的RGB值。

在这个过程中，误差值是当前像素点的RGB值与最匹配颜色的RGB值之间的差异。

Floyd-Steinberg误差扩散算法将这个误差值传播到相邻的像素点。

具体来说，误差值将根据一定的权重分配给下一个像素点，以便在后续的匹配中考虑到这个误差。

这种传播过程将持续进行，直到所有像素点都被处理完毕。

在光栅方式下，像素点的处理顺序是从左到右、从上到下的顺序。

而在蛇形方式下，像素点的处理顺序则是从左到右、从上到下交替进行。

Floyd-Steinberg误差扩散算法的优势在于，它可以在有限的色彩下，让图像呈现出比实际图象更多色彩的效果。

这主要是通过在相邻像素间随机的加入不同的颜色来实现的。

这种技术在打印机、扫描仪和显示器等设备中得到了广泛的应用。

基于暗通道先验和自适应颜色校正的水下图像处理霍光尧; 佃松宜; 武伟让; 余桐; 李淼【期刊名称】《《电光与控制》》【年(卷),期】2019(026)011【总页数】5页(P70-74)【关键词】水下图像处理; 暗通道; 自适应参数; 渐晕模型; 亮度补偿; 去雾; 密闭空间【作者】霍光尧; 佃松宜; 武伟让; 余桐; 李淼【作者单位】四川大学电气信息学院成都610065; 苏州热工研究院有限公司江苏苏州215004【正文语种】中文【中图分类】O213.20 引言水下图像受传播光的衰减影响导致可见度低，光传播随距离和深度呈指数衰减，主要是由于吸收和散射效应。

吸收实质上减少了光能,而散射导致光方向的变化，这种现象在密闭空间内尤为明显[1]。

光的随机衰减是产生雾状外观的主要原因，而从介质沿视线散射回来的光使场景对比度显著降低。

由于悬浮颗粒散射中的后向散射导致图像模糊，物体表面的原有颜色被遮盖，物体局部区域辨认不清。

水下图像增强方法一般分为两种：增强方法和复原方法[2]。

增强方法主要有以Retinex方法为代表的去雾算法[3-8];复原方法是指使水下图像恢复到图像降质之前的状态，该方法依赖于具体的模型，主要有以暗通道先验[9]为代表的去雾算法。

例如 WEN等结合光衰减特性，考虑蓝绿两通道信息求取暗通道，并估计红通道透射率[10]；LI等采用空气暗原色先验求取水下图像透射率，根据物体颜色和图像背景光透射率进行自适应优化[11]；BERMAN等利用蓝红和蓝绿色衰减比，自适应估计水下透射率，还原水下色彩[12]；WANG等利用卷积神经网络和基于白平衡的颜色偏差消除策略消除水下色彩失真现象[13]。

上述算法一定程度上改善水下图像质量，但在密闭空间内光照强度不均的情况下，上述算法表现不佳，使其应用范围受到限制。

对于密闭空间内水下目标物体成像，挑战性体现在腔体内水质问题导致成像模糊和色彩失真，以及空间限制导致的光照强度不均[14]。

第32卷第8期电网技术V ol. 32 No. 8 2008年4月Power System Technology Apr. 2008文章编号：1000-3673（2008）08-0086-05 中图分类号：TM76 文献标识码：A 学科代码：470·4017广域测量系统数据在线无损压缩算法齐文斌1，李东平2，杨东1，吴京涛1（1．北京四方继保自动化股份有限公司，北京市海淀区 100085；2．内蒙古达拉特电厂，内蒙古自治区达拉特 017000）Online Lossless Compression Algorithm of WAMS DataQI Wen-bin1，LI Dong-ping2，YANG Dong1，WU Jing-tao1（1．Beijing Sifang Automation Co. Ltd.，Haidian District，Beijing 100085，China；2．Dalate Power Plant，Dalate 017000，Inner Mongolia Autonomous Region，China）ABSTRACT: In wide-area measurement system (WAMS) the data uploading frequency of phasor measurement units (PMU) is 100 Hz, to meet the high requirement to occupied storage space and writing speed of this system, the PMU data should be compressed. To reserve enough accuracy of raw data, the data compression efficiency of swing door trending (SDT) algorithm is too low; and the calculation time of Huffman coding compression algorithm is too long, thus it is difficult for these compression algorithms to adapt to the sampling speed of real-time data in WAMS. In this paper an online compression algorithm for real-time WAMS data is proposed, in which the elementary transformation of raw data is performed and only the increments of process data are reserved; the float data is decomposed into single bytes to be accessed; and the online lossless compression of process data in WAMS is realized by improved Lemple-Ziv-Welch (LZW) algorithm. Using this data compression method, the compression calculation time for 6000 continuous real-time data is 16ms and the compression ratio is lower than 30%, therefore the demand of power system analysis and calculation on process data can be satisfied. Engineering experiences show that the proposed data compression algorithm is effective and reliable, and can meet the requirement of real-time data storage in WAMS.KEY WORDS: wide-area measurement system；power system；data storage；lossless data compression；compression algorithm摘要：广域测量系统(wide-area measurement system，WAMS)中，相量测量装置(phasor measurement unit，PMU)上传数据频率为100Hz，存储占用空间和写入速度对系统要求很高。