电子信息类专业英语 第二版 李白萍主编 第十七单元B译文 Passage B Compression/Decompression Techniques 压缩/解压缩技术
Numerous methods have been developed for the compression of digital image data. One of the principal drivers for this development is the television industry where quality image data must be transferred to receivers using relatively simple equipment. The development of high definition television is further focusing the attention of industry and university scientists toward problems of data reduction and digital transmission. The principal evaluation criteria for the analysis of compressed versus uncompressed imagery is whether a person can tell the difference between the images. A more implemental measure is the Root Mean Square (RMS) error between the original image and the image that has been compressed. Compression rates may be generated by determining the size of the compressed image in terms of number of bits per image pixel for the original image.[1]
数字图像数据的压缩的许多方法已经开发出来。为这项发展一个主要的驱动是电视产业，它的质量图像数据必须使用相对简单的设备传输到接收器。高清电视的发展进一步使产业和大学的科学家集中注意在减少数据和数字传输的问题。对于压缩和未压缩图像的分析的主要评估标准是一个人是否可以区分图像不同之处。更有帮助的措施是原始图像和图像压缩之间的均方根(RMS)错误。压缩率可能是由确定大小的压缩图像以原始图像的每个图像像素的比特数形式表现。[1]
Here we only considers compression of single high resolution multi-spectral images. Higher compression rates will be achieved in a motion sequence where frame to frame variations may be quantified and only the changes from a reference image need be coded.
这里我们只考虑压缩单一的高分辨率多光谱图像。更高的压缩率将在帧到帧之间的运动序列变化中量化和只有从一个需要进行编码的参考图像的更改达到。
There are two general types of compression: (1) loss-less, and (2) loss. Loss-less compression means that you can achieve a certain compression factor and be able to exactly reproduce the original image. Loss compression on the other hand allows some loss, but has the potential for much higher compression rates. No matter what technique that you use, the exact rate is very dependent on the complexity of the image that you are analyzing. For example, the normal best that can be achieved with loss-less encoding in a rate of 2 bits per pixel. In fact, for some Land-sat scenes with urban areas and many small farms, the factor of 2 bits per pixel may not be able to achieved. The same technique applied to a Land-sat image of the mid-west where large fields occur and few shadows exist in images might produce a much better compression.
一般有两种类型的压缩:(1)无损压缩,和(2)有损。无损压缩意味着你可以实现一定的压缩因素并且能够完全还原原始图像。另一方面损失压缩允许一些损失,但拥有潜在的更高的压缩率。不管你使用什么技术,精确率是非常依赖于你分析的图像的复杂度。例如,正常可以实现最好的无损编码率是2位/像素。事实上,对于一些城市地区和许多小农场的卫星遥感图像,2位/像素的因素可能无法实现。同样的技术应用于西部的大油田的遥感图像,而且很少在可能产生更好的压缩的图像中存在一点阴影。
One loss-less technique is known as run length encoding. The compression algorithm processes each line of input imagery looking for regions in which data values are the same. If ten pixels in the original image have a value of 10, then the same data may be represented as a data value, 10, and a multiplier saying how many times the value is repeated before a changed value. Huffman encoding follows a similar process. These loss-less techniques are generally called entropy coding techniques, and have application in document imaging, desktop publishing, and GIS. It should be noted that entropy coding does not work exceptionally well in the representation of remote sensing images.
一个无损技术被称为运行长度编码。压缩算法流程处理输入图像的每一行去
寻找区域相同的数据值。如果原始图像中的10个像素值为10,那么相同的数据可能被表示成一个数据值,10,而且乘数器实现重复多少次之前改变的值。哈夫曼编码遵循类似的过程。这些无损技术通常被称为熵编码技术,并应用文档成像、桌面发行和GIS。应该注意的是,熵编码不异常地工作在遥感图像的表示中。 In remote sensing imagery it is well known that there may be significant correlation between different bands of multi-spectral data. In image processing, a procedure called principal components has been designed to identify correlation between image bands and to create a new set of transformed bands that represent a new color space in which the new image bands are uncorrelated.[2] The procedure also provides a measure of the percent of the original variation present in the original image as found in each of the new transformed bands. For Land-sat TM data, three to four of the transformed images represent 98 percent of the variance in the original images; therefore, a compression factor of 2 would be achieved with little loss.
众所周知在遥感图像中,可能不同波段的多光谱数据之间有显著的相关性。在图像处理中,一个叫做主成分的程序已经被设计用于识别图像波段之间的相关性,并创建一套转化的波段来代表一个新的颜色空间在那里新图像的波段是不相关的。[2]程序还提供了一个在原始变化的图像中测量每个新波段出现变化的百分比。对于Land-sat TM数据,转换后的图像的三到四个代表在原始图像中方差的98%;因此,压缩因子2的实现几乎没有损失。
Another type of transform coding does not involve a rotation of the color space, but instead represents images in terms of spatial frequency of certain base functions. Fourier transforms map an image into a spatial frequency image base on sin and cosine functions. A fast computer implementation of the Discrete Fourier Transform (DFT) is known as a Fast Fourier Transform (FFT). Discrete Cosine Transforms (DCT’s) map the same image to a spatial frequency image based only on the cosine function. Each pixel may be represented by a series of trigonometric functions and coefficients derived from the images. If all terms of the transform’s trigonometric functions are used, compression is minimal. As more terms are deleted, compression goes up, but
电子信息类专业英语第二版 李白萍主编十七单元译文
