1 2 Compression Algorithms JPEG

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联合图像专家组)

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。

图像压缩编码方法

图像压缩编码方法
图像压缩编码方法是通过减少图像数据的冗余部分来减小图像文件的大小，以便于存储和传输。

以下是常见的图像压缩编码方法：
1. 无损压缩：无损压缩方法可以压缩图像文件的大小，但不会丢失任何图像数据。

常见的无损压缩编码方法包括：
- Huffman编码：基于字符出现频率进行编码，将频率较低的字符用较长的编码表示，频率较高的字符用较短的编码表示。

- 预测编码：根据图像像素间的相关性进行编码，利用当前像素与附近像素的差异来表示像素值。

- 霍夫曼编码：利用霍夫曼树来对图像数据进行编码，降低数据的冗余度。

- 算术编码：根据符号的出现概率，将整个编码空间划分为不同部分，每个符号对应于不同的编码区域。

2. 有损压缩：有损压缩方法可以在压缩图像大小的同时，对图像数据进行一定的丢失，但尽量使丢失的数据对人眼不可见。

常见的有损压缩编码方法包括：
- JPEG压缩：基于离散余弦变换(DCT)的方法，将图像数据转换为频域表示，
然后根据不同频率成分的重要性进行量化和编码。

- 基于小波变换的压缩：将图像数据转换为频域表示，利用小波基函数将图像分解为低频和高频子带，然后对高频子带进行量化和编码。

- 层次编码：将原始图像数据分为不同的预测层次，然后对不同层次的误差进行编码，从而实现压缩。

需要注意的是，不同的压缩编码方法适用于不同类型的图像数据和压缩要求。

有些方法适用于需要高压缩比的情况，但会引入更多的失真，而有些方法适用于需要保留图像质量的情况，但压缩比较低。

因此，在选择图像压缩编码方法时，需要根据具体要求和应用场景进行权衡和选择。

standard-JPEG2000

Digital image compression technology Research and DevelopmentTian Y ong 1, DING Xue-Jun 2(1. Northeastern University, embedded systems Neusoft Institute of Information Engineering, Liaoning, Dalian 116023; 2. Northeast University of Finance and Information Engineering, Liaoning, Dalian 116023)Abstract: Digital image compression technology for digital image information on the network for rapid transfer and real-time processing is of great significance. This article describes several of the most important of the current imageCompression algorithms: JPEG, JPEG2000, fractal image compression and wavelet transform image compression, summed up the advantages and disadvantages of their development prospects. A brief introduction and then arbitrarily shaped visual object codingAlgorithm for the status quo, and pointed out that this algorithm is a high compression ratio resulting image compression algorithms.Keywords: JPEG; JPEG2000; fractal image compression; wavelet transform; arbitrarily shaped visual object codingCLC number: TP3 Abstract: A Article ID :1672-545X (2007) 04-0072-04With the multimedia technology and communication technology continues to evolve, multimedia entertainment, the letterInterest rates have kept the information highway data storage and transmission should be set higherDemand, but also to the limited bandwidth available to a severe test, especially those with large dataThe amount of digital image communication, more difficult to transport and storage, which greatly restricted the image passThe development of the letter, so the image compression technology has been more and more attention. Image compressionThe purpose is to shrink the original image of a larger less bytes exhausted expression and transmission,And asked to recover a better quality image. The use of image compression that can reduce the mapSuch as the burden of storage and transmission, so that the image on the network to achieve fast transfer and real-time OfficeLi.Image compression techniques can be traced back to 1948, raised the number of television signalsWord-oriented, and today has 50 years of history [1]. During this period there have been manyKinds of image compression coding method, especially in the late 20th century, 80 years later,Because the wavelet transform theory, fractal theory, artificial neural network theory, visual simulation of LiOn the establishment of image compression technology has been anunprecedented development, in which fractal imageImage compression and wavelet image compression is the current hot research. In this paper, present, the most widelyPan-use image compression algorithms are reviewed and discussed their advantages and disadvantages, as well as hairShow prospect.1 JPEG compressionResponsible for developing the still image compression standard "Joint Photographic Expert Group" (JointPhotographic Expert Group, referred to as JPEG), was formed in January 1989Adaptive DCT-based JPEG technical specifications of the first draft, followed by severalChanges to the 1991 formation of the draft international standard ISO10918, and after one year into theAs an international standard, called JPEG standard.1.1 JPEG compression principle and characteristics ofJPEG algorithm in the first block of the image processing, generally divided into each other without re -The size of stacked blocks, and then every one to two-dimensional discrete cosine transform (DCT). VariableAfter changing coefficients are not relevant, and the coefficient matrix of the energy concentrated in the low-frequency area, the rootQuantization table, according to quantify, quantify the results of retainedlow-frequency part of the coefficient, remove theThe high-frequency part of the coefficients. The quantized coefficients by Zigzag scan re-organization, RanAfter Huffman coding. JPEG features are as follows:Advantages: (1) the formation of international standards; (2) has a mid-range and high bit-rate on theGood image quality.Disadvantages: (1) Because of the image block, at high compression ratio when you have a seriousBlocking effects; (2) factor to quantify is the lossy compression; (3) the compression ratio is not high, smallAt 50 [2].JPEG compressed image box effect occurs because: Under normal circumstances the imageThe signal is highly non-stationary, it is difficult to characterize the process of using Gauss, and the imageSome of the mutant structure, such as the edge information than the image smoothness important, with the cosine-basedNon-linear approximation for image signals the result is not optimal [3].1.2 JPEG compression status and prospects of the study [2]JPEG at high compression ratio for cases arising under the box effect, decompresses the image than thePoor, in recent years made a lot of improvement methods, the most effective is the following two ways:(1) DCT zerotree codingDCT block zero-tree coding the DCT coefficients in the composition of log2N sub-zone, RanAfter the zero-tree coding scheme used to encode. Compression ratio in the same circumstances, thePSNR value higher than the EZW. However, in the case of high compression ratio, the box effect is stillDCT the Achilles heel of zero-tree coding.(2) layer-type DCT zerotree codingThe algorithm for the DCT transform the image will be low-frequency blocks together, doing anti -DCT; pairs of newly acquired images do the same transformation, and so it goes, until satisfiedRequest until the. Then Layered DCT transform and zero-tree arrangement of the coefficients over zeroTree coding.JPEG compression one of the biggest problem is that at high compression ratio when you have a seriousBlocking effects, so future research should be focused on solving the resulting DCT transformBlocking effects, taking into account the human visual system with the combination of compression.2 JEPG2000 compressionJPEG2000 is a ISO / IEC JTCISC29 standards group responsible for formulatingThe new still image compression standard. One of the biggest improvements is that it uses wavelet transform on behalf of theFor the cosine transform. In March 2000 the T okyo conference to identify a color still imageEncoding a new generation of image compression standard-JPEG2000 coding algorithm.2.1 JPEG2000 compression principle and characteristics ofJPEG2000 Codec encoder and decoder block diagram in Figure 1 [4]. Encoding process is mainly divided into the following processes:pre-processing, core processing and bitStream organization. Pretreatment section includes image segmentation, DC level (DC) displacement and sub -Received date: 2007 - 01 - 16Author: T akada (1975 -), male, Master, Lecturer, major research interests include digital image processing, integrated circuit design, etc.; DING Xue-jun(1978 -), female, master's degree, teaching assistants, the main research direction for informationAnd network security, digital image processing.The amount of change. Core processing in part by the discrete wavelet transform, quantization and entropy coding form. BitStream part of the organization include zoning, code blocks, layers and packages organization.JPEG2000 format image compression ratio can be based on the current JPEG Further increased by 10% to 30%, and the compressed image is even more delicate smooth. ForThe current JPEG standard, in the same compressed stream can not provide lossy andLossless compression, while in JPEG2000 system, by selecting the parameters, be able to imageFor lossy and lossless compression. Now the network is based on JPEG image download"Block" transmission, while the JPEG2000 image format support for progressive transmission, which use theHouseholds do not receive the entire image compression bit stream. Since JPEG2000 uses wavelet technologySurgery may be random access to some image region of interest (ROI) of the compressed stream, rightCompressed image data transmission, filtering and other operations [4]. Figure 1 JPEG2000 compression and decompression of the overall process 2.2 JPEG2000 compression prospectsJPEG2000 standard applies to a variety of image compression coding. Its application fieldWill include the Internet, fax, printing, remote sensing, mobile communications, medical, digital librariesAnd e-commerce [5]. JPEG2000 image compression standard will become the 21st century, the mainFlow still image compression standard.3 Wavelet Image Compression3.1 Principle of Wavelet Image CompressionWavelet Transform for image coding basic idea is that the image according to MallatT ower fast wavelet transform algorithm for multi-resolution decomposition. The specific process is:First, the image multi-level wavelet decomposition, then the amount of the wavelet coefficients of each layer, And re-pairs of the quantized coefficients encoded. Wavelet image compression is the current image compressionOne of the hot shrinkage has been formed based on wavelet transform of international compression standards, such as theMPEG-4 standard, and as mentioned above the JPEG2000 standard [2].3.2 Wavelet Image Compression Status and ProspectsAt present the three highest levels of wavelet image coding are embedded wavelet zero -Tree Image Coding (EZW), hierarchical tree distribution of the sample image coding (SPIHT) andExpand image compression coding (EBCOT).(1) EZW coder [6]In 1993, Shapiro introduced a wavelet "zero-tree" concept, by defining POS, NEG, IZ and ZTR symbols are four kinds of recursive spatial wavelet tree coding, there areEffective to remove the encoding of high-frequency coefficients, which greatly improved the coding of wavelet coefficientsEfficiency. This algorithm is used to quantify and embedded progressive coding mode, the algorithm complexityLow. EZW algorithm for breaking the long-term deep faith in the field of information processing criteria: highly efficient compressionReduction encoder must pass the highly complex algorithms to get, so EZW codingData compression device in the history of a landmark decision.(2) SPIHT encoder [7]By Said and Pearlman proposed a collection of hierarchical wavelet tree segmentation algorithm(SPIHT) is the use of space partitioning tree hierarchical approach to effectively reduce the bit-planeCoded symbol set size. EZW, compared with, SPIHT algorithm is constructed by two kinds of non -The same type of space zero-tree, to make better use of the wavelet coefficients of the amplitude attenuation.Like with the EZW encoder, SPIHT encoder algorithm complexity low, the resultingIs also an embedded bit stream, but the encoder performance than EZW greatly improved.(3) EBCOT encoder [8]Optimized cut-off point of the embedded block coding (EBCOT) first wavelet decompositionEach sub-band is divided into a relatively independent of the code block, and then use a hierarchical optimizationTruncation algorithm to the code block is encoded to generate compressed stream, the results of image compressionReduction stream is not only scalable but also has resolution of SNR can be Extension, you can also support the random image is stored. In comparison, EBCOT algorithm complexity compared with EZW and SPIHT are referred to With its high compression performance than SPIHT increased slightly.Wavelet image compression is considered the most promisingOne of the image compression algorithm. Wavelet image compression research has focusedWavelet coefficients in the coding issues. In later work,Should fully consider the human visual system to further improve the compression ratio,T o improve the image quality. And consider the wavelet transform and other compressionMethod combination. For example, with the combination of fractal image compression is the currentA research focus [2].4 Fractal Image CompressionIn 1988, Barnsley proved by experiments fractal image compression ratio can beClassical image coding technology, high compression ratio of several orders of magnitude. In 1990, BarnsleyStudents AEJacquin making a partial iterated function system theory, will bring the use of fractalIn image compression on the computer automatically possible.4.1 The principle of fractal image compressionFractal compression using mainly self-similar characteristics, through the iterated function system (IteratedFunction System, IFS) to achieve. The theory is based on iterated function systemTheorem and the collage theorem.Fractal image compression the original image is divided into severalsub-images, and then eachSub-image corresponds to an iterated function, sub-images to iterated function storage, iterative letterThe number of the more simple, the greater the compression ratio. The same decoding as long as the transfer out of each sub-mapLike the corresponding iteration function iteration, it can be restored out of the original sub-image, from theObtained the original image [9].4.2 several major fractal image coding techniques [9]With the fractal image compression technology, an increasing number of algorithms have been proposed,Based on the different fractal characteristics can be divided into several major coding method.(1) The size of encoding methodSize coding method is based on fractal geometry, the use of small-scale irregular metricCurve length method, similar to the traditional sub-sampling and interpolation methods, the main non -Scale with the encoding method is that the introduction of the idea of fractal, scale with the imageThe complexity of the various components of the different change.(2) The method of iterated function systemIterated function system method is the most studied, the most widely used as a sub -Shaped compression technology, it is a patchwork of human-computer interaction technology, it is based on the natural world mapLike in the prevailing global and local autocorrelation features, search for such auto-correlation mappingShot between the expression, that is, affine transformation, and by storing a small amount compared with the original image dataAffine coefficients to achieve the purpose of compression. If you find a simple affine transformation Effective, then the iterated function system can achieve very high compression ratio.(3) A-E-Jacquin fractal programA-E-Jacquin fractal program is an automated block-based fractalImage compression scheme, it is also looking for a mapping process, but are looking for rightAs the domain is divided into blocks of the image after the partial and local interests. In this scenario,There are still some redundancy can be removed, and its decoding the image there is a clearBlocking effects.4.3 The prospects for fractal image compression [2]Although the fractal image compression in the field of image compression is not dominant, but theFractal Image Compression take into account both local and partial, but also take into account the relevant part and the wholeNature, suitable for self-similar or self-affine image compression, but in nature there is a large number ofSelf-similarity or self-affine geometry, so its scope is broad.5 other compression algorithmsIn addition to these several common image compression methods, there are: NNT (number theoryTransform) compression, the compression method based on neural networks, Hibert scanned image compression sideFrance, adaptive polyphase subband compression method, etc. This is not to repeat. Following is a briefIn recent years, several arbitrarily shaped texture coding algorithm [10 ~ 13].(1) The shape-adaptive DCT (SA-DCT) algorithmSA-DCT to an arbitrarily shaped visual objects into the image blocks, each blockT o DCT transform, which implements a similar shape-adaptive Gilge DCT [10,11]Transform an effective transformation, however, it Gilge DCT transform lower complexity. ButSA-DCT also has shortcomings, it is the pixel onto one side of the border with the rectangle relative to flatQi, so some spatial correlation may be missing, so further out DCT transform, There is a greater distortion of the [11, 14, 15].(2) Egger methodEgger and others [16, 17] proposed a wavelet applied to objects of arbitrary shape changeFor the program. In this scenario, the first visible line of pixels onto the object and bounding boxThe right boundary phase flush position, then the usefulness of each line pixel wavelet changesChange, followed by a further direction of the wavelet transform. This program, take advantage of a smallThe local characteristics of wavelet transform. However, the program also has its problems, such as might lead toPlay an important part of the high-frequency part of the merger with the boundary, can not guarantee that the distribution coefficient of each of the The same inter-correct phase, as well as the second direction may cause the non-wavelet decompositionContinuous and so on.(3) The shape-adaptive discrete wavelet transform (SA-DWT)Li et al proposed a novel encoding of arbitrary shape of the object, SA-DWT Coding [18 ~ 22]. The technology includes the SA-DWT and zerotree entropy coding extension(ZTE), and embedded wavelet coding (EZW). SA-DWT is characterized by: afterSA-DWT coefficients after the count of visual objects of arbitrary shape with the original number of pixelsThe same; wavelet transform of the spatial correlation, regional attributes, and sub-band between the Zi XiangSimilarity, in the SA-DWT can be well manifested in; for the rectangular area, SA-DWT and the same as the traditional wavelet transform. SA-DWT coding technology to achieveHave been new multimedia coding standard MPEG-4's for any shape of a static patternRationale used by the encoding.In future work, you can fully utilize the human visual system to the image edge Edge of some of the more sensitive features, try to image objects of interest to split up,Some of its edges, internal textures and objects other than some background section according to differentCompression ratio of compression, so that compressed images can achieve greater compression ratio and moreAdd to facilitate transmission.6 SummaryImage compression technology research for decades and has made great achievements, but there areMuch to be desired, it is worth further examination. Wavelet image compression and fractal image compressionReduction is currently a hot research, but both also have their own shortcomings, in the future work,Should be with the human visual properties. In short, image compression is a very developedPromising area of research, a breakthrough in this area of life for our information and communicationT o the development of far-reaching implications.References:[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]Tian Qing. Image compression techniques [J]. Police T echnology, 2002, (1): 30 - 31.ZHANG Hai-yan, Wang Mu, et al. Image compression technique [J]. Journal of System Simulation, 2002, 14 (7):831 - 835.Zhang Zongping, Liu Guizhong. Wavelet-based video image compression research [J]. Journal of Electronics,2002, 30 (6): 883 - 889.Zhou Ning, T ang Xiaojun, Xu Park. JPEG2000 image compression standard and its key algorithm [J].Modern electronic technology, 2002, (12): 1 - 5.WU Y onghui, YU Jian-xin. JPEG2000 image compression algorithm overview and network application prospects [J].Computer Engineering, 2003, 29 (3): 7 - 10.J MShaprio. Embedded image coding using zerotree of wavelet coefficients [ J]. IEEE Trans. On Signal Processing, 1993, 41 (12): 3445 - 3462.A Said, WA Pearlman. A new fast and efficient image codec based onset partitioning in hierarchical trees [J]. IEEE Trans. on Circuits andSystems for Video T ech. 1996, 6 (3): 243 - 250.D T aubman. High performance scalable image compression with EBCOT [ J]. IEEE Transactions on Image Processing, 2000, 9 (7): 1158 --1170.Xulin Jing, MENG Li-min, Jianjun. Wavelet image compression with branches in comparison and applications.China's cable TV, 2003, 03/04: 26 - 29.M Gilge, T Engelhardt, R Mehlan. Coding of arbitrarily shaped image segments based on a generalized orthogonal transform [J]. Signal Processing: Image Commun., 1989, 1 (10): 153-180.T Sikora, B Makai. Shape-adaptive DCT for generic coding of video [J].IEEE Trans. Circuits Syst. Video T echnol., 1995, 5 (1): 59-62.T Sikora, S Bauer, B Makai. Efficiency of shape-adaptive 2 - D transformsfor coding of arbitrarily shaped image segments [J]. IEEE Trans.Circuits Syst. Video T echnol., 1995, 5 (3): 254-258.E Jensen, K Rijk, et al. Coding of arbitrarily shaped image segments [C]. Proc. Workshop Image Analysis and Synthesis in Image Coding, Berlin, Germany, 1994: E2.1-E2.4.M Bi, SH Ong, YH Ang. Comment on "Shape-adaptive DCT forgeneric coding of video "[J]. IEEE Trans. Circuits Syst. Video T echnol., 1996, 6 (6): 686-688.P Kauff, K Schuur. Shape-adaptive DCT with block-based DC separationand Delta DC correction [J]. IEEE Trans. Circuits Syst. VideoT echnol., 1998, 8 (3): 237-242.O Egger, P Fleury, T Ebrahimi. Shape-adaptive wavelet transform for zerotree coding [C]. Proc. Eur. Workshop Image Analysis and Coding for TV, HDTV and Multimedia Application, Rennes, France, 1996: 201 --208.O Egger. Region representation using nonlinear techniques with applications to image and video coding [D]. Ph.D. dissertation, Swiss FederalInstitute of T echnology (EPFL), Lausanne, Switzerland, 1997.S Li, W Li, et al. Shape adaptive vector wavelet coding of arbitrarilyshaped texture [S]. ISO / IEC JTC/SC29/WG11, MPEG-96 - m1027,1996. WLi, F Ling, H Sun. Report on core experiment O3 (Shape adaptivewavelet coding of arbi t rarily shaped texture) [S]. ISO/IECJTC/SC29/WG11, MPEG- 97- m2385, 1997.S Li ,W Li. Shape adaptive discrete wavelet transform for coding arbirarilyshaped texture[C]. Proc. SPIE VCIP’97, 1997, 3024: 1046–1056.[19][20][21][22]S Li, W Li, et al. Shape adaptive wavelet coding [C]. Proc. IEEE Int.Symp. Circui t s and Systems ISCAS’98, 1998, 5: 281–284.S Li, W Li. Shape- adaptive di s crete wavelet transform for arbitrarilyshaped vi s ual object coding[J]. IEEE Trans. Circuits Syst. Video Technol.,2000, 10(5): 725–743. [7][8][[9][10][11]唐琦, 董宏, 朱棣, 黄克菲.用深滚压技术提高曲轴疲劳强度的应用研究[J].汽车技术, 2005,( 8) : 33.李金桂.防腐蚀表面工程技术[M].北京: 化学工业出版社, 2003.孙希泰.材料表面强化技术[M].北京: 化学工业出版社, 2005.覃正光. 滚压技术在气缸套加工中的应用[J]. 内燃机配件, 1995,( 2) : 25.秦书勤.超精内孔滚压技术及其应用[J].航天工艺, 2000,( 6) : 48The Research and Development for Digital Image Compres s ion TechnologyTIAN Yong 1 DING Xue- jun 2(Department of Embedded SystemEngineering, Neusoft Institute of Information, Northeastern UniversityDalian Liaoning 116023, China;Institute of Information Engineering, Dongbei University of Finance &Economics, Dalian Liaoning 116023, China)Abs tract: Digital image compression technology is of special intrest for the fast transmission andreal- time processsing of digital imageinformation on the internet. The paper introduces several kinds of the most important image compression algorithms at present: JPEG,JPEG2000, fractal image compression and wavelet transformation image compression, and summarizes their advantage and disadvantage anddevelopment prospect. Then it introduces simply the present development of coding algorithms about arbitrary shape video object, andindicates the algorithms have a high compression rate.Key words : JPEG; JPEG2000; Fractal image compression;Wavelet transformation; Arbitrary shape video object codingResearch on the Outline of Strengthening Technology of SurfaceDeformationXU Zheng- gong1, CHEN Zong- tie1, HUANG Long- fa2(1. Guangxi University, Mechanical Engineering School, Nanning 530004, China; 2 .Guizhou University, Mechanical Engineering School,Guiyang 550003, China)Abs tract: Surface- strengthening technology is one of techniques, which is widely used for studying in home and aboard, Methods of metallicstrengthening of surface deformation are rolling with pressing, inside extrusion, peening and so on. Its characteristics are prominent strengtheningeffect and low cost. The article has mainly generalized the sort, intention, and action of surface- strengthening technology; analyzed thecharacteristics of primary methods of the strengthening of surface deformation; summarized the outlines of primary methods and developmenttendency at present.Key words : Surface deformation; Rolling with pressing; Inside extrusion; Peening。

jpeg编解码过程详解海王博客园

JPEG编解码过程详解- 海王- 博客园JPEG（Joint Photographic Experts Group）是联合图像专家小组的英文缩写。

它由国际电话与电报咨询委员会CCITT（The International Telegraph and Telephone Consultative Committee）与国际标准化组织ISO于1986年联合成立的一个小组，负责制定静态数字图像的编码标准。

小组一直致力于标准化工作，开发研制出连续色调、多级灰度、静止图像的数字图像压缩编码方法，即JPEG算法。

JPEG算法被确定为国际通用标准，其适用范围广泛，除用于静态图像编码外，还推广到电视图像序列的帧内图像压缩。

而用JPEG算法压缩出来的静态图片文件称为JPEG文件，扩展名通常为*.jpg、*.jpe*.jpeg。

JPEG专家组开发了两种基本的压缩算法、两种数据编码方法、四种编码模式。

具体如下：压缩算法：l 有损的离散余弦变换（Discrete Cosine Transform，DCT）；l 无损的预测技术压缩。

数据编码方法：l 哈夫曼编码；l 算术编码；编码模式：l 基于DCT顺序模式：编/解码通过一次扫描完成；l 基于DCT递进模式：编/解码需要多次扫描完成，扫描效果从粗糙到精细，逐级递进；l 无损模式：基于DPCM，保证解码后完全精确恢复到原图像采样值；l 层次模式：图像在多个空间多种分辨率进行编码，可以根据需要只对低分辨率数据作解码，放弃高分辨率信息。

在实际应用中，JPEG图像使用的是离散余弦变换、哈夫曼编码、顺序模式。

JPEG压缩编码算法的主要计算步骤如下：(0) 8*8分块。

(1) 正向离散余弦变换(FDCT)。

(2) 量化(quantization)。

(3) Z字形编码(zigzag scan)。

(4) 使用差分脉冲编码调制(DPCM)对直流系数(DC)进行编码。

(5) 使用行程长度编码(RLE)对交流系数(AC)进行编码。

JPEG2000码率控制算法研究

V
上海交通大学硕士学位论文
ABSTRACT
algorithms could achieve higher coding efficiency compared with other typical ones. In addition, the idea that SEUCA makes use of the interframe correlation to improve the coding efficiency, and the idea that LASD divides the total rate allocation process into two stages and utilizes two buffers to control the allocation respectively can also benefit for other video compression standards.
I
上海交通大学硕士学位论文
摘要
该算法根据各个码块所在子带的能量权重系数的大小，选择不同的每次熵编码编码通道数，在熵编码的同时进行码率控制ቤተ መጻሕፍቲ ባይዱ可在保证压缩图像质量的同时提高一定的编码效率。对于Motion-JPEG2000，本文主要研究了恒定码率编码（CBR）和可变码率编码（VBR）两种方式下的码率控制算法问题。本文首先较为详细地叙述了几种典型的CBR算法和VBR漏桶算法，并且具体实现和验证了Motion-JPEG2000的两次扫描VBR编码。为提高MotionJPEG2000的编码效率，本文提出了两种较为有效的码率控制算法： SEUCA （Slope Estimation Using Correlation Algorithm）和LASD （Leakbucket Algorithm with Scene-change Detection）。SEUCA算法利用帧间相关性，使用前一个已编码帧的率失真斜率值估计当前帧的率失真情况，并结合使用IREC ( Integrated Rate-control and Entropy-coding )和 EIREC算法 ( Enhanced Integrated Rate-control and Entropy-coding )进行编码，可以有效地提高CBR编码的编码效率，降低编码计算冗余。 LASD算法通过场景切换检测将视频序列合理地划分为若干个场景图像组，先为每个场景图像组分配对应的平均码率，然后再进一步为图像组中的每帧图像分配各自的编码码率。此时缓冲区里的样本帧编码情况可以表征更加广泛的图像帧，因而各帧的码率分配更加合理，可以取得图像质量更为恒定的VBR码流。为验证上述的几种码率控制算法的性能，论文将其与其它比较典型的算法进行了多方面的分析和比较，理论分析和仿真实验的结果均表明本文所提出的算法编码效率更高，编码性能更好，比较有利于图

图像压缩的算法及其国际标准

静态图像压缩－DWT变换
二维DWT变换：
原始图像
列变换
行变换
三层DWT分解后的结果：
静态图像压缩－DWT变换
三层DWT分解的结果：
静态图像压缩－分形方法
自相似性：无论几何尺度怎样变化，物体任何组成部分的形状都以某种方式与整体相似。关键在于引入了局部与全部相关去冗余的思想。压缩效率与物体本身性质有关。
有损压缩
分形编码（Fractal）矢量量化（Vector Quantization）人工神经网络方法（ANN）
静态图像压缩－变换编码
K-L变换
变换编码
离散余弦变换（DCT）
Gabor变换小波变换（DWT）
静态图像压缩－K-L变换
K－L变换是最佳变换，将原始信号中相关性很强的空域变换到相关性彻底去除的变换域；无快速算法而难以实现。
动态图像编码（Video Coding）
静态图像压缩
静态图像压缩无损压缩（Lossless Compression）
有损压缩（Lossy Compression）
静态图像压缩－无损压缩
差分脉冲调制方法（DPCM）
去除相关无损压缩统计编码
分层内插法（HINT）差分金字塔方法（DP）多重自回归方法（MAR）
H.261：第一个高效视频编码标准算法。图像编码的其他几个国际标准（如JPEG、MPEG、CCIR723等）都是由它演变而来的。 1984年12月，CCITT第15研究组成立了“可视电话编码专家组”，并在1988年提出了视频编码器的H.261建议。它的目标是P×64K（P=1～30）码率的视频编码标准，以满足ISDN日益发展的需要。主要应用对象是视频会议的图像传输。它的视频压缩算法必须能够实时操作，解码延迟要短，当P=1或2时，只支持帧速率较小的可视电话，当P>=6时，则可支持电视会议。 H.261建议的原理结构的要点是：采用运动补偿进行帧间预测，以利用图像在时域的相关性；对帧间预测误差以 8×8或者16×16为宏块，进行DCT变换，以利用图像在空域上的相关性；接着对DCT变换系数设置自适应量化器，以利用人们的视觉特性；再采用Huffman熵编码，获得压缩码流。

JPEG图像压缩编码原理及格式

图像灰度级gray(x,y)
JPEG中的余弦变换
对pic2进行DCT：
pic2
DCT:高频系数很小
JPEG中的余弦变换
pic3：
pic3
图像灰度级gray(x,y)
JPEG中的余弦变换
对pic3进行DCT：
pic3
DCT:高频系数较大一些
JPEG中的余弦变换
在JPEG进行余弦变换后，由8x8像素图像块获得8x8个频域系数C(u,v)，如果存储64个频域系数，则图像数据并不能压缩。
(DCT系数x1000)
DCT:高频系数很小
JPEG中的余弦变换
对pic0进行DCT：
pic0
DCT:高频系数很小
JPEG中的余弦变换
pic1：
pic1
图像灰度级gray(x,y)
JPEG中的余弦变换
对pic1进行DCT：
pic1
DCT:高频系数很小
JPEG中的余弦变换
pic2：
pic2
0
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0
0
2
0
0
0
ห้องสมุดไป่ตู้
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
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0
0
0
0
0
0
0
0
0
0
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0
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{74,33,31,-1,-2,-1,2,-2,-2,2,0,0,……,0};
由于大量的0连续排列，可以用“行程编码(Run Length Coding)”方法节约存贮空间。

Mac命令行中的像处理技巧转换和调整像

Mac命令行中的像处理技巧转换和调整像Mac命令行中的图片处理技巧：转换和调整图像Introduction在Mac操作系统中，命令行是一种非常强大的工具，可以用来完成各种任务，包括处理和编辑图像。

本文将介绍一些在Mac命令行中处理和调整图像的技巧，包括图像格式转换、大小调整、旋转等操作。

一、图像格式转换1. 将图片格式从JPEG转换为PNG：使用命令：`sips -s format png <input_image.jpg> --out<output_image.png>`。

这个命令将把输入的JPEG图像转换为PNG格式，并输出为指定的PNG图像文件。

你也可以指定路径来保存输出文件。

2. 将图片格式从PNG转换为JPEG：使用命令：`sips -s format jpeg <input_image.png> --out<output_image.jpg>`。

这个命令将把输入的PNG图像转换为JPEG格式，并输出为指定的JPEG图像文件。

同样，你可以指定路径来保存输出文件。

3. 批量转换多张图片格式：使用循环结构和上述的转换命令，可以轻松地批量处理多张图片的格式转换。

示例代码：```for file in *.jpg; dosips -s format png "$file" --out "${file%.*}.png"done```这段代码将会将当前目录下的所有JPEG图像转换为PNG格式。

二、调整图像大小1. 将图片调整为指定的宽度和高度：使用命令：`sips -z <height> <width> <input_image.jpg> --out<output_image.jpg>`。

你可以将`<height>`和`<width>`替换为你想要的具体数值，以及指定输入和输出的图像文件。

图像压缩编码的方法

图像压缩编码的方法
图像压缩编码的方法有许多，常见的包括以下几种：
1. 无损压缩：无损压缩的目标是在压缩图像的同时不损失任何数据。

常见的无损压缩方法有：
- Run Length Encoding (RLE)：适用于有大量连续重复像素的图像。

- Huffman 编码：通过统计像素出现的频率和概率来分配不同的编码长度。

- Lempel-Ziv-Welch (LZW) 编码：将连续出现的像素序列映射为较短的编码。

2. 有损压缩：有损压缩的目标是在压缩图像的同时牺牲一部分信息以获得更高的压缩比。

常见的有损压缩方法有：
- 基于变换的压缩方法：如福利耶变换（Discrete Cosine Transform, DCT）和小波变换（Wavelet Transform），将图像从时域转换到频域来减少冗余。

- 基于预测的压缩方法：如差分编码（Differential Encoding）和运动补偿（Motion Compensation），通过计算像素之间的差异来减少冗余。

- 量化：将频域系数或预测误差按照一定的量化步长进行量化，牺牲一部分细节信息。

这些方法可以单独使用，也可以结合使用以实现更高的压缩率。

-。

jpeg 2000和jpeg-ls的实现及遥感影像压缩失真分析

第43卷第2期测绘与空间地理信息Vol.43,No.2Feb.,2020 2020年2月GEOMATICS&SPATIAL INFORMATION TECHNOLOGYJPEG2000和JPEG-LS的实现及遥感影像压缩失真分析林瑶瑶］'2,3,艾波「，高小明2（1.山东科技大学测绘科学与工程学院.山东青岛266590；2.自然资源部国土卫星遥感应川中心，北京100048；3.济南市勘察测绘研究院，山东济南250013）摘要:JPEG2000和JPEG-LS是目前应用比较广泛的两种压缩算法"本文介绍了JP EG2000和JP EG-LS的编码流程、实现方法，通过试验分别分析了遥感影像在不同压缩比下的失真程度大小以及不同的失真类型，结果表明：在相同的中低倍压缩比条件下,JPEG2000和JPEG-LS在影像细节信息保持方面效果非常接近;在高倍压缩比下，JPEG2000和JPEG-LS压缩后影像分别出现了明显的模糊失真和间歇性失真现象，本文分析结论对相关算法的使用和改进有一定的借鉴意义.关键词：JPEG2000；.|PE.G-LS；遥感影像压缩；失真分析中图分类号：P237文献标识码：A文章编号：1672-5867（2020）02-0147-03Implementation of JPEG2000and JPEG-LS and ImageCompression Distortion AnalysisLIN Yaoyao1，2,3,AI Bo1,GAO Xiaoming2(l.Collcge of Geomatics,Shandong University of Science and Technology,Qingdao266590,China；nd Satellite Remote Sensing Application Center,MNR,Beijing100048,China；3.Ji,nan Research Institute of Geotechnical Investigation&Surveying,Ji’nan250013,China)Abstract:JPEG2000and JPEG-LS are two kinds of compression algorithms which are widely used at present.This paper mainly introduces the coding process and implementation of JPEG2000and JPEG-LS.The degree of distortion and different distortion types of the two algorithms at different compression ratios are analyzed by the experiment.The results show that:Under the same medium and low compression ratio,JPEG2000and JPEG-LS have very close effects on image detail information maintenance.Under the high compression ratio.the images have obvious blurred distortion and intermittent distort ion scpalalclv alter J PE-G2000compression and J PEG -LS compression.This paper provides some reference for the use and improvement of the algorithm.Key words:JPEG2000；JPEG-LS；remote sensing image compression；distortion analysis0引言随着网络、通信和多媒休技术的发展.图像数据量与丨日剧增,数据压缩技术成为数字图像处理中的-项关键技术。

压缩复原技术原理

压缩复原技术原理Compression and decompression techniques play a crucial role in the digital world. 压缩和解压技术在数字世界中发挥着至关重要的作用。

Data compression is a method used to reduce the size of data by encoding information using fewer bits. 数据压缩是一种通过使用更少的位来编码信息以减小数据大小的方法。

This is particularly important when it comes to storing or transmitting large amounts of data efficiently. 这在有效存储或传输大量数据时尤为重要。

There are various compression algorithms and techniques available, each with its own advantages and disadvantages. 有各种各样的压缩算法和技术可供选择，每种都有其自身的优点和缺点。

One common compression technique is lossless compression, which allows the original data to be perfectly reconstructed from the compressed data. 一种常见的压缩技术是无损压缩，它允许从压缩数据完美地重建原始数据。

Lossless compression is ideal for situations where every single bit of data matters, such as medical imaging or text files. 无损压缩非常适用于每一位数据都很重要的情况，如医学成像或文本文件。

lz开头的英文单词

lz开头的英文单词LZ Starter KitIntroductionAre you an LZ enthusiast looking to improve your skills in a wide range of areas? Look no further! In this article, we will explore various aspects of LZ and provide you with a comprehensive LZ starter kit. This starter kit will equip you with the necessary tools and knowledge to excel as an LZ practitioner. Let's dive in!1. Understanding LZBefore delving into the specifics, let's first establish a clear understanding of LZ. LZ stands for "Lempel-Ziv," which is a lossless data compression algorithm developed by Abraham Lempel and Jacob Ziv in the 1970s. LZ compression algorithms are widely used in areas such as file compression, telecommunications, and data storage.2. LZ Compression TechniquesLZ compression revolves around identifying repetitive patterns in data and replacing them with shorter representations. This section will explore some of the common LZ compression techniques employed in various applications.2.1 Dictionary-Based CompressionOne of the fundamental LZ compression techniques is dictionary-based compression. This approach creates a dictionary or reference table that stores frequently occurring patterns in the input data. Subsequently, these patternsare replaced with shorter references, thereby reducing the overall size of the compressed data.2.2 Sliding Window CompressionSliding window compression is another popular LZ technique. This method involves maintaining a sliding window over the input data, searching for repetitions within the window. The compressed data would then consist of references to previous occurrences of the repeated patterns within the window.3. LZ Application in File CompressionFile compression is a common application of LZ algorithms, with the most notable example being the popular ZIP file format. LZ-based compression algorithms excel at reducing the size of large files, making them easier to store, transmit, and manage.4. LZ in TelecommunicationsAnother domain where LZ algorithms find extensive use is in telecommunications. In scenarios where bandwidth is limited, such as mobile networks, compressing data using LZ techniques can significantly improve transmission efficiency. This translates to faster data transfer rates and more efficient network utilization.5. LZ in Data StorageIn the realm of data storage, LZ compression plays a crucial role in optimizing disk space utilization. By compressing files and data beforestoring them, organizations can significantly enhance storage capacities, leading to cost savings and improved efficiency.6. LZ LimitationsWhile LZ compression techniques provide impressive compression ratios and versatile applications, it's important to acknowledge their limitations. Some of these limitations include increased computational overhead for compression and decompression processes, as well as the inability to achieve compression for certain types of data that lack repetitive patterns.ConclusionIn conclusion, this LZ starter kit has provided you with an overview of LZ, its compression techniques, and its applications in file compression, telecommunications, and data storage. By understanding LZ and its potential, you unlock a world of possibilities for optimizing data management, storage, and transmission. Embrace the power of LZ and elevate your skills to new heights!。

基于双域学习的jpeg压缩图像去压缩效应算法

０引言图像作为主要的信息载体之一，因其具有直观
生动的特征，在人类的生产和生活中有着极为重要的地位。现阶段，随着图像信息量呈爆炸式增长，通常都会对图像进行一定倍数的压缩以节省存储空间和带宽资源。ＪＰＥＧ压缩由于其压缩率高、快速有效等优点，已成为目前最常用的图像压缩方法之一。ＪＰＥＧ压缩是对图像进行分块后，量化其ＤＣＴ系数，从而减少图像高频分量，降存在压缩伪影，
智能算法ＩｎｔｅｌｌｉｇｅｎｔＡｌｇｏｒｉｔｈｍ
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王新欢，任超，何小海，王正勇，李兴龙
（四川大学电子信息学院，四川成都６１００６５）
摘要：针对ＪＰＥＧ压缩图像存在的压缩伪影，提出了一种基于双域学习的ＪＰＥＧ压缩图像去压缩效应算法，以使压缩图像达到更好的视觉效果。该算法利用深度卷积神经网络，根据ＪＰＥＧ压缩图像的特点，分别在像素域和ＤＣＴ变换域对压缩图像进行去噪，最后将双域的学习信息进行有效融合，以达到更好的去块效应效果。所提出的卷积神经网络使用宽激活残差块（ＷｉｄｅａｃｔｉｖａｔｉｏｎＲｅｓｉｄｕａｌＢｌｏｃｋ，ＷＡＲＢ）作为结构单元，能在有效提升网络预测性能的同时，不引入更多的网络参数和计算量。实验结果表明，相比于目前先进的去压缩效应算法，所提出的ＪＰＥＧ压缩图像去压缩效应算法能在主客观上均获得更好的性能。关键词：ＪＰＥＧ压缩；去压缩效应；深度学习；宽激活残差结构中图分类号：ＴＮ９１９．８１文献标识码：ＡＤＯＩ：１０．１９３５８／ｊ．ｉｓｓｎ．２０９６５１３３．２０１９．１２．００９引用格式：王新欢，任超，何小海，等．基于双域学习的ＪＰＥＧ压缩图像去压缩效应算法［Ｊ］．信息技术与网络安全，２０１９，３８（１２）：４２４７，５７．

JPEG使用的几种压缩模式一览

JPEG(Joint Photographic Experts Group)是ISO和CCITT为静态图象建立的一个国际数字图象压缩标准.定义了基于DCT的失真(Lossy)方式和使用预测器(Predictor)的无失真(Lossless)方式.在失真方式中, 又分只处理取样比例为8位的基本模式(BaseLine Process)和可以处理取样比例12位的扩展模式(Extended Process). 使用失真方式,压缩比可调(一般在10-50内效果最好);使用无失真方式,压缩比大于2.JPEG使用的几种压缩模式一览:基于DCT的失真方式DCT_Based基本系统Baseline顺序编码Sequential哈夫曼编码Huffman累进编码Progressie哈夫曼编码Huffman扩展系统Extended顺序编码Sequential哈夫曼编码Huffman算术编码Arithmetic累进编码Progressie哈夫曼编码Huffman算术编码Arithmetic阶梯编码Hierachical哈夫曼编码Huffman算术编码Arithmetic无失真编码Lossless一般编码Normal哈夫曼编码Huffman算术编码Arithmetic阶梯编码Hierachical哈夫曼编码Huffman算术编码Arithmetic我们现在讨论Baseline系统中的顺序编码模式.JPEG的编码主要有以下几个步骤:1.色彩转换2.部分取样3.离散余弦变换DCT4.量化5.熵编码6.数据混合编码的时候,JPEG编码器先将一幅原始图象转换为自己的色彩系统,按照人眼特点对其中各个色彩分量作不同的取样,经由DCT转换从时域变为频域,接着将变换后的数据量化以丢弃无用信息,然后用哈夫曼编码或者算术编码对量化后的系数进行编码得到压缩数据,最后将色彩分量信息,量化表,编码表和各个色彩分量的压缩数据等混合成一个整体数据流,即形成JPEG文件.解码的时候,JPEG解码器先从数据流中获取解码所必须的信息(色彩分量信息,量化表和编码表等),然后将各个色彩分量分别解码,过程和编码的时候刚好相反.编码和解码的流程图如下:色彩系统(Color Space) 返回JPEG编解码流程计算机显示使用的是RGB三色系统,而JPEG文件使用的是亮度-色调-饱和度色彩系统,本节讲的就是它们之间的转换.(为何不直接使用RGB色彩系统,在下一节将会讲到).间接色彩<--->直接色彩1.YIQ:YIQ是北美NTSC电视系统中采用的色彩系统,Y不是指Yellow,而是指颜色的明视度(Lumin ance),或者称做亮度(Brightness),也可以称做灰度值(Gray Value),I和Q分别是色调和饱和度.YIQ与RGB的转换关系如下:Y=0.299R+0.587G+0.114B R=Y+0.956I+0.621QI=0.596R-0.274G-0.322B G=Y-0.272I-0.647QQ=0.211R-0.523G+0.312B B=Y-1.106I-1.703Q2.YUV:YUV是欧洲PAL电视系统中采用的色彩系统,YUV的含义和YIQ的含义一一对应YUV与RGB的转换关系如下:Y=0.299R+0.587G+0.114B R=Y+1.140VU=-0.148R-0.289G+0.473B G=Y-0.395U-0.581VV=0.615R-0.515G-0.100B B=Y+2.032U3.YCbCr:JPEG的缺省色彩系统,它是从YUV色彩系统中衍生出来的,将U和V做少许调整就是Cb和Cr YCbCr与RGB的转换关系如下:Y =0.2990R+0.5870G+0.1140B R=Y+1.40200(Cr-128)Cb=-0.1687R-0.3313G+0.5000B+128 G=Y-0.34414(Cb-128)-0.71414(Cr-128)Cr=0.5000R-0.4187G-0.0813B+128 B=Y+1.77200(Cb-128)下图表明该类色彩系统与RGB系统的对应关系: 色调饱和度亮度部分取样(SubSampling) 返回JPEG编解码流程研究表明:人类眼睛对亮度变化的敏感度比对色彩变化的敏感度要高的多.例如在光线不足的情况下,人眼看到的物体都是黑白的,只有光线足够强的时候,才能感觉到色彩的存在.从上一节的叙述我们知道,间接色彩可以用亮度,色调和饱和度来表示颜色.如果我们对于亮度处理比较精细,而对色调和饱和度只做粗略的处理,那么就可以提高压缩比而不会太影响视觉效果.这也是不用RGB色彩系统的原因.不过也不是绝对,JPEG还支持一种色彩系统CMYK属于直接色彩,那就不在我们现在讨论的范围里了.JPEG使用部分数据取样来完成这个过程.用取样因子描述.用一个例子来说明:如果一幅图,它的水平取样因子为2,1,1.则表示在水平方向上亮度、色调和饱和度的取样数据量比例为2:1:1.垂直取样因子为2,1,2.则表示再垂直方向上亮度、色调和饱和度的取样数据量比例为2:1:2.那么总的取样数据量比例为2*2:1*1:1*2 = 4:2:1.这个比例被称为YUV421.JPEG规定每种色彩成分取8*8个样值(为何要取8*8见下一节)为一个单位(Unit).按取样比例的几个色彩成分的单位组合,称为最小编码单位MCU(Minimum Coded Unit)下图表示一个16*16的图象块是怎样按YUV412被取样为一个MCU的:在上图中:原图象数据(16*16RGB矩阵)首先被转化为三个色彩成分数据(16*16YUV矩阵),然后对于Y成分(Component1),数据不变. 对于U成分(Component2),每2*2个数据求平均,取样为一个数据.对于V成分,每2*1个数据求平均,取样为一个数据,最后得到2*2个Y单位,1*1个U单位,1*2个V单位.这4+1+2个单位就是最小编码单位(MCU)通过部分取样可以看出,原来3*16*16=768个像素数据,变为16*16+8*8+8*16=448个,再未编码前就有约40%的压缩比.如果用YUV411则有50%的压缩比,用YUV422也有33%.理论上讲,如果取YUV911,YUV16 11可以达到更高的压缩比,但是这样图象品质就会受到影响,因此JPEG规定一个MCU里Unit的个数不能大于10离散余弦变换--DCT(Discrete Consine Transform) 返回JPEG编解码流程研究表明:人眼对低频数据比对高频数据有更高的敏感度.人们很早就使用这一特点,例如报纸上印刷照片是由很多黑色小圆点组成,这些小圆点代表高频数据,人眼看上去是一幅图象,即低频数据.如果我们对图象的高频数据作些修饰(将小圆点变为小方点),人眼是不容易辨认的(看上去还是原来的图象).一般图象(照片)有很大部分信息熵都在高频区.DCT的作用即是将一组光强数据(Intensity Data)转换为频率数据(Frequency Data),以便以后流程对高频数据进行修饰处理.由于DCT变换的运算量比较大,JPEG将每一个色彩分量的数据分割成8*8的小块,然后对这个8*8矩阵做DCT原始图象数据Ixy: 变换后DCT系数Duv:I00 I01 I02 I03 I04 I05 I06 I07I10 I11 I12 I13 I14 I15 I16 I17I20 I21 I22 I23 I24 I25 I26 I27I30 I31 I32 I33 I34 I35 I36 I37I40 I41 I42 I43 I44 I45 I46 I47I50 I51 I52 I53 I54 I55 I56 I57I60 I61 I62 I63 I64 I65 I66 I67I70 I71 I72 I73 I74 I75 I76 I77--FDCT-><-IDCT-- D00 D01 D02 D03 D04 D05 D06 D07D10 D11 D12 D13 D14 D15 D16 D17D20 D21 D22 D23 D24 D25 D26 D27D30 D31 D32 D33 D34 D35 D36 D37D40 D41 D42 D43 D44 D45 D46 D47D50 D51 D52 D53 D54 D55 D56 D57D60 D61 D62 D63 D64 D65 D66 D67D70 D71 D72 D73 D74 D75 D76 D77算法如下:其中u,v=0时Cu,Cv=0.70710678(即1/√2) ; u,v为其他值时Cu,Cv=1.变换后的系数D00 JPEG称为直流系数(DC),D01-D77称为交流系数(AC).系数矩阵左上为低频系数,右下为高频系数.用浮点运算可以达到很高的精度,不过常用的还是整数快速算法量化(Quantization) 返回JPEG编解码流程DCT变换后的高频数据由量化来进行修饰处理.将DCT系数按比例缩小,并取最接近的整数值的过程称为量化.将量化后的系数按比例恢复的过程称为逆量化.下为示意图:通过量化,我们可以丢弃一些不重要的图象细节信息,JPEG的主要压缩率集中在这个地方.量化系数是在图象品质与压缩比之间作一个选择.量化间隔大,丢弃的信息就多,使得压缩比增大,图象品质降低.反之压缩比减小,图象品质提高.所以选择一个好的量化系数很重要,JPEG提供的缺省量化表如下:明视度(Luminance)量化表: 色调(Chrominance)量化表:16 11 10 16 24 40 51 6112 12 14 19 26 58 60 5514 13 16 24 40 57 69 5614 17 22 29 51 87 80 6218 22 37 56 68 109 103 7724 35 55 64 81 104 113 9249 64 78 87 103 121 120 10172 92 95 98 112 100 103 9917 18 24 47 99 99 99 9918 21 26 66 99 99 99 9924 26 56 99 99 99 99 9947 66 99 99 99 99 99 9999 99 99 99 99 99 99 9999 99 99 99 99 99 99 9999 99 99 99 99 99 99 9999 99 99 99 99 99 99 99量化系数矩阵与8*8的DCT系数矩阵一一对应.运算的时候直接用DCT系数除以相对应的量化系数,对得到的数四舍五入取整.可以看出低频区(左上角)的量化系数明显比高频区(右下角)的量化系数小.用该量化表压缩的图象,统计平均人眼观看质量为4.5分(5分为满分),压缩比约为20至30熵编码(Entropy Encoding) 返回JPEG编解码流程原始图象数据经过上面的步骤,其数据量已经被压缩了很多,熵编码使用统计模型将系数作最后的压缩.主要有两种方式的编码:Huffman编码和算术编码.由于专利权的缘故,大多数编码器都采用Huffman编码,但是使用算术编码压缩效果要好5%至10%.在对量化后的系数矩阵进行编码之前,还有一些预处理要做:1.Z形排序(Zig-Zag Order):Z形编码入图所示:从图中可以看出,JPEG优先对低频区的数据编码.这样做是因为:低频的系数比高频的系数重要的多;而且低频区的量化系数大大小于高频区的量化系数,使得量化后高频区大部分数据为0,从低频区开始编码可以用一个结束码(EOB,End Of Block)表示以后的高频数据全零.在后面的例子中,我们可以看出这样做的好处.2.差值脉冲编码调制(Differential Pulse Code Modulation)DPCM:JPEG对DC系数的编码采取DPCM方式,也就是取该DC系数与前一个DC系数的差值来编码. 在DCT变换公式中可以看出,DC系数实际上是对64个原始系数求和平均(D00=[∑Ixy]/8).在连续色调图象中,色彩变化不是很剧烈,差值一般比原值小,所以对差值编码所需的数据量会比原值编码小很多.在数据混合(Data Interleave) 返回JPEG编解码流程熵编码之后的数据经过混合成为编码比特流(Coding Bit Stream)一个例子(An Example) 返回JPEG编解码流程现在我们对一个高斯脉冲的亮度分量用上面所述流程进行编码,首先做FDCT和量化,过程如下:高斯脉冲数据: 变换后系数:139 144 149 153 155 155 155 155 144 151 153 156 159 156 156 156 150 155 160 163 158 156 156 156 159 161 162 160 160 159 159 159 159 160 161 162 162 155 155 155 161 161 161 161 160 157 157 157 162 162 161 163 162 157 157 157 162 162 161 161 163 158 158 158 -128--->FDCT 235 -1 -12 -5 2 -1 -2 1-22 -17 -6 -3 -2 0 0 -1-10 -9 -1 1 0 0 0 0-7 -1 0 1 0 0 0 00 0 1 1 0 0 0 11 0 1 0 0 1 1 -1-1 0 0 1 0 1 1 0-2 1 -3 -1 1 1 0 0量化表: 量化↓16 11 10 16 24 40 51 6112 12 14 19 26 58 60 5514 13 16 24 40 57 69 5614 17 22 29 51 87 80 6218 22 37 56 68 109 103 7724 35 55 64 81 104 113 9249 64 78 87 103 121 120 10172 92 95 98 112 100 103 99--> 15 0 -1 0 0 0 0 0-2 -1 0 0 0 0 0 0-1 -1 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 0我们假设上一个DCT的DC系数为20,则经过预处理(ZigZag,DPCM)后,系数排列为-5,0,-2, -1,-1,-1,0,0,-1,在这个地方,可以看出后面的系数全部为零,JPEG定义了一个块结束标记(EOB)来表示后面的数据全是零.重建过程如下：量化表: 解码后系数:16 11 10 16 24 40 51 6112 12 14 19 26 58 60 5514 13 16 24 40 57 69 5614 17 22 29 51 87 80 6218 22 37 56 68 109 103 7724 35 55 64 81 104 113 9249 64 78 87 103 121 120 10172 92 95 98 112 100 103 99---> 15 0 -1 0 0 0 0 0-2 -1 0 0 0 0 0 0-1 -1 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 0重建图象数据: 反量化↓144 146 149 152 154 156 156 156148 150 152 154 156 156 156 156155 156 157 158 158 157 156 155 160 161 161 162 161 159 157 155 163 163 164 163 162 160 158 156 163 164 164 164 162 160 158 157 160 161 162 162 162 161 159 158 158 159 161 161 162 161 159 158 IDCT<---+128 240 0 -10 0 0 0 0 0-24 -12 0 0 0 0 0 0-14 -13 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 00 0 0 0 0 0 0 0。

电信专业专业英语总结

TEXT 1 Digital Representation of Information a sequence of bits 比特序列bandwidth带宽bit rate位率carrying bits携带比特chaining链表coding distortion编码失真communication network通信网络compact disks=CDcompression algorithms 压缩算法data structures数据结构digital data storage device 数据存储设备digital signals数字信号digital-to-analog数模转换error detection and correction错误检查与纠错fidelity保真度integrated services digital network 集成服务数字网络massive digital storage媒体海量存储monodedia单媒体motion video移动图像multimedia digital information多媒体数字信息pattern recognition模式识别perceived distortion察觉失真quantizing量化retrieval检索samping rate采样率time-dependency时间可靠性to a lesser extent在一个很小范围内TEXT 2 Expectations and Moments bivariate双边converse逆命题correlation coefficient相关系数covariance协方差density function密度函数diagonal matrix对角矩阵direct expansion and integration直接展开积分discrete离散entries主对角元expections 期望值first moment一阶矩fundamental theorem of expectation期望值基本定理Gaussian高斯型integral operation积分算子interval区间invoke引用joint density联合密度jointly Gaussian联合高斯jointly normal联合正态分布level contours等值线marginal density边缘密度mean-square value均方值probability density function概率密度函数probability mass function概率质量函数product乘积quadratic二次random variables随机变量row vector行向量scalar-valued function标量值函数scatter色散second moment二阶矩standard deviation标准偏差summation求和symmetric对称the center of mass质心the moment of inertial惯性矩the probability-weighted average概率加权平均值unit length单位长度variance方差zero-mean 0均值TEXT 3 Object-oriented Design access facility 存储机制communication facility通信设备Complex structures 复合结构computer programming 计算机程序设计concurrent access并行存储conventional record-oriented传统的面向记录的corruption 损坏database 数据库Dynamic entity 动态实体encapsulation 封装Hierarchical structure 分层次结构interconnect 链接interface 界面，接口 n.invoke 行使Mechanism of inheritance 继承（传承）机制modem调制解调器network-management网络管理Object classes 对象类Override 重载Physical entity 有形实体，实体Polymorphism 多态性Reentrant 可重入程序semiconductor半导体Subclass 子类Superclass 父类，超类Tree topology 树形布局Virtual circuit 虚拟电路TEXT 4 Multimedia Information and Systems analog signal模拟信号Computer animation计算机动画computer display 电脑显示器digital storage volume数字存储容量discrete media离散媒体human—computer interfaces (HCIS) 人机界面接口modern computer-assisted on-screen presentations计算机辅助屏幕显示技术optical character regnition 光学字符识别physically audible waveform可听波形pixel像素TEXT 5 Basic Ideas of the Word Wide Web administrative管理的，行政的browse浏览client system 用户系统Hyperlinked 超链接Maintenance 维护mechanism机械装置；机制，机理；办法，途径remote servers 远程服务器The world wide web 万维网transaction交易，业务visible anchors 可见锚TEXT 6 Virtual Reality3-D auditory displays三维听觉显示器data-gloves 数据手套dimension尺寸head-mounted displays头盔显示器（HMDS）Humancomputer interface 人机界面interactive communication 交互通信Latency潜伏Multiuser多用户的Particle 粒子Remote exploration远征探索Simulators 模拟器Spatial立体空间的Telepresence surgery远程监控手术the latency of the transport network 传输网络等待The virtual meeting room虚拟会议室Tractors 索引机transport network 传输网络virtual reality 虚拟现实TEXT 7 The TCP/IP Protocal SuiteAugment 增加，增大Datagram 数据包，资料包Destination subnetwork address 目的地子网地址end system 终端系统Entity 实体FTP：file transfer protocol 文件传输协议Host-to-host 主机对主机IP internet protocol 网络协议LANs local area networks 局域网Network acessheader 网络访问头network-access-protocol(NAP) 网络访问协议Packet-switched network 交换网络Router 路由器Standardized computer-communication protocols 标准化的计算机通信协议TEXT 9 Business-to-Business E-commerce application service provider(ASP) 应用服务提供商brandcommoditization 品牌商品化consortium 财团；联合；合伙customer service: 客户服务e-commerce 电子商务in a vacuum脱离现实marathon 马拉松赛跑；耐力的考验mentoring 辅导制，辅导Paradigm 范例pool 合伙经营, 共享profound 深远的proposition 价值主张public-private partnerships公私伙伴关系Return-on-investment 投资回报率rivileged 有特权的，专享的Topological 拓扑的ubiquitous 普遍的unprecedented 空前的，无前例的value proposition 价值主张Vulnerable 脆弱的TEXT 15 Bluetooth Technology asynchronous 异步authentication认证baseband protocol 基带协议Bluetooth Technology蓝牙技术Cable 电缆Cellular phones: 蜂窝电话，手机，移动电话；eavesdrop 偷听encryption 加密frequency band频带宽度Hook up 连接Infrared port 红外线接口link configuration连接配置link manager protocol连接管理协议logical link control and adaptation protocol 逻辑链路控制适配协议Piconet 微微网RF(Radio Frequency) 射频search engine 搜索引擎small form factor小型化The Bluetooth Special Interest Group蓝牙技术联盟unlicensed ISM band无许可的国际安全管理频段worldwide acceptance全球通行TEXT 16 Introduction to 3GThird Generation 第三代移动通信技术CDMA 分码多重进接，码分多址（Code Division Multiple Access）NMT 北欧移动电话（Nordic Mobile Telephone）TACS 全接入通信系统（Total Access Communication System）GSM 全球移动通信系统（Global System for Mobile Communications）TDMA 分时多址（Time Division Multiple Address）GPRS 通用分组无线业务（General Packet Radio Service）EDGE改进数据率GSM服务（Enhanced Data rate for GSM Evolution）circuit switched counterparts 电路交换同行packet—based data standards 基于分组的数据标准Frame Relay 帧中继ATM (Asynchronous Transfer Mode) 异步传输模式CDPD ( Cellular Digital Packet Data) 蜂窝数字分组数据PDCP (Personal Digital Cellular Packet) 个人数字蜂窝分组packet radio data network standard 分组无线数据网络标准Intranet内联网extranet外联网Megabit 兆位。

jpeg解压算法原理

jpeg解压算法原理
JPEG（联合图像专家组）压缩算法是一种基于离散余弦变换（DCT）的有损压缩算法。

它利用人眼对图像质量的敏感性和图像中冗余的特性，将原始图像转换成一系列的8×8像素块，在每个像素块上进行频域变换（DCT），并对系数进行量化。

JPEG算法的解压过程主要包括如下步骤：
1. 读取压缩后的JPEG文件，获取图像的高度和宽度信息。

2. 根据压缩文件的数据格式读取所有压缩系数数据。

3. 将所读取的码流数据逆量化并使用离散余弦变换（IDCT）反变换为图像上的8×8像素块。

4. 对于每个8×8的像素块，将它们在图像上组合排列，并进行像素插值处理，得到一张完整的图像。

总体来说，JPEG解压算法的过程就是先将压缩文件中的数据反量化和反变换，再对像素进行插值和组合排列得到原始图像。

遥感图像客观质量评价方法研究

遥感图像客观质量评价方法研究何中翔;王明富;杨世洪;吴钦章【摘要】为分析图像压缩过程对遥感图像质量的影响,从遥感图像构像质量和几何质量两个方面探讨了遥感图像客观质量评价方法,并改进一种基于Harris角点检测算法的亚像素级角点检测算法.采用JPEG2000、SPIHT两种算法分别对这些评价方法进行验证.实验表明:为满足人眼视觉观察能分辨图像细节的要求,遥感图像压缩比不宜超过16倍,为使压缩后图像满足计算机视觉应用,图像压缩比不宜超过8倍.%To study the quality effect of the remote sensing image brought by the image compression process, several algorithms of objective quality assessment about the visual and geometric quality of the remote sensing image are discussed. A sub-pixel level corner detection algorithm based on the Harria corner detection algorithm is improved. Two compression algorithms of JPEG2000 and SPIHT are used to verify these assessment algorithms. The experimental results show that the image compression rate can be no larger than 16 if you want to distinguish the details by your eyes, and it can be no larger than 8 if you need satisfactory precision in computer vision applications.【期刊名称】《图学学报》【年(卷),期】2011(032)006【总页数】6页(P47-52)【关键词】图像压缩;图像质量评价;角点检测算法;压缩比【作者】何中翔;王明富;杨世洪;吴钦章【作者单位】中国科学院光电技术研究所,四川成都610209;中国科学院研究生院,北京100039;中国科学院光电技术研究所,四川成都610209;中国科学院研究生院,北京100039;中国科学院光电技术研究所,四川成都610209;中国科学院光电技术研究所,四川成都610209【正文语种】中文【中图分类】TP391航空遥感图像数据量巨大，为在有限的传输信道带宽下尽量保持遥感图像信息，必须对图像进行压缩减少数据量。

unexpected jpeg encoding quality levels 0 -回复

unexpected jpeg encoding quality levels 0 -回复JPEG（Joint Photographic Experts Group）是一种常见的图像压缩编码标准。

JPEG编码质量等级是影响图像质量和文件大小的重要参数之一。

在这篇文章中，我们将详细讨论JPEG编码质量级别为0（Quality Level 0）的意义、影响和用途。

JPEG编码等级从0到100，其中0表示最低的图像编码质量，而100表示最高的图像编码质量。

在JPEG编码质量级别为0的情况下，图像压缩比将达到最高水平。

JPEG编码质量级别为0所导致的主要问题是信号丢失。

这意味着在图像压缩过程中，某些图像细节和数据将被移除，从而导致图像质量的显著降低。

这种数据丢失是由于JPEG编码的特性决定的。

在JPEG编码中，DCT（Discrete Cosine Transform）和量化是两个主要的步骤。

DCT将图像转换为频域数据，然后使用量化表来缩减频谱中的高频部分。

在JPEG编码质量级别为0的情况下，量化表中的数值将被调整为最大程度地移除高频细节，从而导致图像质量下降。

图像质量的损失主要表现在细节模糊、边缘伪影以及色块状失真等方面。

尤其是对于高对比度、细节丰富的图像，JPEG编码质量级别为0会对其造成显著的破坏。

因此，在需要高质量图像的情况下，不建议使用JPEG 编码质量级别为0。

然而，JPEG编码质量级别为0在某些特定情况下仍然具有一定的应用价值。

首先，它可以用于在带宽受限的网络环境中进行快速图像传输。

由于JPEG编码质量级别为0的图像文件较小，传输速度更快，适用于需要快速加载图像的场景。

此外，JPEG编码质量级别为0可以用于一些不需要高质量图像的应用，例如监控摄像头的实时视频流。

在这种情况下，图像的实时性和流畅性更重要，而不是图像的细节和质量。

在图像处理和图像编辑领域中，JPEG编码质量级别为0可以作为图像预处理的步骤之一。

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1COMP 249 Advanced Distributed SystemsMultimedia Networking/~jeffay/courses/comp249f99Video Compression StandardsKevin JeffayDepartment of Computer ScienceUniversity of North Carolina at Chapel Hilljeffay@ September 14, 1999The Video Data TypeCompression StandardsuBasic compression techniques»Truncation, CLUT, run-length coding »sub-sampling & interpolation »DPCM »DCT»Huffman codinguCommon algorithms»JPEG/MJPEG »H.261/H.263»MPEG-1,-23Compression AlgorithmsJPEGuA still image (“continuous tone”) compression standard»DCT-basedu4 Modes of compression»sequential — image components coded in order scannedvBaseline — “default compression”»progressive — image coded in multiple passes so partial images can be displayed during decoding »lossless — guaranteed no loss»hierarchical — image encoded at multiple resolutionsuTypical results»24:1 compression (1 bpp)JPEG CompressionCoding AC coefficientsu Each coefficient encoded as a variable length “pair”»([run length, size], amplitude)u First element coded using a variable-length Huffman code »A coding table (“code book”) must be providedv Can be generated on-the-fly with an additional pass over the coefficientsv Up to four code books per image may be specifiedv The codebook becomes part of the coded bit-streamu Second element coded as a variable length integer»whose length is specified in the previous “symbol”JPEG CompressionExamples of quality v. bpp4.4 bpp 1.4 bpp0.9 bppExamples of quality v. bpp4.4 bpp0.7 bpp13 JPEG CompressionExamples of quality v. bpp0.7 bpp0.5 bpp15uLossy compression modes»sequential — image components coded in order scannedvDefault mode»progressive — image coded in multiple passes so partial images can be displayed during decodingvUseful for transmission of images over slow communications links »hierarchical — image encoded at multiple resolutionsvUseful for images that will be displayed on heterogeneous displaysuLossless mode»Guaranteed lossless»Uses DPCM encoding rather than DCT23Motion JPEGApplying JPEG to moving imagesuVideo can be (trivially) encoded as a sequence of stills»This practice is routine in the digital video editing worlduThe issue is how to encode and transmit “side information”»Quantization tables, Huffman code-book may/may not change between framesThe Video Data TypeCompression StandardsuBasic compression techniques»Truncation, CLUT, run-length coding »sub-sampling & interpolation »DPCM »DCT»Huffman codinguCommon algorithms»JPEG/MJPEG »H.261/H.263»MPEG-1,-225Compression AlgorithmsH.261 (p x 64)uA telecommunications (ITU) standard for audio & video transmission over digital phone lines (ISDN)uH.261 primarily intended for interactive video applications»Design of the standard driven by a 150 ms maximum encoding/decoding delay goaluA scalable coding architecture capable of generating bit streams from 64 kbps (“1x 64”) to 1,920 kbps (“30x 64”)in 64 kbps increments»p = 1, 2 produces a low res “videophone”(Common use is for ISDN BRI — 112 kbps video, 16 kbps audio)»p ≥ 6 produces an acceptable videoconference and allows multipoint communicationITU H.320 Teleconferencing Standards Teleconferencing over ISDNu H.261 — Video communications at p x64 kbpsu H.221 —Syntax for multiplexing audio and videopacketsu H.230 — Protocol for call setup and negotiation ofend-system (“terminal”) capabilitiesu H.242 — Conference control protocolu G.711 —ISDN audio coding standard at 64 kbpsu G.722 — High-quality audio at 64 kbpsu G.728 — Reduced quality speech at 16 kbps37H.263 Video CompressionLow-bitrate video compression for data networksu Based on H.261 (& MPEG-1, -2)uIncludes new image formats:Image Size Format Maximum Number of coded bits/picture128 x 96176 x 144352 x 288704 x 5761,408 x 1,152sub-QCIF QCIF CIF 4CIF 16CIF64642565121024uAdded coding efficiency from:»Unrestricted motion vectors»Bi-directional motion estimation/prediction »Arithmetic coding of AC coefficientsH.263 Video CompressionCompanion standardsu H.263 —“Low bit-rate” video coding u H.324 —Terminal systems u H.245 —Conference controlu H.223 —Audio/video multiplexing uG.723 —Audio coding 5.3 and 6.3 kbpsuFor Internet conferencing there is also the related T.120 Document Conferencing standards family39The Video Data TypeCompression StandardsuBasic compression techniques»Truncation, CLUT, run-length coding »sub-sampling & interpolation »DPCM »DCT»Huffman codinguCommon algorithms»JPEG/MJPEG »H.261/H.263»MPEG-1,-2Compression AlgorithmsMPEGuA family of audio/video coding schemes»MPEG-1 —A video coding standard for digital storage/retrieval devicesv“VHS quality” video coded at approximately 1.5 Mbps »MPEG-2 — Video coding for digital televisionvSIF/CIF to HDTV resolutions at data rates up to 100 Mbps»MPEG-4 — Coding of audio/visual “objects” for multimedia applicationsv Coding of natural & synthetic imagesvObject-based encoding for content access & manipulation»MPEG-7 — A content/meta-data representation standard for content search and retrieval41RequirementsuMPEG intended primarily for stored video applications»A “generic” standard»But a basic assumption is that video will be coded once and played multiple timesuSupport for VCR-like operations»Fast forward/forward scan »Rewind/reverse scan »Direct random access »...51MPEG Video CompressionCoded bit-streamu MPEG has a layered bit-stream similar to H.261uThere are seven layers:»Sequence Layervdecoding parameters (bit-rate, buffer size, picture resolution, frame rate, ...)»Group of Pictures Layerv a random access point»Picture Layer v picture type and reference picture information»Slice Layerv position and state information for decoder resynchronization »Macroblock Layer v coded motion vectors»Block Layervcoded DCT coefficients, quantizer step size, etc.MPEG-2“New & Improved” MPEG-1uA coding standard for the broadcast industry»Coding for video that originates from cameras»Offers little benefit for material originally recorded on filmuBut included is support for:»Higher (chrominance) sampling rates »Resilience to transmission errors »...uMore mature and powerful coding/compression technology is used»Unrestricted motion search with 1/2 pel resolution for motion vectors。