外文文獻(xiàn)—數(shù)字圖像壓縮技術(shù)介紹.doc

附錄附錄idigital image compressiondigital image compression, also known as image compression or image coding is divided into still image compression and motion image compression (video compression). there is a high degree of correlation in the image data, an image of internal and video images between a lot of redundant information. redundant information including the following five: (1) time redundancy: the difference between adjacent frames of the image sequence is very small, this time redundancy is called temporal redundancy. (2) spatial redundancy: an image internal uniform coloring part, or the images within the regular pattern, this space-related redundancy is known as spatial redundancy. (3) structural redundancy: in strong texture, or between the various parts of the image there is a certain relationship, such as self-similarity in the part of the image area memory. this redundancy is called structural redundancy. (4) the redundancy of knowledge: the information contained in the image and some basic knowledge of a priori, such as in the general face images, the mutual position of the head, eyes, nose and mouth is some common sense. this redundancy is called knowledge redundancy. (5) visual redundancy: in most cases, the ultimate recipients of the reconstructed image is the human eye. in order to achieve higher compression ratio, you can use the characteristics of the human visual system. for example, the human eye, the ability to distinguish different colors, the sensitivity of different directions. therefore, if the encoding scheme is the use of some of the features of the human visual system, can further improve the compression ratio and image of the so-called subjective quality. image coding is possible to remove redundant information of the various forms in order to reduce the number of bits representing the image required commonly used in image compression methods are the following:1, the run length encoding (rle) length encoding (run-length encoding) is one of the easiest way to compress a file. its approach is a series of duplicate values (for example, the gray values of image pixels) with a single value plus a count value to replace. for example, there is such a letter sequence aabbbccccccccdddddd the stroke length encoding is 2a3b8c6d. this method is very easy to implement, but also for string compression with long repeated values。the coding is very effective. for example, there are large areas of continuous shadow or the image of the same color, using this method pressure。reduction effect of a good. many bitmap file formats with a run length encoding, such as tiff, pcx, gem.2, the lzw coding this is the abbreviation of the name of three inventors (the lempel, ziv, welch), its principle is that each one byte the value should be paired with the value of the next byte is a character, and set a code for each character. when the same kind of a character on the re-emergence of code instead of this character pair, then this code and the next character matching. lzw coding principle is an important feature, the code is not only able to replace a bunch of the same value of the data, but also be able to replace.a bunch of different data values. if some of the different data values in the image data is often repeated, can also be found a code to replace the data string. in this regard, the lzw compression principle is better than rle.3, huffman coding huffman coding (huffman encoding) instead of the original data is not fixed length coding to achieve. huffman coding was first established, in order to compress the text file and so far has been a lot of change body. its basic idea is the frequency the higher the value, the shorter the length of its corresponding coding, on the contrary the frequency of the more low values, the corresponding coding length. huffman coding rarely achieve 8:1 compression ratio, in addition, it also has the following two problems: the it must be refined indeed the statistics of the frequency of occurrence of each value in the original document, if not this precise statistics, the effect of compression on will be greatly reduced, or even less than the compression effect. huffman coding is usually to go through twice the operation, the first over the statistics, the second time the code, the encoding process is relatively slow. in addition, due to various length,encoded in the decoding process is relatively complex, so the extraction process is relatively slow. it is more sensitive. huffman coding all together regardless of byte sub, so increase plus one, or reduce one will make the decoding results beyond recognition.4, prediction and interpolation coding usually in the local region in the image pixels are highly correlated, so using the previous pixel gray expected degree of knowledge of the current pixel gray, which is forecast. the so-called interpolation is based on previous and pixel gray-scale knowledge to infer the current pixel grayscale. if the prediction and interpolation is correct,do not have to compress each pixel gray, but the difference between the predicted value and the actual pixel values after entropy coded and sent to the receiving end. predictive value and the difference signal to reconstruct the original pixel in the receiving end.predictive coding can be obtained relatively high coding quality, and relatively simple to achieve, which is widely used in image compression coding system. but its compression ratio is not high, and accurate prediction depends on the image special.of a priori knowledge, and must make a large number of non-linear operation, it is generally not used alone, but used in combination with other methods. such as predictive coding in jpeg dct dc coefficient the encoding of the exchange coefficient is used to quantify the + rle + huffman coding.5, vector quantization coding vector quantization encoding the high correlation between adjacent image data, the input image data sequence grouping,each set of m data constitute an m-dimensional vector, is encoded together, that is, to quantify more than once. according to the shannon rate, distortion theory for memoryless sources, the vector quantization coding is always better than scalar quantization coding.before coding, first by the large number of samples of the training or learning, or self-organizing feature map neural network, get a series of standard image mode, each image pattern is called a codeword or code vector, these codewords or code vectors together.together are called the codebook, the codebook is actually a database. the input image block in accordance with a certain way to form an input vector. encoding this input vector and all codewords of the code book to calculate the distance to find the nearest codeword, that is to find the best matching image block. the output index (address) as the encoding results. decoding process is the opposite. according to the coding results of the index from the code book to find the index corresponding to code word (the code book and codingcodebook), constitute the decoding result. therefore, vector quantization coding is a lossy codec. at present the use of more,the multi-vector quantization coding scheme is a random vector quantization, the transform domain vector quantization, finite state vector quantization, the address vector quantization waveform gain vector quantization, classified vector quantization, and prediction vector quantization.6, transform coding transform coding image intensity matrix (time-domain signal) transform to the coefficient space (frequency domain signal) motivated line processing method. has a strong signal in space, reflected in the frequency domain within certain areas.the amount is often together, or the distribution of the coefficient matrix with some regularity. we can use these rules,law to reduce the number of quantization bits in the frequency domain, to achieve the purpose of compression. as the transformation matrix of orthogonal transformation is reversible .inverse matrix transpose matrix are equal, which makes the decoding operation is the solvability of operator convenience, so the operational matrix of the total is the choice of the orthogonal transform to do.commonly used transform coding k-l transform coding and dct coding. k-l transform coding in compression ratio is superior to dct coding, but the large amount of computation and there is no fast algorithm for dct coding is widely used in practical application.7, the model law coding predictive coding, vector quantization coding and transform coding is a waveform coding, its theoretical foundation is a signal processor.theory and information theory; starting point is the image signal as irregular statistical signal from the correlation between pixels.this image signal statistical model starting the design of the encoder. model coding is the use of computer vision and computer graphics analysis and synthesis of knowledge on the image signal.model coding the image signal as the target and scene projection in the 3d world to the product of the two-dimensional plane, while evaluation of the product is determined by the characteristics of the human visual system. model encoded key is a particular graph.like model, and according to this model to determine the characteristic parameters of the image of the scene, such as motion parameters, shape parameters.and so on. decoding according to the parameters and known model synthesis image reconstruction of images. encoded object is a specialsign parameters, instead of the original image, it is possible to achieve relatively large compression ratio. the error introduced by the model coding is less sensitive to the human visual geometric distortion, the reconstructed image is very natural and realistic. in addition, in recent years, fractal coding coding and wavelet transform techniques and an increasing number of applications in image compression.reduction of the field, but most are still in the research stage, still in front of the common image compression method described in the main. of course, in actual applications, a variety of image compression methods are often combined to use, such as jpeg.數(shù)字圖像壓縮技術(shù)介紹 數(shù)字圖像壓縮又稱為圖像壓縮或圖像編碼，分為靜止圖像壓縮和運(yùn)動(dòng)圖像壓 縮（視頻壓縮）。由于圖像數(shù)據(jù)中存在著高度的相關(guān)性，一幅圖像內(nèi)部及視頻圖 像之間存在大量的冗余信息。這些冗余信息主要包括以下五種：（1）時(shí)間冗余：圖像序列的相鄰幀之間差別很小，這種與時(shí)間相關(guān)的冗余稱為時(shí)間冗余。（2）空間冗余：一幅圖像內(nèi)部存在均勻著色的部分，或者圖像內(nèi)部存在規(guī)則的模式，這種與空間相關(guān)的冗余稱為空間冗余。（3）結(jié)構(gòu)冗余：在圖像的部分區(qū)域內(nèi)存在著較強(qiáng)的紋理結(jié)構(gòu)，或者圖像的各部分之間存在著某種關(guān)系，如自相似性。這種冗余稱為結(jié)構(gòu)冗余。（4）知識(shí)冗余：圖像中包含的信息與某些先驗(yàn)的基礎(chǔ)知識(shí)有關(guān)，如在一般的人臉圖像中，頭、眼、鼻和嘴的相互位置等信息就是一些常識(shí)。這種冗余稱為知識(shí)冗余。（5）視覺(jué)冗余：在多數(shù)情況下，重建圖像的最終接受者是人的眼睛。為了達(dá)到較高的壓縮比，可以利用人類視覺(jué)系統(tǒng)的特點(diǎn)。比如人眼對(duì)不同顏色的分辨能力不同，對(duì)不同方向的敏感度也不同等等。因此，如果編碼方案利用人類視覺(jué)系統(tǒng)的一些特點(diǎn)，可以進(jìn)一步提高壓縮比和圖像的所謂主觀質(zhì)量。圖像編碼就是要盡可能的去除上述各種形式的冗余信息，以降低表示圖像所需的比特?cái)?shù)。常用的圖像的壓縮方法有以下幾種： 1、行程長(zhǎng)度編碼（rle） 行程長(zhǎng)度編碼（run-length encoding）是壓縮一個(gè)文件最簡(jiǎn)單的方法之一。它的做法就是把一系列的重復(fù)值（例如圖象像素的灰度值）用一個(gè)單獨(dú)的值再加上一個(gè)計(jì)數(shù)值來(lái)取代。比如有這樣一個(gè)字母序列aabbbccccccccdddddd它的行程長(zhǎng)度編碼就是2a3b8c6d。這種方法實(shí)現(xiàn)起來(lái)很容易，而且對(duì)于具有長(zhǎng)重復(fù)值的串的壓縮編碼很有效。例如對(duì)于有大面積的連續(xù)陰影或者顏色相同的圖象，使用這種方法壓縮效果很好。很多位圖文件格式都用行程長(zhǎng)度編碼，例如tiff，pcx，gem等。 2、lzw編碼 這是三個(gè)發(fā)明人名字的縮寫（lempel，ziv，welch），其原理是將每一個(gè)字節(jié)的值都要與下一個(gè)字節(jié)的值配成一個(gè)字符對(duì)，并為每個(gè)字符對(duì)設(shè)定一個(gè)代碼。當(dāng)同樣的一個(gè)字符對(duì)再度出現(xiàn)時(shí)，就用代號(hào)代替這一字符對(duì)，然后再以這個(gè)代號(hào)與下個(gè)字符配對(duì)。 lzw編碼原理的一個(gè)重要特征是，代碼不僅僅能取代一串同值的數(shù)據(jù)，也能夠代替一串不同值的數(shù)據(jù)。在圖像數(shù)據(jù)中若有某些不同值的數(shù)據(jù)經(jīng)常重復(fù)出現(xiàn)，也能找到一個(gè)代號(hào)來(lái)取代這些數(shù)據(jù)串。在此方面，lzw壓縮原理是優(yōu)于rle的。 3、霍夫曼編碼 霍夫曼編碼（huffman encoding）是通過(guò)用不固定長(zhǎng)度的編碼代替原始數(shù)據(jù)來(lái)實(shí)現(xiàn)的?；舴蚵幋a最初是為了對(duì)文本文件進(jìn)行壓縮而建立的，迄今已經(jīng)有很多變體。它的基本思路是出現(xiàn)頻率越高的值，其對(duì)應(yīng)的編碼長(zhǎng)度越短，反之出現(xiàn)頻率越低的值，其對(duì)應(yīng)的編碼長(zhǎng)度越長(zhǎng)。 霍夫曼編碼很少能達(dá)到81的壓縮比，此外它還有以下兩個(gè)不足：它必須精確地統(tǒng)計(jì)出原始文件中每個(gè)值的出現(xiàn)頻率，如果沒(méi)有這個(gè)精確統(tǒng)計(jì)，壓縮的效果就會(huì)大打折扣，甚至根本達(dá)不到壓縮的效果。霍夫曼編碼通常要經(jīng)過(guò)兩遍操作，第一遍進(jìn)行統(tǒng)計(jì)，第二遍產(chǎn)生編碼，所以編碼的過(guò)程是比較慢的。另外由于各種長(zhǎng)度的編碼的譯碼過(guò)程也是比較復(fù)雜的，因此解壓縮的過(guò)程也比較慢。 它對(duì)于位的增刪比較敏感。由于霍夫曼編碼的所有位都是合在一起的而不考慮字節(jié)分位，因此增加一位或者減少一位都會(huì)使譯碼結(jié)果面目全非。 4、預(yù)測(cè)及內(nèi)插編碼 一般在圖象中局部區(qū)域的象素是高度相關(guān)的，因此可以用先前的象素的有關(guān)灰度知識(shí)來(lái)對(duì)當(dāng)前象素的灰度進(jìn)行預(yù)計(jì)，這就是預(yù)測(cè)。而所謂內(nèi)插就是根據(jù)先前的和后來(lái)的象素的灰度知識(shí)來(lái)推斷當(dāng)前象素的灰度情況。如果預(yù)測(cè)和內(nèi)插是正確的，則不必對(duì)每一個(gè)象素的灰度都進(jìn)行壓縮，而是把預(yù)測(cè)值與實(shí)際象素值之間的差值經(jīng)過(guò)熵編碼后發(fā)送到接收端。在接收端通過(guò)預(yù)測(cè)值加差值信號(hào)來(lái)重建原象素。 預(yù)測(cè)編碼可以獲得比較高的編