【正文】 Bit is 1, it bees 0. Difference between the five should retain the median 3, the following table that lists the difference between the Bit to be retained and the difference between the number of content controls. In the margin of the margin frontend add some additional value Hoffman code, such as the brightness difference of 5 (101) of the median of three, then the Huffman code value should be 100, the two connected together shall be 100101. The following two tables are the brightness and chroma DC difference en。s plement binary number can be. The socalled one39。s sharpness. Finally we will see later on that there is another way to make edge detection with matrix convolution.② Color extractionThe other immediate application of pixel parison is color of paring each pixel with its neighbors, we are going to pare it with a given color C1. This algorithm will try to detect all the objects in the image that are colored with C1. This was quite useful for robotics for example. It enables you to search on streaming images for a particular color. You can then make you robot go get a red ball for example. We will call the reference color, the one we are looking for in the image C0 = (R0,G0,B0). Once again, even if the square root can be easily removed it doesn39。t see the edges of those stripes if it only pared a pixel to its right neighbor. Thus the two parisons for each pixel are necessary.This algorithm was tested on several source images of different types and it gives fairly good results. It is mainly limited in speed because of frequent memory access. The two square roots can be removed easily by squaring the parison。 between two colors by puting the geometric distance between the vectors representing those two colors. Lets consider two colors C1 = (R1,G1,B1) and C2 = (R2,B2,G2), the distance between the two colors is given by the formula :D(C1, C 2) ==(R1+R2)2 +(G1+ G2)2+(B1+B2)2 This leads us to our first filter: edge detection. The aim of edge detection is to determine the edge of shapes in a picture and to be able to draw a result bitmap where edges are in white on black background (for example). The idea is very simple。s memory unit, can be coded up to 256 values. As opposed to the audio signal which is coded in the time domain, the image signal is coded in a two dimensional spatial domain. The raw image data is much more straightforward and easy to analyze than the temporal domain data of the audio signal. This is why we will be able to do lots of stuff and filters for images without transforming the source data, while this would have been totally impossible for audio signal. This first part deals with the simple effects and filters you can pute without transforming the source data, just by analyzing the raw image signal as it is.The standard dimensions, also called resolution, for a bitmap are about 500 rows by 500 columns. This is the resolution encountered in standard analogical television and standard puter applications. You can easily calculate the memory space a bitmap of this size will require. We have 500500 pixels, each coded on three bytes, this makes 750 Ko. It might not seem enormous pared to the size of hard drives, but if you must deal with an image in real time then processing things get tougher. Indeed rendering images fluidly demands a minimum of 30 images per second, the required bandwidth of 10 Mo/sec is enormous. We will see later that the limitation of data access and transfer in RAM has a crucial importance in image processing, and sometimes it happens to be much more important than limitation of CPU puting, which may seem quite different from what one can be used to in optimization issues. Notice that, with modern pression techniques such as JPEG 2000, the total size of the image can be easily reduced by 50 times without losing a lot of quality, but this is another topic.② Vector representation of colors As we have seen, in a bitmap, colors are coded on three bytes representing their deposition on the three primary colors. It sounds obvious to a mathematician to immediately interpret colors as vectors in a threedimension space where each axis stands for one of the primary colors. Therefore we will benefit of most of the geometric mathematical concepts to deal with our colors, such as norms, scalar product, projection, rotation or distance. This will be really interesting for some kind of filters we will see soon. Figure 1 illustrates this new interpretation: Figure 1(2) Immediate application to filters① Edge DetectionFrom what we have said before we can quantify the 39。 black will be known as (R,G,B)= (0,0,0)。 we will more concentrate on the algorithms themselves, the methods. Anyway, this document should be used as a source of ideas only, and not as a source of code.2. A simple approach to image processing(1) The color data: Vector representation① Bitmaps The original and basic way of representing a digital colored image in a puter39。未來的日子里，我將更加奮發(fā)圖強(qiáng)，決不辜負(fù)這么多曾給予我真誠幫助的人們! 參考文獻(xiàn)[1]孫學(xué)巖,葉海建,韓玉坤．?dāng)?shù)字圖像壓縮原理及常用壓縮編碼方法．農(nóng)機(jī)化研究．2005．(3)，128～130[2]，馮健．圖像編碼基礎(chǔ)和小波壓縮技術(shù)．清華大學(xué)出版杜．2004．3[3][4] R2007圖像處理技術(shù)與應(yīng)用/[5][6] 彭天強(qiáng),+P變換的圖像無損壓縮．計(jì)算機(jī)工程與應(yīng)用．2005．(16)；42～44 [7] 葉輕舟，林挺釗．基于FPGA的JPEG靜態(tài)圖像壓縮實(shí)現(xiàn)．福建工程學(xué)院學(xué)報(bào)．(3)：216～219 [8]岡薩雷斯．?dāng)?shù)字圖像處理(第二版)．電子工業(yè)出版社．2004．1 [9]姚慶棟，畢厚杰，王兆華等．圖像編碼基礎(chǔ)．2006．08 [10][11]（美）[12]（第二版）.[13][14](第2版).[15]（第三版）. [16][17]姚慶棟,畢厚杰,王兆華等．圖像編碼基礎(chǔ)．2006．08[18]Tharnvichai R．Bose T．Radenkovic M．Multiplierless predictor for DPCM of images．Circuits and systems，[19]Mielikainen J. Toivanen P．Clustered DPCM for the lossless pression of hyperspectral images．Geoscience and Remote Sensing,[20]附錄1 外文原文Source: the 21st century literature the applied undergraduate electronic munication series of practical teaching plan