Research Of The Image Compression Technology In The Electronic Seeing And Hearing Board

In recent years, with the constant development of microelectric technique,technology of the computer, modern communication technology and network technology,the human society is marching toward information age in vigorous strides; thismakes social treatment to information of the picture, transmission, spread andneed that exchanges more and more urgent. And modern electronic device andequipment of society deal with ability and processing speeds raise by a wide margin,digital picture compress technology progress constantly , video with unique ocular ,amount of information characteristic such as being heavy their image, more andmore product application, all these make the digital picture become the mainstreamthat information is being expressed gradually. In addition, people have more andmore high expectations for resolution ratio of the image , this makes the dataamount of the image bigger and bigger, and when the data amount of the image willinfluence the product design , choose such problems as the size of space of thememory and processing speeds of the processor ,etc., at the same time thesequestions are in direct radio with the cost directly, at this moment, the imagecompression became very important and very essential technology in the products.The electronic seeing and hearing board is a very practical electronic product,can be used in multimedia teaching or various kinds of academic meetings.Electronic seeing and hearing image of board thesis study compress problem is wholeelectronic seeing and hearing key point, board of design. Because the occasionthat the electronic seeing and hearing board uses demands to transmit in real time,its compression has one's own characteristics: First, on terms that ask highcompression ratio most, want the possible reduction to compress the complexityof the method , transmission when the ones that can just realize the image onlyin this way are real. Second, the electronic seeing and hearing board is transmittedthrough the wireless, the situation unstable in bandwidth may exist , under suchan environment, the most ideal state can realize the compression ratio carrieson the change according to the size of the bandwidth, can adapt to the size ofthe bandwidth in case of any transmission. Then can compress harmlessly ifbandwidth permitting, when bandwidth is not enough, improve compression ratio soas to adapt to bandwidth, under the essential situation, can proper to die thequality of image.The self-adaptation based on context predicts entropy code CALIC (Context-basedLossless Image Compression) is a compression algorithm of a kind of compressionwith very outstanding performance, often quoted by many kinds of documents asconsulting the method. CALIC algorithm, which is a categorized thought of thecontext, has succeeded in using a kind of outstanding compression thought, andit makes the image not only having a large compression ratio but also having ahigh-quality image picture. It and before use the categorized method differenceof the context to lie in: First, the categorized method of context with one's owncharacteristics. Second, with method that means value compensate, may acceleratespeed that models disappear so after not classifying. While compressingcharacteristics, the adaptive entropy code classified on the basis of the contexthas a deadly weakness too: its computing method is mixed and stayed high. Meanwhile,this compress algorithm is not have the level scalability, that is to say it doesnot compress so variable, and it can't meet the bandwidth of the change at anytime.Because of the shortcoming, the thesis has proposed a kind of new compressionalgorithm: Level Embedded Lossless Image Compression (LELIC). This method isinspired in CALIC, and it is a kind of algorithm of compression based on contextmodel too. Level (bit-plane) scalability is achieved by separating the image intotwo layers (the base layer and the residual layer) before compression. Excellentcompression performance is obtained by exploiting both spatial and inter-levelcorrelations. A comparison of the proposed scheme with a number of scalable andnon-scalable lossless image compression algorithms indicates that thelevel-embedded compression incurs only a small penalty in compression efficiencyover non-scalable lossless compression, while offering the significant benefitof level-scalability. After using this algorithm to compress the picture , if needto cast out the low embedded level, so long as yard corresponding low-order parttake out in bit stream. Obviously, the memory of this method , with calculating...