電子信息工程外文翻譯--使用自適應預測和自適應算術(shù)編碼的有損圖像的無損壓縮（英文）

1、Lossless Image Compression with Lossy Image Using Adaptive Prediction and Arithmetic Coding Seishi Taka“ and Mikio Takagi Institute of Industrial Science, University of Tokyo Abstract Lossless gray scale image compress

2、ion is necessary in many purposes, such as medical image, image database and so on. Lossy image is important as well, because of its high compression ratio. In this paper, we propose a lossless image compression sc

3、heme using a lossy image generated with PEG-DCT scheme. Our concept is, send a PEG-compressed lossy image primaly, then send residual information and reconstruct the original image using both the lossy image an

4、d residual information. 3-dimensional adaptive prediction and an adaptive arithmetic coding are used, which fully uses the statistical parameter of distribution of symbol source. The optimal number of neighbor pi

5、xels and lossy pixels used for prediction is discussed. The compression ratio is better than previous work and quite close to the originally lossless algorithm. 1 Introduction Today there are many studies on image co

6、mpression, particularly on lossy and very low bit rate compression. For image database, such high compression ratio is important for storage and also for quick transmission, but to deal with various kinds of users d

7、emand, lossless image transmission is indispensable. In this paper, we propose an effective lossless compression algorithm for gray image using lossy compressed image. The lossy compression scheme uses the Joint Pho

8、tographic Experts Group discrete cosine transform (PEG-DCT) algorithm as the lossy coding algorithm. First we search the similar pairs of pixels (conlexts), according to their neighbor pixels. For such pixels which

9、have contexts, we predict their values from the contexts and the neighbors. On the other hand, for each pixel which doesn't have its context pairs, we calculate the edge level according to the difference of adjace

10、nt pixel values. For each edge level of pixels, we calculate the predictive coefficients of linear combi- nation under the least square error criterion. Not only the pixels which have already processed but also the

11、 pixels of the lossy image is used for prediction. For every pixel, the difference between predicted value and real value is calculated, and the difference is convertedto anon-negative value before being encode

12、d, according to their distriiution. In entropy coding stage, we use the arithmetic coding. It is made adaptive, and initial error distribution is given only by one parameter, which is specific for each edge level

13、's statistical distribution. The pixels belonging to the different edge levels are encoded independently. 166 1068-0314/94 $ 3 . 0 00 1994 IEEE 168 (a) (b) Figure 2: (a)Original image ‘Girl’ and (b)JPEG comp