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

1、附錄 附錄 B 外文參考文獻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 im

2、age compression 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 compress

3、ion Scheme using a lossy image generated with PEG-DCT scheme. Our concept is, send a PEG-compressed lossy image primary, then send residual information and reconstruct the original image using both the lossy image and r

4、esidual 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 pixels and l

5、ossy pixels used for prediction is discussed. The compression ratio is better than previous work and quite close to the originally Lossless algorithm. Introduction Today there are many studies on image compression, parti

6、cularly 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 demand, Lossless im

7、age 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 Photographic Experts G

8、roup 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 have contexts,we predic

9、t 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 adjacent pixel values. For each

10、 edge level of pixels, we calculate the predictive coefficients of linear combination under the least square error criterion. Not only the pixels which have already processed but also the pixels of the lossy image is us

11、ed for prediction. For every pixel, the difference between predicted value and real value is image compression, grouping similar pixels and encode them together causes effective result. For grouping the pixels, we use t

12、he Q value: Q= | | | | | p p p p | p p | p p | 1 5 1 4 1 3 1 2 ? ? ? ? ? ? ?(2)Using this Q value, we classify each pixel into several groups according to table 1 Table 1:Grouping table Figure 2: (a)Original image

13、‘Girl’and (b)JPEG compressed image(qua1ity value=5) Figure 3: (a)Image of Q value, (b)Image of prediction error of simple prediction Figure 3(a)shows the Q value and (b) shows the error of simple prediction.As can