專業(yè)英語-imageandtelev(編輯修改稿)
2025-02-02 14:36 本頁面
【文章內容簡介】 s. 損失性圖像壓縮則允許某些圖像信息丟失，但在壓縮比上有著更大的潛力。 No matter what technique that you use, the exact rate is very dependent on the plexity of the image that you are analyzing. 無論你采用什么技術，準確的壓縮率室依靠于你所分析的圖像的復雜程度的。 For example, the normal best that can be achieved with lossless encoding in a rate of 2 bits per pixel. 例如，通常非損失性編碼能產生最好的效果是各像素 2位的壓縮比。 In fact, for some landsat scenes with urban areas and many small farms, the factor of 2 bits per pixel may not be able to achieved. 實際上，對于很多農場的鄉(xiāng)村衛(wèi)星圖像，用各像素 2位的系數就不能夠實現。 The same technique applied to a landsat image of the midwest where large fields occur and few shadows exist in images might produce a much better pression. 但對于大片開闊地和少數陰影的中西部地區(qū)的衛(wèi)星圖像，同樣的技術能產生較好的壓縮。 One lossless technique is known as run length encoding. 有一種非損失性壓縮技術稱為行程編碼。 The pression algorithm processes each line of input imagery looking for regions in which data values are the same. 該壓縮算法對每一行輸入圖像進行處理以找出數據值一致的范圍。 If ten pixels in the original image have a value of 10, then the same data may be represented as a data value, 10, and a multiplier saying how many times the value is repeated before a changed value. 如果原始圖像中的 10個像素值為 10，那么相同的數據可被表示成同一數值： 10，并且一個乘法器將指出該值在改變之前重復了多少次。 Huffman encoding follows a similar process. 哈夫曼編碼就使用了相似的方法。 These lossless techniques are generally called entropy coding techniques, and have application in document imaging, desktop publishing, and GIS. 這些非損失性技術通常稱為熵編碼技術并已經在檔案圖像、桌面出版和地理信息系統(tǒng)上得到應用。 It should be noted that entropy coding does not work exceptionally well in the representation of remote sensing images. 應該注意，熵編碼在遙感圖像的表示中并不是特別令人滿意。 In remote sensing imagery it is well known that there may be significant correlation between different bands of multispectral data. 眾所周知，在遙感圖像中，在不同頻譜的多譜數據之間可能存在顯著的相關性。 In image processing, a procedure called principal ponents has been designed to identify correlation between image bands and to create a new set of transformed bands that represent a new color space in which the new image bands are uncorrelated. 在圖像處理過程中，一個稱為主分量的過程被設計用以確認圖像頻譜之間的相關性，并產生一組新的變換頻譜 ,新圖像頻譜互不相關地表示一個新的彩色空間 . The proce
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