本論文提出一個利用雙線性內插法與離散餘弦轉換之適應性組合以達到基於像素尺寸之二維影像快速及超大尺度放大/縮小，並且增進放大/縮小的速度與品質。利用雙線性內插法與離散餘弦轉換針對各區塊不同的特性，適應性地進行超大尺度的放大/縮小。與雙線性內插法作比較，離散餘弦轉換在邊界區塊上產生較佳品質的高階近似方法，但執行時間較長。然而，雙線性內插法是個在平滑區塊產生不錯品質的低階進似方法，並且只需較短時間執行。此外，如果品質為主要考量，進化規劃演算法被利用來調整縮小影像之頻率係數矩陣中的元素以進一步提升縮小的品質。最後，本論文也提出一個新的演算法以處理任意像素尺寸之放大/縮小。

In this thesis, a fast pixel-size-based very large scale enlargement and reduction via an adaptive combination of bilinear interpolation and DCT of image is proposed to improve the quality and speed of the image zooming process. The proposed algorithm combined with discrete cosine transform (DCT) and bilinear interpolation is proposed to adaptive process different blocks of an image with different characteristics for a very large scale image enlargement and reduction. Compared with bilinear interpolation, DCT is a high-order approximation method that has high quality interpolation for sharp blocks but costs much operating time. Nevertheless, bilinear interpolation is a low-order approximation method with good quality for smooth blocks and costs few operating time. Moreover, if quality is the main concern factor, the evolutionary programming (EP) is applied to further improve the quality of reduction algorithm of an image by tuning the frequency coefficient matrix of the reduced image. Finally, for dealing with arbitrary pixel-size-based zooming of an image, a new algorithm is also proposed in this thesis.

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