具物件感知之即時非線性縮放;Design of Real-Time Object-Aware Non-Linear Scaling

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Please use this identifier to cite or link to this item: http://ccur.lib.ccu.edu.tw/handle/A095B0000Q/339

Title:	具物件感知之即時非線性縮放;Design of Real-Time Object-Aware Non-Linear Scaling
Authors:	何宗興;He, Zong-Xing
Contributors:	電機工程研究所
Keywords:	影像縮放;影像重置;物件感知;顯著物;影像評估;Scaling;Retargeting;Object-Aware
Date:	2016
Issue Date:	2019-07-17
Publisher:	電機工程研究所
Abstract:	由於面板技術的進步，使得顯示器的面板大小與形狀更顯多元，小至行動裝置大至電視等，進而使得影像縮放的技術備受重視。由於影像來源尺寸各式各樣，在輸出至終端機顯示時，必需縮放影像的尺寸大小來符合顯示器輸出規格，以往傳統的方式是用等比例的縮放，像是縮放(scaling)或是剪裁(cropping)，但是單純的縮放(scaling)容易造成畫面的失真或是變形，而剪裁(cropping)會造成黑邊或是資料流失的問題。因此近年來，興起基於影像的內容去做縮放，基於目的不同處理的方式也有很多種，而我們的訴求在於影像中的重要物件不會因為解析度的轉換而有長寬比例上的變形，並且減少整體畫面失真。本論文所提之方法，以硬體達成所謂之目標，將演算法硬體化，使我們的能即時處理，也因此我們必需思考適合硬體實現的演算法，並且在不使用frame buffer條件下做處理，來達到降低硬體的成本與面積。相較於過往找尋重要物件的方法，我們減少找尋重要物體的計算量，經由邊緣偵測找尋可能之重要物件，且為了減少運算量，我們把圖片從像素層級轉成區域層級，並使用相連元件標籤，找出我們所包含重要物體的重要區域，來適合硬體化的實現。處理的方面我們盡可能地在保持整張圖的完整性條件下，將重要物體的長寬做等比例縮放，且對其餘的非重要區域使用不同的縮放比例來配合重要區域，來達到目的之影像大小。 With the progress of panel technology, there are diversities in shape and size of monitor panels ranging from small as mobile device to large as television. Furthermore, the image scaling technology also draws growing attention because of this progress. On account of the diversity of image sources, scaling image to fit the monitor output specifications is necessary. Traditional method is equal ratio scaling, including scaling and cropping. However, overall scaling causes image distortion or deformation; cropping results in problems of black border or data loss. Therefore, in recent years, a new upsurge of information-based scaling is growing; changeable methods result from different goals. The main purpose is that the vital objects in the image won’t be ratio distorted because of the change of resolution. The approaches mentioned in this paper will focus specifically on hardware-based algorithm, which can timely dispose of In order to cost down and downsize the hardware, we have to come up with an algorithm which can be run by the hardware without using frame buffer. Compare to the traditional process of searching the main objects in the image, we pay attention to hardware and spend less computation cost by using edge detection to target the main objects; converting images from pixel level to regional level; using connected component labels to mark out the important regions of the images. Whiling reading the images, we try to keep the integrity. By ratio-scaling the main objects and using different ratio on the rest of the trivial regions, we can bring out the theme of the images to attend our goal.
Appears in Collections:	[電機工程研究所] 學位論文

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