電機資訊學院: 電信工程學研究所指導教授: 丁建均陳立昂Chen, Li-AngLi-AngChen2017-03-062018-07-052017-03-062018-07-052016http://ntur.lib.ntu.edu.tw//handle/246246/276427　　由於目前盛行的影像壓縮標準大多為基於方型區塊分割處理的架構，此種架構不僅不會針對影像內容做適當調整，在高壓縮率的情況也容易出現影像塊狀化的問題。有鑑於此，我們認為先將影像作適當的分區切割再進行壓縮處理將有利於壓縮效率及視覺評估的改善。在這篇論文中，我們根據影像形狀自適應壓縮系統的架構，將其分為前、中、後三部分，並且在各個部分皆提出對應的演算法。 　　第一部分為處理並記錄影像切割完後的資訊。我們提出的有損輪廓壓縮演算法採用了離散餘弦轉換的基底來對輪廓曲線進行近似，另外使用加權曲率、疊代的架構來幫助我們將一個完整輪廓分為不同的子線段以利近似。與其他有損輪廓壓縮演算法比較的實驗結果顯示，我們提出的方法在同樣的壓縮率下能夠有更小的誤差，針對愈複雜的輪廓尤甚。 　　第二部分是針對自然影像本身進行形狀自適應壓縮。我們的方法首先採用改良式的影像預測技術，即使在高壓縮率的情況下仍能保持影像整體的結構輪廓。再者，我們使用形狀自適應的離散餘弦轉換以及適應性變動長度編碼來儲存殘留影像。為了減少影像塊狀化的影響，在重建壓縮影像時也額外加入改良式去塊狀濾波器。在客觀評估指標中，所提出的影像壓縮方法與其他的方型區塊壓縮方法相比皆有較好的分數，並且與以小波轉換為基底的JPEG2000表現相當。在主觀視覺比較中，我們提出的壓縮方法有較好的視覺表現。此外，在高壓縮率伴隨的影像損失中，提出的影像壓縮方法僅是細節流失而已，並無多餘的塊狀或鬼影問題。 　　為了能夠給出更貼近人眼感知的影像品質評估，第三部分提出了一個新的評估演算法。此演算法採用了影像梯度及亮度的相似度來評估局部影像品質，並且使用以梯度為導向的加權策略。實驗結果顯示此影像品質評估方法能夠有效的給予第二部分中形狀自適應壓縮影像較適當的分數，並且針對不同種類的影像失真也有不錯的評估表現。As the existing image compression standards are mostly block-based and do not take the image contents into consideration, the compressed images appear to have blocking and ringing artifacts at high compression ratios. Therefore, in order to utilize image characteristics and achieve better visual qualities, we suggest to segment the image into several self-correlated regions in the first place and encode these regions separately. In this thesis, we first propose an efficient algorithm to encode the contours of regions from the image segmentation. The key feature of the proposed algorithm is using the DCT bases to approximate curve segments. In addition, we employ the weighted curvature measurement and the iterative split and merge calculation structure to divide the whole contour into suitable segments to be estimated piece-wisely. Comparisons with other existing contour approximation methods show the proposed method outperform other algorithms in either approximation error or compressed bits. In the second part of the thesis, we describe the proposed shape-adaptive image compression algorithm. Following the contour compression from part one, this method further employs the shape-adaptive versions of the image prediction, orthogonal transform, and image deblocking techniques. The resultant objective compression performance of our method is better than other block-based approaches and comparative to the JPEG2000. For the subjective comparisons, the proposed compression method has the best visual quality and more reasonable degradation at low bitrate. Moreover, to model the visual quality more accurately, we further propose an image quality assessment (IQA) method in the third part of this thesis. The proposed IQA method employs adaptive gradient and luminance similarity along with gradient-oriented pooling strategy. Comparing the shape-adaptively compressed images with the proposed IQA method, we find the results accordant to the subjective visual qualities. In addition, the performance on public image database also suggests the proposed method can be an alternate competitive IQA approach.7392197 bytesapplication/pdf論文公開時間: 2016/7/25論文使用權限: 同意有償授權(權利金給回饋本人)輪廓近似影像壓縮形狀自適應壓縮影像預測影像去塊影像品質評估結構相似度contour approximationimage compressionshape-adaptive compressionimage predictionimage deblockingimage quality assessmentstructural similaritythesis10.6342/NTU201600821http://ntur.lib.ntu.edu.tw/bitstream/246246/276427/1/ntu-105-R03942036-1.pdf