結合SVM與SSIM之快速碎形影像壓縮演算法

A SVM and SSIM Embedded Fast Fractal Image Compression Algorithm

施宗男(Tsung-nan Shih)

指導教授 ： 龔志賢

長榮大學/資訊暨工程學院/資訊管理學系(所)/碩士(2011年)

https://doi.org/10.6833/CJCU.2011.00232

摘要

碎形影像壓縮法是一個嶄新而又富有吸引力的靜態影像壓縮技術，兼具有解析度獨立(resolution independence)、快速解壓縮及高壓縮率等優點。本研究之主要目的，在於針對傳統碎形影像編碼中編碼時間過長的問題，提出了一種結合支撐向量機(Support Vector Machine, SVM)與結構相似(Structure Similarity, SSIM)指標的快速碎形影像壓縮編碼技術。本技術使用支撐向量機將圖像塊之內部細節資訊的分佈狀況做為分類標準對Domain Pool進行分類，同時運用SSIM指標分別從亮度、對比度和結構等三個方面測量Range Block與Domain Block之間的相似程度。透過SVM方法與SSIM指標的結合，將可加快分類速度、減少匹配運算時間；並藉由SSIM的特性，找出更快速、更高品質的碎形影像壓縮編碼演算法。

關鍵字

碎形影像壓縮 ； 影像品質評估 ； 結構相似性指標 ； 支撐向量機

並列摘要

Fractal image compression (FIC) is promising both theoretically and practically. The encoding speed of the traditional full search method is a key factor rendering the fractal image compression unsuitable for real-time application. The primary objective of this research is to investigate the comprehensive coverage of the principles and techniques of fractal image compression, and describes the implementation of a pre-processing strategy that can reduce the full searching domain blocks by training the Support Vector Machine which could recognize the self-similar pattern feature to enhance the domain block searching efficiency. In this research, the novel image quality index (Structure Similarity, SSIM) and SVM based block property classifier employed for the fractal image compression is investigated.

並列關鍵字

Fractal image compression ； Image quality assessment ； Structure similarity ； Support vector machine

參考文獻

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