| Summary: | 碩士 === 國立屏東科技大學 === 資訊管理系所 === 97 === The contents of abstract in this thesis
The popularity of multimedia on the Internet has created a heavy load on storage capacity and network bandwidth. Consequently, digital content compression techniques have become significant and popular topics recently. Vector Quantization (VQ) is the most popular method in lossy image compression. An appropriate codebook design is an essential and helpful principle for Vector Quantization such as LBG (Linde, Buzo and Gray), SOM (Self-Organizing Map) and HSOM (Hierarchical SOM). The features of LBG are fast and simple, but the compression quality of LBG is not so stable. Even though SOM and HSOM yield the satisfied results, they are too time-consuming. This thesis develops a new image compression method named ELSA, which employs an expanding leaf to determine the rough vectors (codewords) quickly and utilizes an LBG for quality improvement in the end. Experimental results reveal that ELSA outperforms LBG, SOM, HSOM and INTSOM in terms of computational cost and quality.
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