標題: 用於影像壓縮之混合式向量量化演算法Hybrid Vector Quantization for image compression 作者: 呂忠晏Chung-Yen Lu溫壞岸Kuei-Ann Wen電子研究所 關鍵字: 向量量化; 區塊截除編碼; 離散餘弦轉換; 分類式向量量化;Vector quantization;Block truncation;Discrete cosine Classified vector 公開日期: 1992 摘要: 向量量化是一種關於低位元比率影像編的有效技術。然而，向量量化影像 編碼的初期研究揭示了一些難處，主要是在影像邊緣部分和高計算複雜性 o 雖然已經有些方法針對影像邊緣編碼，如區塊截斷編碼與向量量化或分 類式向量量化，但是其壓縮比還不能令人滿意。在另一方面，離散餘弦轉 換與向量量化應用於影像壓縮，是可以得到高壓縮比，但是其影像邊緣的 效果較差。我們提出一種影像壓縮演算法，利用DCT/VQ及BTC/VQ以達到高 畫質及高壓縮比。實行此演算法可得到壓縮率0.34..0.46(位元/影像點)o Vector quantization (VQ) is a powerful technique for low bit rate image coding. However, initial studies of image coding with VQ have revealed difficulities, most notably edge degradation and high computational complexity. Although there are many algorithms developed to keep the image from edge degradation, such as block truncation coding with VQ (BTC/VQ) or classified vector quantization, the compression ratio could not be satisfactory. On the other hand, discrete cosine transform with VQ (DCT/VQ) has beeb applied to image compression, it gains high compression ratio but the edge degradation problem still exists. We present an image compression algorithm that takes the advantage of the DCT/VQ and BTC/VQ to achieve a high quality and low-bit rate compression of images. High quality image can be achieved at rates 0.34 .. 0.46 (bits/pixel). URI: http://140.113.39.130/cdrfb3/record/nctu/#NT810430022http://hdl.handle.net/11536/56880 Appears in Collections: Thesis