在匹配式向量量化壓縮上的研究已經發展到參考三邊或甚至四邊匹配來找到更好的選擇。但是在有線經過的區塊仍然是找不到好的選擇或需要浪費較多位元來使解碼器選出區塊。在向量量化壓縮影像上一般都把圖片分成4x4的區塊，使得影像上的連續的線會經過好幾個區塊，而造成這些區塊在匹配式向量量化壓縮上與鄰近的區塊有很高的相關性，所以我們利用這些相似的特性可以由附近區塊猜出即將要編碼的有邊經過的區塊。本篇論文提出進階預測方法：在有限機向量量化壓縮上的改進、最小失真度預測法，並加以討論其結果所代表的意義。
另外，由於匹配式向量量化壓縮受限於區塊邊緣的連續性或相關性，如果區塊間沒有很強的連續性或是有很明顯的邊界存在此區塊中，便會選擇不出好的有限機向量量化壓縮(FSVQ)組合，所以我們將這些猜得不好的區塊另外獨立出來使用原始的向量量化壓縮法來求得其索引值，並使用四元樹編碼法或算術編碼法使得解碼器知道這些特殊的區塊位置，而這些猜的不好的區塊中有變異數很大的會對影像品質有很大的影響，由此我們可以從這些使用有限機向量量化壓縮失敗的區塊中，找尋變異數較大的，這樣可以浪費一些位元換得更高的影像品質。

最後針對有限機向量量化壓縮上的改進預測方法與回歸原始向量量化壓縮編碼法，全面性的改進，並與其他小波轉換的方法比較。

The research on Side-Match Vector Quantization is involved to adapt three sides or even four sides to obtain better case on edge block prediction. The most major improvement is the recent study on TSMVQ. However its finite state coding strategy like side match using equal weight of surrounding block sides. When a clear line goes through the blocks, adapting which two sides could make the side-match state codebook select total different state codebook. This kind of characteristics makes the neighboring blocks which goes through by a line has high correlation. We use such trait to propose an advanced prediction which can use these correlation to predict such edge blocks in less bits.
We also study the limitation of the TSMVQ, which leads us to find out there is certainly blocks is hard to predict by Three-sided side match method if the correlation in between the side is not strong. Hence selecting certain number of blocks and sending their position by quadtree coding and their Vector Quantization indices to obtain better image quality by wasting some bits as tradeof

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